200312971

Sustainable wastewater treatment : developing a methodology
and selecting promising systems
Citation for published version (APA):
Balkema, A. J. (2003). Sustainable wastewater treatment : developing a methodology and selecting promising
systems. Eindhoven: Technische Universiteit Eindhoven. https://doi.org/10.6100/IR568850
DOI:
10.6100/IR568850
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Published: 01/01/2003
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Sustainable Wastewater Treatment,
developing a methodology and selecting promising systems
CIP-DATA LIBRARY TECHNISCHE UNIVERSITEIT EINDHOVEN
van der Vleuten-Balkema, Annelies J.
Sustainable wastewater treatment, developing a methodology and selecting
promising systems / by Annelies J. van der Vleuten-Balkema. Eindhoven : Technische Univesiteit Eindhoven, 2003.
Proefschrift. - ISBN 90-386-1805-0
NUR 950: Technische wetenschappen algemeen
Trefwoorden: duurzaamheid / hergebruik / huishoudelijk afvalwater / model /
optimalisering / regenwater / waterzuivering
Subject headings: decision support systems / modelling / optimisation / sustainable
development / recycling / wastewater / water treatment
Printed on recycled paper by Eindhoven University Press.
Cover by Frank, Anya and Sacha van der Vleuten.
Thesis and software are available electronically at the internet site of the TU/e library.
Sustainable Wastewater Treatment,
developing a methodology and selecting promising systems
PROEFSCHRIFT
ter verkrijging van de graad doctor aan de
Technische Universiteit Eindhoven, op gezag van de
Rector Magnificus, prof.dr. R.A. van Santen, voor een
commissie aangewezen door het College voor
Promoties in het openbaar te verdedigen
op dinsdag 4 november 2003 om 16.00 uur
door
Annelise Juliana van der Vleuten-Balkema
geboren te Amersfoort
Dit proefschrift is goedgekeurd door de promotoren:
prof.dr.dipl-ing. H.A. Preisig
en
prof.dr.-ing. R. Otterpohl
Copromotor:
dr.ir. A.J.D. Lambert
voor FRANK
Contents
SUMMARY
THANKS TO ....
1
INTRODUCTION ................................................................
...................................................................................
................... 1
1.1
SUSTAINABLE DEVELOPMENT ........................................................................1
1.2
SUSTAINABLE TECHNOLOGY ........................................................................2
1.3
THE CHALLENGES OF THE NEW MILLENNIUM.....................................................4
1.3.1 The global water crisis ...........................................................................4
1.3.2 Decentralised systems to close cycles .....................................................6
1.3.3 Small scale systems for source control to enable reuse ............................7
1.3.4 Optimising the existing systems or introducing to new systems?................7
1.4
RESEARCH OBJECTIVE .................................................................................9
1.5
OVERVIEW OF THE RESEARCH .......................................................................9
References ............................................................................................9
2
ASSESSING THE SUSTAINABILITY
SUSTAINABILITY OF DOMESTIC
DOMESTIC WATER SYSTEMS ............ 11
2.1
EXPLORATION OF METHODOLOGIES (LITERATURE REVIEW) ..................................11
2.1.1 Exergy analysis....................................................................................11
2.1.2 Economic analysis ...............................................................................12
2.1.3 Life Cycle Assessment..........................................................................12
2.1.4 System analysis ...................................................................................14
2.2
EXPLORATION OF METHODOLOGY APPLICATION—A LITERATURE REVIEW ................16
2.2.1 System boundaries ..............................................................................16
2.2.2 Sustainability indicators........................................................................16
2.2.3 Interpretation of results ........................................................................17
2.3
METHODOLOGICAL OUTLINE FOR THIS RESEARCH ...........................................19
2.3.1 Methodology ......................................................................................19
2.3.2 Goal ..................................................................................................20
2.3.3 Scope.................................................................................................21
2.3.3.1 Chosen System boundaries...........................................................21
2.3.3.2 Selected Sustainability indicators ...................................................22
References ..........................................................................................25
3
DEVELOPING A MODELMODEL-BASED DECISION SUPPORT
SUPPORT TOOL......................
TOOL ...................... 27
3.1
DATA ..................................................................................................27
3.2
MODEL OF DOMESTIC WATER SYSTEMS .........................................................27
3.2.1 Superstructure .....................................................................................28
3.2.2 Model implementation.........................................................................30
3.3
SUSTAINABILITY INDICATORS ......................................................................31
3.4
OPTIMISATION .......................................................................................31
3.4.1 Integer optimisation.............................................................................31
3.4.2 Multi-objective optimisation..................................................................32
3.4.3 Normalization and Weighting ..............................................................32
3.4.4 Solvers used........................................................................................33
3.5
OUTPUT OF DECISION SUPPORT SYSTEM .......................................................36
References ..........................................................................................36
4
STREAMS: QUANTIFYING INPUTS & CATEGORISING
CATEGORISING OUTPUTS ................ 37
4.1
4.2
WATER SERVICES .....................................................................................37
WATER SUPPLY .......................................................................................37
4.2.1
Domestic water use: .....................................................................38
4.2.2
Economics of water supply:...........................................................39
4.2.3
Social and cultural aspects: Institutions for water supply ........................41
4.3
WASTEWATER ........................................................................................43
4.3.1
Economics of wastewater..............................................................45
4.3.2
Environmental aspects of wastewater treatment..............................45
4.3.3
Social and cultural aspects: Institutions for wastewater management46
4.4
THREATS TO WATER SERVICES .....................................................................46
4.4.1 Pathogenic organisms .........................................................................47
4.4.2 Heavy metals ......................................................................................49
4.5
RESOURCES IN WASTEWATER ......................................................................50
4.5.1 Nutrients.............................................................................................50
4.6
WATER SERVICES QUANTIFIED IN THE DECISION SUPPORT TOOL...........................56
References: .........................................................................................57
5
PROCESSING UNITS: TECHNOLOGY
TECHNOLOGY CHARACTERISATION
CHARACTERISATION ...................... 61
5.1
SELECTION AND CHARACTERISATION............................................................61
5.2
WATER SUPPLY .......................................................................................61
5.2.1 Water disinfection ...............................................................................61
5.2.2 Water conservation .............................................................................64
5.3
WATER REUSE ........................................................................................65
5.3.1 Dynamics of domestic water use ..........................................................65
5.3.2 Rainwater systems ...............................................................................68
5.3.3 Greywater systems ..............................................................................71
5.3.4 Closed water systems ..........................................................................72
5.4
SANITATION ..........................................................................................73
5.4.1 Different types of water closets .............................................................73
5.4.2 Composting toilets...............................................................................74
5.4.3 Dry toilets ...........................................................................................75
5.4.4 Urine separating toilets ........................................................................75
5.5
WASTEWATER TRANSPORT .........................................................................76
5.5.1 Conventional sewer.............................................................................76
5.5.2 Vacuum sewer ....................................................................................77
5.5.3 Truck ..................................................................................................78
5.5.4 Onsite disposal ...................................................................................78
5.6
WASTEWATER TREATMENT .........................................................................78
5.6.1 Activated sludge..................................................................................78
5.6.2 Anaerobic digestion.............................................................................81
5.6.3 Composting ........................................................................................81
5.6.4 Constructed wetlands ..........................................................................82
5.6.5 Fixed bed reactors...............................................................................84
5.6.6 Membranes ........................................................................................85
5.6.7 Rotation biological contactors ..............................................................88
5.6.8 Sedimentation .....................................................................................88
5.6.9 Septic tanks ........................................................................................89
5.6.10 Trickling filters .....................................................................................89
5.6.11 UV-disinfection....................................................................................91
5.7
TECHNOLOGY CHARACTERISATION IN THE DECISION SUPPORT TOOL ...................92
References ..........................................................................................93
6
SUSTAINABLE DOMESTIC WATER SYSTEMS ...........................................
........................................... 101
6.1
CLOSING NUTRIENT CYCLES ....................................................................101
6.1.1 Solution space ..................................................................................101
6.1.2 Selected systems ......................................................................................... 101
6.1.3 Visualising results...............................................................................1065
6.1.4 Sensitivity..........................................................................................106
6.2
CREATING LIFE SUPPORT SYSTEMS..............................................................109
6.2.1 Solution space ..................................................................................109
6.2.2 Selected systems ...............................................................................109
6.2.3 Visualising results ..............................................................................109
6.3
CONCLUSIONS ....................................................................................114
6.3.1 Sustainable systems...........................................................................114
6.3.2 Evaluating the decision support tool ...................................................114
7
CONCLUSIONS & DISCUSSION
DISCUSSION................................
..........................................................
.......................... 117
7.1
SUSTAINABLE WATER MANAGEMENT ...........................................................117
7.2
ASSESSING SUSTAINABILITY ......................................................................117
7.3
THE DECISION SUPPORT TOOL .................................................................118
7.3.1 Data.................................................................................................118
7.3.2 Indicators..........................................................................................118
7.3.3 Models .............................................................................................119
7.3.4 Optimisation .....................................................................................120
7.3.5 Using the decision support tool ..........................................................120
7.4
SUSTAINABLE DOMESTIC WATER SYSTEMS .....................................................121
7.4.1 Sustainable systems...........................................................................121
7.4.2 Trade-offs .........................................................................................121
7.4.3 Decisive variables .............................................................................122
GLOSSARY: Acronyms & symbols and Terminology
APPENDIX 1:
APPENDIX 2:
APPENDIX 3:
APPENDIX 4:
DATA IN SPREADSHEET (DATA2003)
OPTIMISATION
TECHNOLOGICAL DATA SHEETS
RESULTS: SELECTED WATER SYSTEMS
CURICULUM VITAE
SAMENVATTING
Summary
A concrete challenge for this new millennium is to meet the UN target that aims at providing
safe drinking water and hygienic sanitation to all people on earth by the year 2025. There is a
wide variety of technologies that could fulfill these services, however many of the existing
water systems do not provide an integral solution, such that we now face problems like
eutrophication, heavy metals in sludge, water shortages. Even more complex problems are
emerging, such as the loss of fertility due to traces of medicine and chemicals in the water,
and scarcity of nutrients for food production due to the disruption of the nitrogen and
phosphorus cycle. How can we try to meet those challenges? A major aspect in finding
solutions maybe the fact that problems have become so complex that there is no single
optimal solution. Furthermore, the solutions must be formulated with a long-term global view
in mind, such that these do not trigger new problems. Or in other words: we are looking for
sustainable solutions, carefully balancing the use of different resources such as
environmental, economical, and social-cultural resources in such a way that the contribution
to local and global problems is minimised or are at least known and accounted for.
To gain insight into the sustainability of wastewater treatment systems we developed a
sustainability assessment, which is a combination of existing tools, such as life cycle
assessment, cost-benefit analysis, and social inventories. The main features that make the
combination more than a sum of the parts are: the broad scope, the set of sustainability
indicators, the process design oriented approach for modelling through the use of a
superstructure, and selection of optimal structures by multi-objective integer optimisation. We
implemented this sustainability assessment in a model-based decision support tool for the
selection of sustainable domestic water systems. The three main components of this tool are:
(1) Sustainability indicators:
Based on the different dimensions of sustainability we defined a set of indicators
including economic, environmental, and social-cultural aspects. In addition we
included functional indicators that are used to account for characteristics of
technologies such as robustness, adaptability, and maintenance. The total set consists
of 27 sustainability indicators, some of these are quantified in the mass balance for
instance rainwater use, some others are derived indirectly through a categorisation of
the outgoing streams, for instance water with a quality suitable for domestic reuse. A
third type of indicators provides a qualitative measure, for instance social acceptance
and robustness. These are used to indicate a potential advantage or disadvantage of a
certain technology.
(2) Model:
The quantification of the sustainability indicators is based on a model that represents
the mass balances of the domestic water system. This model is constructed as a
superstructure by superimposing a large number of known options for the supply of
different water sources (drinkingwater, householdwater, and rainwater), in-house
water disinfection, water conservation, and wastewater treatment ranging from smallscale onsite treatment to large-scale systems that serve complete urban areas. In the
model structure, 37 simple static models of 13 different technologies are contained in
the processing units. The optimisation selects a combination of technologies resulting
in a complete model of a domestic water system.
(3) Optimisation:
We defined the selection of optimal systems as a multi-objective integer optimisation
problem. Important objectives in selecting sustainable domestic water systems are:
minimise costs, the use of resources such as water, energy and space, maximise the
production of clean water, nutrients, and biomass for reuse, minimise harmful waste
products, maximise social-cultural embedding through acceptance, participation, and
stimulation of sustainable behaviour. To combine these sometimes contradicting
objectives, the different sustainability indicators quantifying these different
objectives have to be normalised and weighted such that they can be integrated into a
single final objective for optimisation.
To select optimal domestic water systems for different cases, we experimented with
different solvers. However, we found that due to the large number of possible
combinations, about 7*1012 different water systems are contained in the decision
support tool, and the discrete changes in sustainability it is difficult to find global
optima within a reasonable calculation time. Therefore, we choose to reduce the
problem size by defining a smaller solution space in the form of two scenarios of
which the results are discussed below.
We defined two scenarios, to select solutions that could help to fulfil the water challenge of
this new millennium. These scenarios are: (1) systems for nutrient recycling, and (2) systems
for water scarce condition aiming at minimizing drinking water use and maximising water
reuse. In both scenarios we searched for affordable solutions being aware that poverty is a
major issues still in many parts of the world.
Based on the technology choices made by the decision support tool we can conclude that
technologies not commonly used today, such as urine separation, membrane bioreactors, and
rainwater systems, may become important in future domestic water systems that aim at the
reuse of nutrients and water. We did not find the more conventional solutions due to the fact
that we aim at recycling of nutrients and water while most studies aim at comparing and
optimising existing treatment systems. Furthermore, the sludge treatment included in the
decision support tool is limited. If clean sludge can be produced and/or reused in different
ways then the treatment configurations based on conventional treatment may be promising
too. Still, based on the selections made by the decision support tool and its present settings we
conclude that, if future domestic water systems aim at nutrient and water reuse, it is likely that
the conventional systems will have to be replaced by new systems that allow separation of
wastewater streams at the source, a conclusion that is very much in line with what is reported
in the recent literature. The reuse of water will probably trigger decentralised treatment of
rainwater and greywater and may introduce low-flush or even dry toilets and disinfection of
drinking water on household scale. For nutrient recycling decentralisation is not necessary
although collection and reuse of urine and compost may trigger this as well.
While affordable systems for nutrient recycling and minimising drinking water are available,
water recycling seems to be expensive due to the choice for a membrane bioreactor. Further
research is needed to find out whether the tool selects cheaper systems such as wetlands if
realistic changes are made to data on removal rates, incoming streams, and restriction.
Since no trade-off between domestic reuse and fertiliser was found, it is very well possible to
combine the goals of our two scenarios and construct domestic water systems that recycle
both nutrients and water, thereby approaching life support systems.
Sustainable wastewater treatment
Thanks to ....
Thanks to ....
This project would never have been finalised if I had not had the support of my colleagues,
many interested researchers, enthusiastic students, and dear friends and family. Therefore, I
want to thank everybody who contributed to my project. First of all my promotor prof Heinz
Preisig who despite difficulties at out faculty and his new job in Throndheim managed to stick
with me throughout the project to provide creativity and critical reflection. Furthermore, my
second promotor prof Ralf Otterpohl who is leading one of the most progressive wastewater
research groups in Europe that creates new opportunities in wastewater treatment for the
future. I am also proud to have both, prof Wim Rulkes and prof David Butler in my PhDcommission, both also lead inspiring research groups. Other people of the commission are
prof Ruud van Heijningen and Fred Lambert, I owe them thanks for commenting on my work
several times. Thanks also go out to prof Harry Lintsen who coached me through a
challenging period in my research. Furthermore, I want to thank prof van Dongen and prof
Schot in advanced for being my opponents at my PhD-defence. Stefan Weijers I want to thank
for initiating the project, and prof Paul van den Bosch for giving me the opportunity to finish
the project in his group. Thanks also to the Centre for Sustainable technology (TDO) for their
financial support to the project and all their meetings.
My colleagues in Eindhoven, all members of the System & Control group, and especially the
PhD-students Georgo, Alexander, Roel, Uwe, Patrick, Gerwald, and Mathieu, I want to thank
for the friendly atmosphere. Others that coloured my research period at TU/e were the MScstudents who all did interesting and sometimes amazing water projects, the design student
Jean Arnaud with his socks-and-dish-washing machine, and Bart Verhoeven, who’s project
made me measure our household water use real time and resulted in the implementation of our
rainwater system, Elske van Doornum, and Gilbert Tychon. I also want to thank all
supporting staff at TU/e, especially those who helped me out when having problems with my
computer, the network, viruses, etc.
Furthermore, I want to thank my Swedish colleagues Margareta Lundin and Daniel Hellström,
keep up the good work! Thanks also to the Swedish researchers whom I never met but who’s
work contributed to mine, namely those working ORWARE and those working on Tomlab. I
also owe thanks to my Dutch colleagues at Wageningen University and TUDelft, especially
Henri Spanjers for managing the COST Working group 5 in which I participated several times
and Adriaan Mels. Also very inspiring were the European Junior Scientist Workshops,
therefore thanks to all organisers and participants.
Special thanks goes out to the ones I love, my husband Frank, our daughters Sacha and Anya,
and the baby I am carrying now. Thanks for supporting me by making every day a special
day, ;-) !!!
Annelies van der Vleuten-Balkema,
Eindhoven, September 2003.
Sustainable wastewater treatment
1 Introduction
1 Introduction
Due to the complexity and the dynamic understanding of today’s problems there is a risk of
introducing new problems when implementing solutions. To ensure that solutions have a
positive overall effect, we use sustainability as point of departure in identifying solutions for
the water sector. Therefore, we start this chapter with introducing sustainability and
sustainable technology, followed by the problems in the water sector, and possible directions
for solutions, leading to the research objective.
1.1 Sustainable development
The concept of sustainable development is based on the observation that economy,
environment and well-being can no longer be separated. The definition for sustainable
development is often quoted from the World Commission on Environment and Development
(WCED 1987): ‘development that meets the needs of the present generation without
compromising the ability of future generations to meet their own needs’. The fundamental
thought behind this definition is to accept that all human individuals have equal rights,
whether living now or in future. This means treasuring human life, independent of power and
wealth. No newborn child should be doomed to a short or miserable life, merely because that
child happens to be born in a certain class, country, or of particular sex. Sustainability defined
as to future generations makes little sense if it means sustaining human deprivation. Nor
should the less privileged today be denied the attention that we are willing to bestow on future
generations. An important aspect of sustainable development is therefore equity in
distributing development opportunities within present generations and between present and
future generations.
Another definition of sustainability formulated within the framework of the World
Conservation Strategy (IUCN 1980, 1991) is ‘Improving the quality of human life while living
in the carrying capacity of supporting ecosystems’. Alike the definition of the WCED, this
sketches a concept rather than giving an unambiguous restrictive parameter that can be
applied right away. Therefore, sustainability can be interpreted differently by different people,
evoking the critique that the term sustainability could mean almost anything1 (Mitcham
1995). However, the room left for interpretation will prove to be valuable as ideas about
sustainability are destined to be discussed over time and place, since different generations will
have to deal with different problems and different cultures and local circumstances will give a
different perspective on these problems.
When looking closer at the different reflections on sustainable development, we see that
scientists categorise different aspects of sustainability in a first step to make the concept of
sustainability operational. For instance, Schumacher (1974) specified three categories of
irreplaceable capital: fossil fuels, the tolerance margins of nature, and the human substance.
Meadows (1972) defined sustainable development in terms of material well-being, social
1
In line with this criticism Tijmes (1995) argues that in traditional societies ‘desire’ was limited by institutions, such as
religion, which were responsible for sharing wealth, while sustainability coupled with development is a Western concept to
based on growth and competition unable to bring limitation of desire and leading to social and environmental destruction.
However, if social and environmental aspects are valued as a ‘quality of life’ sustainable development will be able to bring
this limitation in desire. Therefore, it is not the concept but the implementation that deserves this critic: if we rather invest
abroad than changing or unsustainable behaviour we misuse the concept to sustain our Western way of life.
1
Sustainable wastewater treatment
1 Introduction
security and ecological balance. The WCED (1987) distinguished three dimensions of the
concept of sustainable development: environment, development and security. Security can be
related to violent conflicts, but it also covers social security in terms of income distribution,
and health care. All these quotations indicate a multidimensional character of sustainable
development. The most important dimensions mentioned are:
Economic:
Economic sustainability implies paying for itself, with costs not exceeding
benefits. Mainly focussing on increasing human well-being, through optimal
allocation and distribution of scarce resources, to meet and satisfy human
needs. This approach should in principle include all resources: even social and
environmental values (e.g. in environmental economics). However, practical
analysis include often only the financial costs and benefits.
Environmental:
The long-term viability of the natural environment should be maintained to
support development by supplying resources and taking up emissions. This
should result in protection and efficient utilisation of environmental resources.
Environmental sustainability refers to the ability of the environment to sustain
the human ways of life. The latter mainly depends upon the ethical basis: to
what extent should policies be anthropocentric, and to what extent does nature
have endogenous qualities. Although public opinion goes further, public
policies mainly remain limited to so-called use-values, which can be
incorporated in economic analysis relatively easily.
Social-Cultural:
Here the objective is to secure people’s social-cultural and spiritual needs in an
equitable way, with stability in human morality, relationships, and institutions.
This dimension builds upon human relations, the need for people to interact, to
develop themselves, and to organise their society.
Similar categorisation can be found in other publications as well (Ravetz 2000, Barbier 1987
in Bergh 1994). For instance, Barbier (1987) suggested that sustainable development is an
interaction between three systems – biological, economical, and social, with the goal to
optimise across these systems taking into account the trade-offs. The difficulty to express and
weigh these trade-offs suggests that the optimisation is a political process rather than a
scientific one. This is in line with the vision of the Scientific Council for Governmental
Policies (WRR 1994). The central thought of this council is that when implementing the
concept of sustainability, one cannot ignore the uncertainties and the mutual dependencies
between the environment and the society. The forthcoming risks for the environment and for
the economy will have to be balanced. The council concludes that due to the threatening,
despite the uncertainty, sustainable development is seen as an important guideline for
governmental policies.
1.2 Sustainable technology
Implementing sustainability means seeking solutions that balances the costs with respect to
the different resources (environmental, economical, and social-cultural) in such a way that the
2
Sustainable wastewater treatment
1 Introduction
contribution to local and global problems is minimised or are at least known and accounted
for. These solutions should be based on a long and global view participating on possible
future problems. Thereby avoiding export of problems over time or space. Realising that the
solutions are embedded in a complex entirety, one should be looking for integrated solutions.
The concept of sustainability leaves room for interpretation based on present knowledge, local
circumstances and culture. This implies that a diversity of sustainable solutions must be
available, preferably flexible to adapt to changing situations.
In our case of the problems in the water sector, we are looking technological solutions. Taking
sustainability as point of departure we have to analyse the sustainability of the different
technological solutions. Note that sustainable technology is very similar to what used to be
defined as appropriate technology, namely technology that is compatible with or readily
adaptable to the natural, economic, technical, and social environment, and that offers a
possibility for further development. Sustainability adds the long term and global view. This
means that we should take into account the different dimensions of sustainability when
analysing technology. Sustainable technology can either be high-tech or low-tech as long as it
is appropriate for the particular circumstances. The sustainable technology solutions should be
both effective, providing a real solution, and efficient, providing the solution against
minimum costs.
To analyse the sustainability of the
technological solution we analyse the
interaction of the technology with its
environment, which is schematically
represented in Figure 1. The first step is to
translate the demands of the end user into
functional criteria that must be fulfilled by
the technology. This step is important as a
choice on functional level is made. For
instance, the demand for safe water supply
maybe translated into drinking water
standards. However, the actual problem
Figure 1: Technology interaction with environment.
may not be solved with drinking water
supply alone, as lack of hygienic sanitation and hygiene in food preparation are also part of
the problem. Therefore, it is important to get an overview of how the problem is embedded
and interrelated with other problems and solutions. Furthermore, it is important to know what
actors are involved. A clear problem definition and involvement of the actors should avoid
choosing the most sustainable solution from a set of inferior solutions (ineffective solutions).
In order to fulfil its function the technology uses resources out of its environment and will
affect its environment through emissions. For instance different resources that can be used
are: money from the economic environment, natural resources such as water and energy from
the physical or ecological environment, and expertise from the social-cultural environment.
Emissions that can be made affecting the environments are: decrease of economic value due
to implementation of a technology or claim on scarce resources such as labour may affect the
economic environment, environmental pollution may deteriorate the physical or ecological
3
Sustainable wastewater treatment
1 Introduction
environment, while inconvenience or participation may negatively or positively affect the
social-cultural environment2.
Sustainable technology is technology that does not threat the quantity and quality (including
diversity) of the resources. The most sustainable solutions are those that have the lowest costs
with respect to the different environments. As the quantity and quality of the resources and
the resiliency of the environment to emissions differ over time and space so will the most
sustainable technological solution. The balancing of the different costs, economic,
environmental, and social cultural, depends on preferences of the actors.
1.3 The challenges of the new millennium
1.3.1
The global water crisis
The UN defined a major challenge for this millennium, namely to provide safe drinking water
and hygienic sanitation to all people on earth by the year 2025. In spite of the efforts during
the UNESCO’s International Water and Sanitation Decades in 1981-1990 and 1991-2000,
about 18 % of the world’s population still has no access to safe drinking water and about 40%
lacks hygienic sanitation (Mara and Feachem 2001). In many developing countries, rivers
downstream of large cities are little cleaner than open sewers. For instance, the faecal
coliform count in Asia’s rivers is 50 times higher than the WHO guidelines (UNEP 1999).
Worldwide, polluted water is estimated to affect health of 1200 million people and to
contribute to the death of about 15 million young children every year (UNEP 1999). That is
comparable with about 70 to 100 jumbo jets crashing with total loss of passengers – every day
of the year (IRC 1997). At the same time, we should realise that many water systems are
unsustainable and give rise to other problems such as eutrophication and heavy metals in
sludge. In addition some new water problems are emerging; the loss of fertility due to traces
of medicine and chemicals in drinking water and the scarcity of nutrients for food production
due to disruption of the nutrient cycles
In their “Global Environmental Outlook”, UNEP states that the declining state of the world’s
freshwater resources, in terms of quantity and quality, may prove to be the dominant issue on
the environment and development agenda of the coming century (UNEP 1999). Also the
World Health Organisation predicts straining conditions for the near future: two out of every
three people on Earth will live in water-stressed3 conditions by the year 2025, if present
consumption patterns continue (UNEP 1999). Similar, the World Water Vision (Cosgroce
2000) stresses that the today’s widespread water crisis will widen and deepen in the coming
decades (see Box 1).
2
The 3 dimensions of sustainability we defined are not really distinct in the sense that for instance environmental pollution
also affects the economic and social cultural environment. When turning it around we could express everything in Euros and
making the physical or ecological and the social-cultural environment a part of the economic environment. However, using
the definition of the 3 dimensions has the advantage that the trade off between the very different aspects of sustainability is
expressed clearly.
3
Water stress is defined as the water consumption as percentage of the renewable freshwater supply, if between 10 and 20%
water stress is said to be moderate, medium high if between 20 and 40%, and high if >40%.
4
Sustainable wastewater treatment
1 Introduction
Box 1: Main points of the world water crisis as defined in the World Water Vision (Cosgroce 2000).
• An unacceptable large part of the world population – 1 in 5 – has no access to safe drinking water,
and half of the world population has no access to hygienic sanitation. Each year 3 to 4 million
people die of water related diseases, most of them are young children dying of diarrhoea.
• Much economic progress has come at cost of severe impacts on natural ecosystems. Half of the
world’s wetlands were destroyed in the 20th century, causing major loss of biodiversity. Many rivers
and streams running through urban areas are dead or dying. Major rivers – from the Yellow River
in China to the Colorado in North America – are drying up, barely reaching the sea.
• Water services are often heavily subsidised, leading to unrealistic low prices. This undermines the
incentives for water conservation and reuse. As a result water resources are often managed
unsustainable leading to exhausting groundwater resources and pollution of all water resources.
• In most countries water continues to be managed ineffectively by highly fragmented institutions.
The strange thing is that worldwide water is abundant. True, most water available on Earth is
saline (97%), and most (75%) of the freshwater is stored in ice and snow, but still there is an
estimated freshwater stock of about 10*1018 kg available as ground or surface water (Hoekstra
1998, p.34). Although, some aquifers recharge fairly quickly, the average recycle time for
groundwater is 1,400 years, as opposed to only 20 days for river water (Sampat 2000). Still,
groundwater is the water source for more than 1.5 billion people worldwide (up to 75% in
Europe, Sampat 2000, p.12). Sustainable use of groundwater requires recharge and protection
of the soil ecosystem. Unsustainable, excessive withdrawal resulted in dropping of the water
table, with consequently loss of biodiversity, and sometimes followed by land subsidence,
collapsing aquifers, and intrusion of saltwater. A more sustainable fresh water source is the
water that moves in the hydrological cycle through evaporation, precipitation, runoff and river
discharge. Even though, figures4 on the available amount of this fresh water resource differ,
there seems to be enough to sustain the world population and the different ecosystems.
However, local water scarcity occurs as water is distributed unevenly, 40% of the land on
earth is classified as arid or semi-arid as it conceives only 2% of the continental run-off
(WHO 2000).
Maybe even more important than quantity is water quality, as nowadays pollution is a major
threat to the water resources. Sewage pollution is a common problem threatening human
health directly through the dissemination of pathogens. Furthermore, sewage leads to
pollution of water resources with nutrients (N, P), heavy metals, and other toxic compounds
such as traces of medicines (anti-depressia, heart medicines, hormones, etc). Industrial waste
gives also rise to dissemination of heavy metals and persistent organic pollutants, while
agriculture through intensive use of pesticides and fertilisers threatens water resources with
chemical pollutants.
4
The fresh water in hydrological cycle directly available to man is 43,000 km3 (Groot 1992), the amount world river flow
available for human consumption is 12,500 km3 of which half is already taken up by human consumption according to WHO
(WHO 2000). Vörösmarty 2000, quotes comparable figures, a long term available runoff of 40,000 km3/y, of which only
31% is accessible, while humans exploit more than half (10 to 15% of total available runoff), similar Hoekstra, estimates that
the freshwater renewal rate or total continental run-off is 40 to 47*1015 kg/y of which mankind withdraws about 8 to 9%
(Hoekstra 1998, p35) .
5
Sustainable wastewater treatment
1 Introduction
Precipitation
109.5 * 109 m3/y
Water use
2.1* 109 m3/y
Outflow surface water
86 * 109 m3/y
(Source RIVM/CBS 2000)
Evaporation
21.4* 109 m3/y
(domestic 0.8 * 109) m3/y)
Infiltration
80.4 * 109 m3/y
Inflow surface water
80.4 * 109 m3/y
(estimated: infiltration=precipitation+inflow-evaporation-outflow-use)
Figure 2: Water balance for the Netherlands (RIVM/CBS 2000).
Looking at Figure 2 makes one wonder how even in a water rich country such as the
Netherlands problems like desiccation and eutrophication can occur. Of course it is true that
water is distributed unevenly and that water quality is at least as important as quantity. On the
other hand, a wide range of high and low-tech solutions is available for water treatment
(including disinfection and desalination) and distribution. As such, we have to conclude that
the water crisis is not a resource problem but to a large extent a management problem.
Worldwide, there is a pressing need for sustainable solutions throughout the whole water
cycle to assure freshwater needed to support hygienic living conditions, industrial
development, irrigation, and conservation of ecosystems.
1.3.2
Decentralised systems to close cycles
Technically it is possible to close the water cycle on urban scale or even on household scale.
However, when using flush toilets one mixes the water cycle with the nutrient cycle. This
means often that nutrients such as carbon, nitrogen, phosphorus, potassium, calcium and
magnesium, are not returned to agricultural land but end up in surface water or through
treatment in sludge or gaseous emissions. This is in contradiction with nutrient management
in natural ecosystems, where the flow of nutrients is conserved as much as possible and input
and loss are usually small compared with the volume, which circulates within the system,
notably in terrestrial systems (Groot 1992). Nutrient cycles are unbalance as some steps in the
natural nutrient cycles are limiting while other steps are scaled-up by human activities. For
instance the natural binding of atmospheric nitrogen by bacteria and plants is limiting.
Nitrogen is abundant in the atmosphere and can be bound in industrialised processes to
produce artificial fertilisers, although this is a very energy intensive process. Due to this
human interference, the nitrogen cycle is the most disrupted element cycle, with related
problems as eutrophication, greenhouse gas emission, and possibly a direct effect on human
health through blue baby disease and cancer (Gijzen and Mulder 2001). Other nutrients, as
phosphorus and potassium, are mined for artificial fertiliser production. The lack of gaseous
compounds deprives these cycles of an atmospheric pathway linking land and sea, this means
6
Sustainable wastewater treatment
1 Introduction
that the closing of these cycles depends on very slow pace of sedimentation, uplift, and
weathering (Groot 1992, Lange 1997, p.51-54). These nutrients can therefore be seen as
unrenewable resources. The closing of both the water and the nutrient cycle are essential in
finding sustainable solutions for the water sector.
1.3.3
Small scale systems for source control to enable reuse
Conventional solutions in the form of water flush toilets, combined sewerage, and centralised
treatment did not bring an integral solution. On the contrary, the system dilutes the waste
stream which makes it extremely difficult to separate the value components from the toxic
components. This initiated a wide range of experiments with storage of rainwater in the
sewerage system, rainwater infiltration, rainwater or household water for toilet flushing, low
flush and vacuum toilets, the separate treatment of black and greywater and possibly even
yellowwater (urine), anaerobic digestion, etc. One of the main questions is whether we can
obtain sustainable urban water management through improving the existing systems or
whether we will have to switch to completely new systems, for instance decentralised
systems.
Operating processes on a large scale may be preferable with respect to robustness and
economy of scale. However, the end-of-pipe approach of centralised systems requires an
increasingly sophisticated treatment as regulations become stricter, making the treatment
increasingly expensive (for historical background see Box 2). An advantage of centralised
treatment is the fact that monitoring and control of one single plant is easier than a large
number of small ones. If not referring to robustness but solely to the number of treatment
plants, one should realise that new techniques make it is possible to monitor and control at a
distance, and that the effect of failure in a small-scale plant is less severe (see also Butler
1997). The main advantage of decentralised systems is that it offers the possibility to keep
different wastewater streams separate, use different treatment techniques and close water and
nutrients cycles locally. In addition the expensive transport of wastewater over large distances
is eliminated. Furthermore, small-scale solutions are often experienced as very positive as it
visualises sustainability in an understandable way. However, to make small-scale solutions
successful the acceptation and participation of the end user and the different institutions
involved is often needed.
1.3.4
Optimising the existing systems or introducing to new systems?
In the theory of large-scale technological system the growing in scale and complexity is
sometimes described as growing autonomy of the system. The autonomy makes it difficult for
alternative system to compete. At the same time, the growing complexity may cause internal
defects in the system. Large-scale system created to control ‘old’ problems, may at the same
time create ‘new’ uncontrolled and often invisible or distant problems. The urban water
system, for instance, prevents spreading of diseases and flooding, however, uncontrolled by
this system are combined sewer overflows in case of heavy rains, diluted toxic components
are hard to remove and nutrients are often difficult to recycle. While some researches state
that the autonomy off the large-scale systems is hard to break, others recognised that largescale systems go through different phases of stable growth, conflict, and reconfiguration
(Vleuten 2000).
7
Sustainable wastewater treatment
1 Introduction
Box 2: Some historical dynamics of the urban wastewater system.
Traditional water services as drinking water supply and wastewater management were
decentralised. Wells, surface water, and rainwater harvesting provided drinking water, while dry
toilets such as pitlatrines or barrel latrines were used for sanitation, or else the excreta were
dumped in the surface waters. The sludge gathered in latrines could be used as fertiliser on
agricultural land. The rest of the domestic wastewater as well as surface runoff was either
infiltrated or channelled to surface water. In densely populated urban areas these solutions were
often not hygienic. With the increasing knowledge of spreading of diseases the importance of
hygienic water supply and wastewater management became apparent. Around 1840 to 1900
improved drinking water supply and sewerage systems were introduced in the larger urban areas
in Europe. The introduction of these systems was very expensive and therefore there was a lot of
discussion, delay, and implementation of partial solutions (Lintsen 1993, p.60).
Part of the discussion focussed on the recycling of nutrients to agriculture by collecting urine
and faeces separate (Lintsen 1993, p.58). In the Netherlands two systems were implemented for
this purpose: the barrel latrine and the Liernur system (Lintsen 1993, p.71-76 (barrel) p.62-71
(Liernur), Lange 1997, p.13-15). The shortage of fertiliser in agriculture made the human
excreta valuable. Furthermore, the barrels system was simple and required low investments,
however the transportation of the excreta was unhygienic (spilling, smelling). The Liernur system
was an ingenious vacuum system transporting the excreta to collection tanks. These collection
tanks were emptied by pumping wagons. This closed system was considered very hygienic and
tested in Amsterdam. Leiden and Dordrecht. Technically the system functioned well, but costs
were higher and revenues lower than expected (Lintsen 1993, p.65). In 1915 in Leiden the system
was taken out when the iron pipes were corroded and needed replacement (45 years after
installation) (Lintsen 1993, p.65). In Amsterdam where almost 40% of the population was
connected, the introduction of the water closet system meant the end of the Liernur systems
(Lintsen 1993, p.68). Large-scale introduction of combined sewerage systems followed, with the
advantage that all wastewater was directly taken out of sight. The investment costs were high,
but at that time one expected no exploitation costs, as the wastewater was discharged without
treatment and maintenance was not considered necessary. Once the choice of a combined
central sewerage system was made the implementation of end-of-pipe treatment to prevent
pollution of the surface water was a logical next step.
The expansion of the central sewerage systems in the Netherlands has come to an end. The
few households (3%) that are not connected are allowed to treat their wastewater with a smallscale systems onsite. Throughout its history, the central treatment systems have been growing
in scale and complexity. The growing of scale is limited through the costs of transporting
wastewater. But the complexity is increasing, as the restrictions of discharging effluent and
sludge are getting tighter. This could mean that system is in a phase of conflict that will lead
to reconfiguration.
Or do the advantages of small-scale solutions counter balance the disadvantages? Should we
continue with improving the existing system? Or could one say that environmental problems
have become the reverse salient5 of current large-scale systems such as conventional
wastewater treatment systems (Nielsen 1999). A technological breakthrough can occur once
the new decentralised approach is accepted as better than the centralised end-of-pipe
treatment. Possibly a mixture of decentralised and centralised treatment can be created to
5
A key concept in understanding large technical systems is reverse salient, which is a part of the system that is behind in the
development compared to other systems and therefore reduces the capability of the whole system. Reverse salient can lead
to closure of a system if it weakens its competitive position in relation to alternatives (Nielsen 1999).
8
Sustainable wastewater treatment
1 Introduction
combine the advantages of both systems. For instance decentralised treatment for water reuse
inside the households, and centralised systems outside the urban area to regain the nutrients
for use in agriculture. The changing perspective, induced by sustainability, triggers this
process of change, however, before switching towards a more decentralised approach, smallscale systems with water and nutrient recycling have to prove themselves.
Small scale systems are sometimes seen as intrinsic sustainable (Schumacher 1974), however
in wastewater treatment the main advantage is to treat waste streams separate. This means that
different treatment processes can be applied, enables the closing of the water and nutrient
cycles.
1.4 Research objective
The objective of the research is to gain insight into the sustainability of domestic water
systems, including water supply, water use, and wastewater treatment. Thus we want to
contribute to the development of a methodology for sustainability assessment, and to give an
overview of promising combinations of wastewater treatment technologies and the trade-offs
made in technology choices. We are specially interested in the selection of affordable systems
and systems that close the water and nutrient cycles as we believe those are needed to fulfil
the challenge of this millennium: providing safe drinking water and hygienic sanitation to all
by 2025.
1.5 Overview of the research
This chapter introduced the concept of sustainability and the present problems in water
management. The next chapter gives an overview of methodologies applied to assess the
sustainability of different parts of the urban water cycle and explicates the use of
sustainability indicators in system analysis, the methodology we are applying. Chapter 3
describes the implementation of the sustainability assessment in the form of the decision
support tool. Subsequently, Chapter 4 provides an overview of the water services and
quantifies all inputs needed to quantify the streams and sustainability indicators in the
decision support tool. In Chapter 5 the decision support tool is completed through the
description of the technologies. Chapter 6 presents the results of the calculations executed
with the tool, describing the use of the tool and the sustainable water systems selected. The
last Chapter, number 7, gives an overview of the conclusions, discusses all findings and gives
recommendations for further research.
References
Bergh, C.J.M. van den, and Straaten, J. van der (editors) (1994). Towards sustainable development, concepts,
methods, and policy, International society for ecological economics, Island Press, Washington DC and
Covelo California, ISBN 1559633492.
Butler D. and Parkinson J. (1997). Towards sustainable urban drainage, Water Science and Technolgy, vol.35,
no.9, p.53-63.
Cosgrove W.J. and Rijsberman F.R. (2000). World water vision, making water everybody’s business, for the
World Water Council, Earthscan Publications Ltd, London. (Internet http://www.watervision.org/ ).
Dirkzwager A.H. (1997). Sustainable development: new ways of thinking about water in urban areas, European
Water Pollution Control, Vol .7, No.1, Jan. 97, pp.28-40.
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1 Introduction
Finnson A. (1996). Sustainable urban water systems, Platform for research submitted to MISTRA, Swedish
Environmental Protection Agency.
Gijzen H.J. and Mulder A. (2001). The nitrogen cycle out of balance, Water21, August 2001, pp.38-40.
Groot, R.S. de (1992). Functions of nature, evaluation of nature in environmental planning, management and
decision making, Wolters-Noordhoff, ISBN 900135594 3.
Hoekstra A.Y. (1998). Perspectives on water, an integrated model-based exploration of the future, PhD thesis
Delft University of Technology, Civil Engineering, Published by International Books, ISBN 9057270188.
IRC (1997). Two billion and counting, Water newsletter, no.251, Oct. 1997, IRC , The Hague, The Netherlands.
IUCN (1991). Caring for the earth: a strategy for sustainable living, Earthscan Publications Ltd, ISBN:
1853831263.
IUCN (1980). World conservation strategy: living resource conservation for sustainable development, by the
International Union for Conservation of Nature and Natural Resources (IUCN), with the advice, cooperation
and financial assistance of the United Nations Environment Programme (UNEP) and the World Wildlife
Fund, ISBN: 2880321018, 2880321042.
Mara D.D., and Feachem R. (2001). Taps and toilets for all- two decades already, and now a quarter of a
century more, Water 21, August 2001, pp.13-14.
Lange J, Ottenpohl R (1997). Ökologie aktuell, Abwasser Handbuch zur einer zukunftfähigen Wasserwirtschaft
(Wastewater handbook, for a future oriented water management), Mallbeton GmbH, DS Pfohren, ISBN
3980350215 (in German).
Lintsen H.W. (editor) (1993). Geschiedenis van de techniek in Nederland, de wording van een moderne
samenleving 1800-1890, Deel II Gezondheid en openbare hygiëne, Waterstaat en infrastructuur, Papier,
druk en communicatie, (History of technology in the Netherlands, towards a modern society 1800-1890,
part II, Health and public hygiene, water and infrastructure, Paper, press and communication),Stichting
Historie der Techniek, Walburg Press, Zutphen, the Netherlands, ISNB 9060118367 (in Dutch).
Meadows D.H., Meadows D.L., Randers J., and Behrens (1972). Limits to growth, a report for the Club of
Rome’s project on the predicament of mankind, Universe Books, New York, ISBN 0856440086.
Mitcham C. (1995). The concept of sustainable development: it’s origins and ambivalence, Technology in
Society, Vol. 17, No. 3, pp. 311-326.
Nielsen, S Balslev (1999). Urban ecology and transformation of technical infrastructure, Paper for IPS specials,
February 1999, Department of Planning, building, Technical University Denmark, Lyngby.
Otterpohl R., Grottker M., and Lange J. (1997). Sustainable water and waste management in urban areas, Water
Science and Technology, vol.35, no.9, pp.121-134.
Ravetz J. (2000). Integrated assessment for sustainability appraisal in cities and regions, Environmental Impact
Assessment Review, 20 (2000), 31-64.
Schumacher E.F. (1974). Small is beautiful, a study of economics as if people mattered, London, Abacus, ISBN
0349131325.
SCC (1999). Water Supply and Sanitation Collaborative Council, Damme, Hans van, and Chatterjee, Ashoke,
Vision 21- water for the people, (available on the Internet: http://www.wsscc.org/index.html
Tijmes P. and Luijff R. (1995). The sustainability of ‘Our common future’: An inquiry into the foundations of an
ideology, Technology in Society, Vol. 17, No 3, pp 327-336.
UNDP (1998). Human development report 1998, New York: Oxford University Press, ISBN 0195124596.
UNEP (1999). Global Environmental Outlook 2000 (GEO2000), UNEP’s millennium report on the environment,
Earthscan publications Ltd London, ISBN 1 85383 588 9 (Internet: http://www.unep.org/Geo2000/ ).
Vleuten, E. van der (2000). Twee decennia van onderzoek naar grote technische systemen: thema’s en
afbakening en kritiek (Two decennia of research in the field of large scale technological systems: themes,
boundaries, and critic), Reprint TM/675, also appeared in the NEHA-jaarboek voor economie, bedrijfs- en
techniekgeschiedenis, dl.63, 2000, p.328-364, ISSN 13805717 (in Dutch).
Vörösmarty C.J., and Sahagian D. (2000). Anthropogenic disturbance of the terrestrial water cycle, BioScience,
September 2000, vol.50, no.9, pp.753-765)
WCED (1987). (World Commission on Environment and Development so-called Brundtland report), Our
common future, New York: Oxford University Press, ISBN 019282080X.
WHO(2000). Global water supply and sanitation assessment, report 2000, World Health Organisation (WHO)
& United Nations Children’s Fund (UNICEF) Joint programme for water supply and sanitation, ISBN
9241562021 (internet: http://www.who.int/water_sanitation_health/Globassessment/GlasspdfTOC.htm).
WRR (1994). Report to the government by the Scientific board for governmental policies, Duurzame risico’s:
een blijvend gegeven (Sustainability risks, a lasting fact), Sdu, ‘s Gravenhage, ISBN 90-399-0719-6 (in
Dutch).
10
Sustainable wastewater treatment
2 Assessing sustainability
2 Assessing the sustainability of domestic water systems
Like development, sustainability is per definition impossible to measure in absolute terms.
The subjectivity in terms such as ‘human needs’ and ‘quality of life’ make it impossible to
define and measure sustainability unambiguously. To achieve our goal of providing insight
into the sustainability of domestic water systems, indicators are defined to compare a wide
variety of systems with respect to sustainability. This chapter describes the different
methodologies that can be used for such a sustainability.
2.1 Exploration of methodologies (literature review)
Our implementation of the methodology in a model-based decision support tool is described
in Section 2.3, whilst the methodology of sustainability assessment itself is explicated in
Section 2.2. This methodology utilises the results of previous research done in the field of
sustainable or environmental friendly water systems. For instance, some researchers attempt
to capture sustainability in a single indicator such as Exergy and Economic analysis.
However, other, frequently used methodologies include multiple indicators as for instance
Life Cycle Assessment or System analysis. This section, 2.1, analyses those four
methodologies with respect to the scope of their application in assessing the sustainability of
the domestic water cycle.
2.1.1
Exergy analysis
Exergy is defined as the part of energy that can perform mechanical work, as such it expresses
the quality of a energy flow. Hellström, for example, compared different wastewater systems
based on exergy (see Box 3).
The advantage of the exergy analysis is that the whole comparison is based on a single
unambiguously quantifiable indicator (exergy), no weighting of different indicators is
involved. This advantage is at the same time a limitation, as insight is gained into the
efficiency of the processes but not into the different environmental impacts the process has.
Box 3: Conclusions based on exergy analysis (Hellström 1997, 1998).
The analysis is based on a Swedish wastewater treatment plant focusing on the exergy contained in
heat, organic matter, phosphorus, nitrogen, and electricity. The exergy of clean water and
uncontaminated nutrients was not incorporated.
• The exergy analysis shows that the largest exergy input is due to electric tap water heating.
The calculated exergy is even underestimated because the energy losses to the surroundings
are not included. However, the exergy reduces considerably when an energy source with a
lower exergy than electricity is used.
• Considerable flows of exergy are related to handling of organic matter. Possibilities to
recover exergy as methane gives a strong influence on the exergy balance.
• Comparing exergy flows related to nutrients we see that phosphorus is almost insignificant,
whilst nitrogen is significant.
When comparing the wastewater treatment plant with sources separation and on-site treatment the
following conclusions are most interesting:
• If nitrogen removal is considered important urine separation toilets and onsite treatment
become an interesting alternative. However, efficient utilisation of organic matter is crucial,
and the on-site treatment techniques are area-demanding (infiltration sites and wetlands).
• The magnitude of the system, as well as the access to arable land and the degree of dilution,
has a significant influence on the exergy consumption of the urine separation system.
11
Sustainable wastewater treatment
2.1.2
2 Assessing sustainability
Economic analysis
The economic theory also suggests a single indicator approach. A strong argument to express
sustainability in terms of money is the general use of financial indicators in decision-making.
Tools such as: Cost-Benefit Analysis, Life Cycle Costing, and Total Cost Assessment, all
balance the expected costs and benefits, and are often the first step in a project. In theory, all
kinds of costs and benefits can be included, however in practice these tools are mostly used as
a one-dimensional techniques incorporating only financial costs and benefits. The obvious
reason is that most social and environmental costs are difficult to quantify.
De Groot (1992) combined Environmental Impact Assessment with Cost-Benefit Analysis to
provide a decision support tool based on the concept of sustainability. He defined 47
functions of nature, categorized in four groups: (1) Regulation functions (for instance,
regulation of climates, chemical composition of the atmosphere and oceans, storage and
recycling of nutrients, etc.), (2) Carrier functions (for instance crop growing, settlements,
tourism, etc.), (3) Production functions (for instance water, fuels, raw materials, etc.), and
(4) Information functions (such aesthetic, spiritual, scientific information, etc.). In three case
studies is shown that it is possible to estimate monetary values of some functions using:
market prices, costs of environmental damage, maintenance costs, mitigation costs,
willingness to pay, property pricing, and travel costs. Other functions are indicated
qualitatively. In this way, a more refined feasibility study is presented to the decision-maker.
Indicating sustainability in monetary values has the advantage that indicators are easier to
handle in decision-making. However, translating environmental and social-cultural indicators
into monetary values is a part of the decision-making process since it includes normative
choices as determining values and weighing different indicators. In a perfect marketeconomy, prices would reflect the value of things as perceived by society. However, no
perfect market-economy exists and especially in the water sector prices are often determined
by governmental organisations, using price regulations such as taxes and subsidies. As such,
an economic analysis of the sustainability of water supply and wastewater treatment could
provide a valuable insight in the ‘real’ cost of water services.
2.1.3
Life Cycle Assessment
A methodology especially developed to assess different environmental impacts encountering
during a product’s lifetime is Life Cycle Assessment (LCA). This is a structured methodology
starting with defining the goal and scope of the study. Thereafter, a life cycle inventory of
environmental aspects is made, based on mass and energy balances. Finally, these
environmental aspects are categorised in environmental impact categories, such as depletion
of resources, global warming potential, ozone depletion, acidification, eco-toxicity,
desiccation, eutrophication, landscape degradation, etc. These categories can be normalised
and weighted to come to a final decision whether to choose one technology or the other. The
advantage of LCA is the well-described and standardised structure and the fact that it is
applied to a wide range of products and services including the different parts of the urban
water cycle (see Table 1) (ISO 14040 – 14043).
However, LCA has some drawbacks, assessing the whole life cycle requires lots of data, and
aggregating data into the standardized environmental impact categories means loss of insight
in emissions specifically interesting for wastewater treatment. Furthermore, additional
indicators are needed to indicate sustainability as LCA limits itself to environmental aspects.
12
Sustainable wastewater treatment
2 Assessing sustainability
Table 1: Conclusions based on Life Cycle Assessment.
Crettaz
1997
Hoek
1999
Dennison
1997
Emmerson
1995
Technologies compared:
Rain water systems (10m3,
50m3, and a centralised
system for 5 houses), and
drinking water supply.
Comparison of the supply of
drinking water, rain water,
treated greywater, and
household water produced
out of IJ-lake water, river
Rhine water, polder seepage
water, effluent, brackish
groundwater, or local
infiltrated rain water.
15 different treatment plants
(activated sludge, percolating filters, bio-discs) and 5
sludge treatment & disposal
strategies (land application,
digestion, com-posting,
thickening dewatering).
Activated sludge and 2
biological filter plants.
Functional unit:
Toilet flushing for
one person during
one day (54 litre or
22 litre per person
per day depending
on type).
Assumed is a
drinking water use
of 144 litre per
capita per day,
household water
will substitute 37%
of this demand,
greywater 21%,
rainwater 16%.
9.8*108 kg raw
sewage to be
treated and sludge
to be disposed.
Main conclusions:
The rainwater systems use pumps and require more
energy than conventional water supply. The 10m3
systems and the centralised system use the same
amount of energy, while the 50m3 system uses 30 to
60 % more. A water saving toilet is a good way to
safe water.
Dual water supply with household water produced
out of IJ-lake water is chosen as best option. The
options to use treated effluent and infiltrated
rainwater are rejected on public health aspects. The
use of rainwater, greywater, Rhine water, and
polder seepage water, is rejected based on costs.
The environmental impact of the use of brackish
groundwater is higher than the use of IJ-lake water
and drinking water.
Important improvement would be to decrease the
sludge volume to be transported and disposed,
dewatering or composting are good options for this
purpose.
Population 1000,
dry weather flow
200 m3/d.
The activated sludge plants used considerably less
materials (30% land take, 11% raw materials, 10%
solid waste), the biological filter plants used less
energy (56%). For rural areas land use is no
problem, therefore the more energy efficient
biological filter plants are preferred, taking in
account additional benefits as less control needed
and being resilient to shock loads. Potential
improvements lay in optimising energy use in
aeration system, reducing the material used (PVC!)
and use recycled materials.
The process with chemical pre-treatment is
favourable according to this analysis. Also when the
plant is expanding to tertiary treatment.
Furthermore, the biogas from anaerobic sludge
digestion represents a valuable resource, thus the
process maximising biogas production is favoured.
Main environmental impacts associated with sludge
treatment are: energy consumption, use of diesel in
transportation, direct emissions such as NH3 and
NH+4. Because of the process-inherent output of
methane gas that can be used for energy
production, the anaerobic variants score better on
energy use and these score also better on in
terrestrial eco-toxicity in zinc equivalents, but less
good on eutrophication measured in PO3-4
equivalents per kg P.
Urine sorting system is preferable to more
conventional treatment.
Primary treatment
(mechanical), secondary
(chemical or
chemical\biological),
tertiary treatment (with and
without N-removal).
6 different sludge recycle
strategies: anaerobic or
aerobic digested liquid
sludge, or adding
dewatering or adding
composting.
100,000
population
equivalent.
Bengtsson
1997
Urine sorting and
conventional.
Bengtsson
1997
Liquid composting and
wastewater mini-plant.
Bengtsson
1997
Local treatment and
spreading and central
treatment.
Treatment of the
sewage from one
person-equivalent
during one year.
Treatment of one
person’s sewage
and organic waste
during one year.
Handling of 1m3
raw sewage
sludge.
Ødegaard
1995
Neumayr
1997
One kg phosphorus
on the field.
13
Environmental impact from liquid composting is
lower than impact from wastewater treatment in a
mini-plant.
Local treatment and spreading is more environmentally advantageous in comparison with transport and treatment in a central treatment plant.
Sustainable wastewater treatment
2 Assessing sustainability
These disadvantages of LCA are reflected in the fact that most researchers adapt LCA for
their research by looking at the operational phase only, by not categorising the emissions but
using them straight away for their assessment, or by adding impact categories such as land
use, costs, etc. (see Table 2 on page 18). If assessing the operational phase only, one should
no longer speak of a LCA, but of a chain analysis or environmental impact analysis. When not
using impact categories but making the comparison on the emissions and wastes produced,
the analysis is a mass flow analysis rather than an environmental impact analysis.
2.1.4
System analysis
The general approach followed in a sustainability assessment of water services is a system
analysis, based on mass and energy balances and design equations to indicate material use,
emissions, costs and required land area. In principle LCA is a type of system analysis, also a
structured method based on mass and energy balances that uses indicators (in LCA called
impact categories). LCA is usually applied to compare a few technologies on environmental
impacts only. While system theory as a rule assesses the process more generally and abstract
by capturing the nature of the system in a mathematical description. In the case of domestic
water systems, this means focussing on the comparison of whole systems including water
supply, use and wastewater treatment, often incorporating larger numbers of alternatives, and
using a multi-dimensional set of sustainability indicators. Both looking at whole systems and
using a multi-dimensional set of indicators is essential for assessing sustainability. Looking at
the whole system one can find integrated solutions that may not be visible when looking at
smaller parts of the system. Similar, optimising in one dimension, for instance environmental,
will improve this aspect of the system but may have unwanted effects in other dimensions, for
instance the system may become unaffordable.
Like in LCA, the different system analyses are difficult to compare because the goals and
scopes as well as the assumptions differ per study. System analyses comparing a relatively
small number of systems are done by, for instance, Mels and van Nieuwenhuizen (1999),
Kärrman (2000), and Otterpohl (1997) (see Box 4).
Ranking a large number of systems can also be done by comparing the systems pair wise on
all selected indicators (for instance is system A more affordable than system B?, is system A
more affordable than system C? etc.). In this way, a decision matrix is built for each indicator.
These matrices are combined to reach a final decision. This approach is applied to wastewater
treatment by Ellis and Tang (1990, 1994). They defined 20 parameters, including technical,
economic, environmental and social-cultural factors, to rank 46 wastewater treatments
systems. Based on several case studies, they conclude that their method is useful in selecting
wastewater treatment systems.
Ellis and Tang compare different configurations of wastewater unit operations, however, if
one models the unit operations separately, the number of configurations is enormously
enlarged. This is illustrated by the screening analysis by Chen and Beck (1997), which is used
to classify and review over 120 unit operations for wastewater drainage and treatment. Chen
and Beck constituted 50,000 candidate wastewater treatment systems. Comparison of the
feasible candidate wastewater treatment systems led to the conclusions shown in Box 5. The
screening analysis of Chen and Beck uses fixed processing units that do not provide decision
makers the freedom to adapt to local conditions or to incorporate different assumptions.
14
Sustainable wastewater treatment
2 Assessing sustainability
Box 4: Conclusions based on system analysis.
Mels 1999:
Three wastewater treatment scenarios are compared: (1) low-loaded activated sludge with biological P
and N removal, (2) pre-precipitation before the low-loaded activated sludge followed by a rapid sand
filtration, and (3) floatation before the low-loaded activated sludge followed by rapid sand filtration.
• Scenario (1) generates more energy, less sludge, uses no chemicals, and is a bit cheaper,
however, more suspended solids remain in the effluent and the system requires more space.
• Scenario (2) and (3) do not differ much, (2) generates a bit less energy, uses some more space,
and is a bit cheaper.
Kärrman 2000:
Four systems are compared, conventional treatment, irrigation of energy forests, liquid composting,
and urine separation.
• All 4 alternative systems increase the nitrogen utilised by crops, and decrease the nitrogen
emission to water.
• All 4 systems remove more than 95% of the phosphorus. Urine separation is favourable in
phosphorus recycling, as this does not increase the cadmium content in arable soil.
• Looking at primary energy consumed, the difference between recycling of sewage sludge from
conventional treatment to use of mineral fertiliser is marginal (recycling of sludge is 5% more
efficient if transported 10 km – equal efficiency when transported over 1080 km). Using urine
as fertiliser is 30% more energy efficient compared to mineral fertiliser if the urine is
transported 1 km, and equally energy efficient if transported over 400km.
• Energy consumption is considerably higher in liquid composting.
• Liquid composting may be interesting when combined with treatment of organic wastes.
Otterpohl 1997:
The conclusions are based on a comparison between a conventional sanitation system and a vacuum
toilet system in which the blackwater is digested together with organic household waste and the
greywater is treated in constructed wetlands.
• The traditional concept has the disadvantage of using too much water, diluting the faeces and
rinsing out nutrients to the sea.
• Separate treatment of black and greywater has the advantage of using less energy and
materials; furthermore, emissions to receiving waters are reduced.
In contrast, a sanitation expert system such as SANEX (Loetscher 1999) uses information on
local circumstances to screen out inappropriate sanitation systems. With SANEX the choice
was made to provide a user-friendly interface thereby sacrificing on the transparency of the
model by not providing insight into the equations and assumptions used.
The ORWARE model (Dalemo 1999, Sonesson 1998) is again a more theoretically oriented
decision support tool. The steady-state material flow model is developed for evaluating the
environmental impact of organic waste management activities in different geographical areas,
especially focussing on the return of nutrients to arable land. All the 43 indicators being are
chemical compounds, such as BOD, metals, nutrients, etc. The main conclusions are
summarised in Box 6.
In general we can conclude that all of the above methodologies can lead to new insights when
applied to the urban water system. Exergy analysis, economic analysis, and LCA provide
specific insights into (energy) efficiency, ‘real’ costs, or environmental impacts respectively.
The system analysis is a more general approach and can include a wide range of aspects
through the use of self-defined sustainability, for instance exergy, costs, environmental
impacts, or even social-cultural aspects such as acceptance, convenience, etc.
15
Sustainable wastewater treatment
2 Assessing sustainability
Box 5: Conclusions based on screening analysis (Beck 1994, Chen 1997).
• Powerful discriminants to screen out technologies that lack promise are capital costs, land
area, the ability to remove heavy metals, N-bearing, and P-bearing materials.
• Most of today’s commonplace technologies, including the activated sludge process, the trickling
filter, and so on, are very rarely chosen as constituents of a satisfactory strand of technologies.
• Processes exploiting a high biomass, such as improved reedbed system (with constructed
infiltration facilities), pond systems, and all polishing technologies, including micro-filtration
and reverse osmosis, emerge as being essential to success
Box 6: Main conclusions based the ORWARE model (Dalemo 1999, Sonesson 1998).
• Land filling scenario has exceptionally high contribution to global warming even though 50%
of the methane was collected. It was concluded that this was the least desirable scenario.
• Environmental impact composting low for all categories, also low energy production.
• Incineration results in strong acidification and a large amount of heat.
• Anaerobic digestion has a large impact on the human health category and photooxidant
formation, also recirculated large amount of nutrients.
• The wastewater contained a major part of the plant nutrients although nitrogen is partly
removed at the sewage plant there is a large eutrophication impact on surface waters.
2.2 Exploration of methodology application—
application—a literature review
2.2.1
System boundaries
In general a sustainability assessment will not limit itself to a process but will rather be an
integrated assessment over a whole chain of processes that provide a certain service. This
wide view makes it possible to compare a large variety of solutions. For instance, comparison
of large-scale and small-scale wastewater treatment systems requires inclusion of the
household in order to enable separation of different wastewater streams, to apply different
forms of sanitation, and use and reuse different water sources. However, a wide view often
results in loosing details and possibly means a lot of extra work. A careful definition of the
system boundaries is therefore required in the goal and scope definition of the research.
Figure 3 visualises the system boundaries that different researches chose in their research.
2.2.2
Sustainability indicators
The definition of sustainability indicators is an important step, as the selection of sustainable
solutions is based on these indicators. A sustainable solution means limited use and limited
degradation of resources through harmful emissions, at the same time avoiding the export of
the problem in time or space. As described in the section on sustainable technology, we
distinguish three types of resources: economic, environmental and socio-cultural resources.
Therefore, the same classification is used for the indicators, including one additional category,
namely the functional indicators (see Figure 1 on page 3). While the economic,
environmental, and social-cultural indicators give insight in the efficiency of the solution, the
functional indicators determine the effectiveness of the solution. This last group of indicators
can therefore be seen as constraints, as it is no use defining an efficient solution if in the
perception of the end user this solution is not acceptable.
16
Sustainable wastewater treatment
2 Assessing sustainability
Figure 3: System boundaries applied in different researches.
An overview of the different indicators used in literature is given in Table 2. Note that due to
different goals and scopes as well as different terminology the different researches are not
comparable, as shown in Figure 3.
2.2.3
Interpretation of results
In researches comparing a small number of systems, the results are mostly interpretate to
select a single system for a particular situation. Whilst researches comparing a large number
of systems are often done to gain inside in the factors determining the sustainability of water
systems, for instance by listing decisive indicators, such as:
•
Organic matter – methane recovery may be essential for sustainable wastewater
treatment (Hellström 1997 and 1998, Neumayr et al. 1997, Otterpohl et al. 1997,
Ødegaard 1995), composting seems to be a promising option for sludge handling as well
(Dennison 1997).
•
Nutrients – urine separation may be essential for sustainable sanitation (Bengtsson
1997, Chen 1997, Dalemo 1999, Hellström 1997 and 1998, Kärrmann 2000, Sonnesson
1998).
•
Land area – land area is named in several studies for instance by Chen et al. 1997 and
Hellström 1997 and 1998, there is however a trade-off with other indicators. Chen et al.
for instance, mention land area as a decisive indicator to screen out technologies that lack
promise. Nevertheless, they conclude that constructed wetlands and pond systems are
among the technologies that emerge as essential to success.
17
Sustainable wastewater treatment
2 Assessing sustainability
Table 2: An overview of indicators used in the literature to compare wastewater treatment systems.
A
Economical indicators:
Costs
Labour
Environmental indicators:
Accumulation
Biodiversity / land fertility
Dissication
Export of problems in time &
space
Extraction
Integration in natural cycles
Land area required / space
Odour / noise / insects / visual
Optimal resource utilisation /
reuse:
Water
Nutrients
Energy
Raw materials
Pathogen removal / health
B
*
Bu
P
P
D
Em
*
E
F
H
C
S
P
S
S
100
S
P
I
J
L
E
M
N
O
S
Ø
*
E
T
T
P
Cn
P
S
P
S
P
1
S
P
V
V
V
S
S
S
S
Pollution prevention
Emissions:
BOD / COD
Nutrients
Heavy metals
Others
V
V
Sludge / waste production
V
100
0
100
10
100
0
V
V
S
V
100
0
100
100
0
100
0
10
V
S
S
S
S
S
P
P
P
P
P
S
S
S
S
S
P
S
S
P
V
V
S
S
S
S
V
S
S
S
t
S
t
S
t
S
P
S
S
S
S
P
S
S
S
S
P
Cn
Cn
Cn
Cn
V
P
S
S
S
S
V
V
S
S
V
Use of chemicals
V
S
S S
Technical indicators:
Durability
S
S
Ease of construction / low tech
P
Endure shock loads / seasonal
S
Cn
effects
Flexibility / adaptability
S
S
S
Maintenance
Cn
Reliability / security
S P
Small scale / onsite / local
S
T
P
solution
e
Social-cultural indicators:
Awareness / participation
S
S
S
Competence / information
S P
requirements
Cultural acceptance
S
S
Institutional requirements
S P
Local development
S
Responsibility
P
Source: A = Azar, Holmberg 1996, B = Bengtsson 1997, Bu = Butler 1997, D = DTO 1994, Em = Emmerson
1995, E = ETC 1996, F = Finnson 1996, H = Helström 2000, I = Icke 1997, J = Jacobs 1996, L = Lundin, 1999, M
= Mels et al. 1999, N = Niemcynowicz 1994, O = Otterpohl et al. 1997, Ø = Ødegaard 1995.
Note: The numbers in the table indicate the used weighting factors, the abbreviations refer to the terms used in the
publications; C=costs, Cn=concerns, E=environmental efficiency, P=principles for sustainability, S=sustainability
indicator / factor / criterion, St=steering variables, T=target, Te=technical paradigm, V=variables in the LCA inputoutput table, * = LCA study.
18
Sustainable wastewater treatment
2 Assessing sustainability
Although several researches name decisive indicators, none of them gives a clear analysis of
the trade-offs made, as such there is still limited insight in what systems are the most
sustainable in different situations. Through a sustainability assessment based on multiobjective optimisation that combine the results of the different tools into an integrated
assessment we want to gain insights in the trade-offs and select the most sustainable domestic
water systems for different cases. How this can be done is described in the remainder of this
chapter.
2.3 Methodological outline for this re
research
2.3.1 Methodology
A suitable and often used methodology for sustainability assessment is a system analysis using a
multi-disciplinary set of sustainability indicators. Similar to LCA, the methodology can be structured
into three phases: goal and scope definition, inventory analysis, and optimisation.
In the first phase, the goal and scope definition, the system boundaries and sustainability indicators are
defined. To avoid ruling out sustainable solutions beforehand the system boundaries must be set to
include whole systems rather than components and the sustainability indicators must reflect all
dimensions of sustainability, including functional, economic, environmental, and social-cultural
aspects. Some of these indicators are difficult to quantify, however, to assure the integrated and multidimensional character of the sustainability assessment it is better to include those indicators using
crude quantification rather than not including those indicators at all.
In the second phase of the analysis, the inventory analysis, the sustainability indicators are quantified
through mass and energy balances, cost-benefit analysis, and actor analysis, or indicated qualitatively.
In the third and last phase, the most sustainable systems are selected through multi-objective
optimisation using the normalised and weighted sustainability indicators as the objective function.
Literature analysis of LCA case studies reveals that this last phase, in LCA called the impact analysis,
is often omitted due to the rather subjective character of this step. Scientists like to avoid the more
political process of normalisation and weighting, however, this step is essential to the sustainability
assessment. The results of the different assessment methods should be used in combination in order to
obtain a balanced solution. The role of the scientist is to reveal the decisive indicators, trade-offs, and
the sensitivity to the individual weighting factors.
All the methodologies described in the previous sections have their advantages and
disadvantages. Achieving our objective, to provide insight in the achievable sustainability of
domestic water systems, can in our case best be done with a system analysis, as we are mainly
interested in the sustainability of the different technologies. Exergy analysis limits itself to
efficiency of processes in energy terms, which could be used as a component of the system
analysis, however goes into details beyond the scope of this research. The economic analysis
includes subjectivity in the translation of sustainability aspects into terms of money, as costs
often do not reflect ‘real costs’. For transparency we want to capture the subjectivity in
weighting factors only, economic analysis in the form of a Cost Benefit Analysis is a
component of our system analysis. LCA can also be seen as a component of our system
analysis; however, instead of the standardised environmental impact categories we use a selfdefined multi-dimensional set of sustainability indicators.
In our system analysis we try to combine the advantages of the different analysis methods.
The freedom to adapt the comparisons to local conditions, different scales, etc., can be
19
Sustainable wastewater treatment
2 Assessing sustainability
guaranteed by a model-based comparison, similar to ORWARE. The technology models
applied are based on design equations incorporate scale and some incorporate temperature as
indicator for the local climate. The freedom to incorporate local preferences can be expressed
in weighting factors, by formulating the decision problem as a mathematical, optimisation
problem with an objective defined as the weighted sum of the sustainability criteria.
Furthermore, the comparison of a large number of systems is enabled by modelling all unit
operations separately, similar to the approach of Chen and Beck (Chen 1997, Beck 1994).
2.3.2
Goal
We want to use the methodology to compare a large variety of different urban water systems.
Our goal is to provide insight into the sustainability of wastewater treatment systems.
Therefore, we aim at two outcomes for this research (1) to contribute to the development of a
methodology for sustainability assessment, and (2) to give an overview of promising
combinations of wastewater treatment technologies and the trade-off made in technology
choices.
Our assessment is based on literature data that we collected from a large number of
references. Some data is defined in terms of ranges. This for several reasons, (1) removal rates
will differ with circumstances such as climate and process control, (2) there are fluctuations in
the influent concentrations (3) data are gathered from different literature sources that may not
always be comparable, (4) the accuracy of the data is unknown. The data we use is therefore
very rough. it would be good to collect the opinions of a number of experts in the field to
compromise on the input data, however, this is beyond the scope of this project.
Our models will therefore be simple and general rather than detailed and precise. The added
value of the research is the combination of the wide system boundaries, the large number of
different technologies, the wide range of sustainability indicators, and the application of the
multi-objective optimisation.
This means that the models have to quantify all sustainability indicators for all potential
sustainable technologies that can be applied in water supply, domestic water use, and
wastewater management. Indicators that cannot be quantified and are considered important
are incorporated in the form of crudely quantified measures. According to the definition of
sustainable technology in Sectionfprintf('\n\nThe size of the variable xtot indicates a
optimisation with %4.0f evaluations',grx); 1.2, the technology has to be effective and
efficient. The functional indicators express effectiveness, in other words the technology has to
achieve a certain minimal performance. In the water sector this is for instance defined by
drinking water quality and effluent standards. The efficiency is expressed in the form of
economic, environmental, and social-cultural indicators. Meaning that the technology should
use the different resources in a balanced process without threatening the quantity and quality
of these resources. The resources in the domestic water system and potential options to
improve sustainability are described in the next chapter. This inventory provides the
quantification of the sustainability indicators that are the input data for the methodology. The
implementation of the methodology as a model-based decision support system is described in
Chapter 3. Resources are quantified in Chapter 4 and technologies in Chapter 5. The next
sections of this chapter describe the scope of this research.
20
Sustainable wastewater treatment
2 Assessing sustainability
2.3.3 Scope
2.3.3.1 Chosen System boundaries
We defined the domestic water system to include: water supply, domestic water use,
wastewater transport and treatment including sludge treatment (see Figure 4). Drinking water
treatment is excluded from the domestic water system, except for the water treated inside the
household. This choice is made because we are mainly interested in comparing different
wastewater systems. Rainwater use, water conservation, and water reuse, are more interesting
to us than drinking water production. The comparison of large-scale and small-scale
wastewater treatment systems requires inclusion of the household to enable separating
different wastewater streams.
We also chose to exclude agricultural and industrial water use and pollution. These sectors are
major water consumers and polluters; however, the required water quality as well as the
required wastewater treatment is very specific to the type of agriculture or industry. Industrial
wastewater could have been added as an extra input stream to the wastewater treatment in the
model however, water reuse and treatment of different water streams separately require a
detailed analysis of industrial processes, which is beyond the scope of this research.
Agricultural water use is essential different from industrial and domestic water use as
irrigation, uptake by plants and soil is a diffuse process. Analysing this in a similar approach
as the domestic water system would require the modelling of the whole agricultural
ecosystem. Also, since the conditions in each of these later systems are very specific to the
sectors, the assumption that the water treatment systems will have to be tailored to these
conditions and thus require individual designs seems appropriate.
Crucial aspects of the domestic water system are the control of pathogens and nutrient
recycling. Within the domestic water system the food and water cycles are mixed as the waste
in domestic wastewater originates largely from the nutrient cycle (see Figure 4). Therefore,
recycling nutrients is one of the main focuses.
Figure 4: System Boundaries.
21
Sustainable wastewater treatment
2.3.3.2
2 Assessing sustainability
Selected Sustainability indicators
The definition of sustainability indicators is essential, as the selection of sustainable solutions
is based on these indicators. A sustainable solution aims at limited use and limited
degradation of resources through harmful emissions, at the same time avoiding the export of
the problem in time or space. As described in Chapter 1, we distinguish three types of
resources: economic, environmental and socio-cultural resources. As mentioned above, we
use the same classification but add the class of the functional indicators (see Figure 1 on page
3), which can be seen as constraints.
Literature describes the use of different sets of sustainability criteria (Azar 1996, Foxon 2000,
Hellstrom 2000, Holmberg 1995, Kärrman 2000, Larsen 1997, Lundin 1999, Mels 1999,
Otterpohl 1997, Parkinson 1997). However, there seems to be a consensus as functional
indicators measuring performance are chosen for the effluent and sludge quality. Economics
is mostly expressed in terms of monetary costs and benefits, while environmental indicators
often incorporate both resource use and emissions.
Literature describes the use of different sets of sustainability criteria (Azar 1996, Foxon 2000,
Hellstrom 2000, Holmberg 1995, Kärrman 2000, Larsen 1997, Lundin 1999, Mels 1999,
Otterpohl 1997, Parkinson 1997). However, there seems to be a consensus as functional
indicators measuring performance are chosen for the effluent and sludge quality. Economics
is mostly expressed in terms of monetary costs and benefits, while environmental indicators
often incorporate both resource use and emissions.
It is widely recognised that other aspects are also important for the functioning of the system,
for instance the ability to cope with shock loads, the maintenance required, cultural
acceptance, etc. However, these aspects are more difficult to quantify and therefore rarely
used. In developing a decision support tool we aim at including a comprehensive list of
sustainability indicators thereby providing the decision maker with a complete view on the
various aspects of the technology. Realising that a decision maker has the option to ignore
individual indicators, we define a “super-set” of indicators by including, qualitatively, the
following hard-to-quantify-indicators: acceptance, adaptability, expertise, quality of space,
institutional requirements, maintenance, participation, reliability, robustness, and stimulation
of sustainable behaviour. A complete overview of the indicators is given in Table 3.
As stated in the beginning of this chapter, sustainability is by definition impossible to measure
quantitatively. As knowledge proceeds, ideas changes, situations and priorities, there is no
guarantee that the solutions we identify as sustainable today will prove to be sustainable in the
far future. Although, one may expect that a system with 100% reuse and powered by solar
energy could stand the test of time. Still, the set of sustainability indicators defined below
should be seen as a first step. Over time new indicators will be added and others, proven less
effective will be removed.
Functional indicators:
Functional indicators define the minimal technical requirements of the solution. In our
model there is one crucial indicator namely the functional indicator waste. Waste is defined
as those outgoing streams that have potentially no reuse and cannot be discharged based on
restrictions in legislation and health guidelines. This means that if one allows waste to be
produced and for instance optimise costs the selected domestic water system will consist of
water use only as one defined the quality of outgoing streams as not important. Thus it is the
22
Sustainable wastewater treatment
2 Assessing sustainability
amount of waste produced that indicates the ineffectiveness of the domestic water system.
Additional indicators are adaptability (possibility to extend the system in capacity, or with
additional treatment), robustness (ability to cope with fluctuations in the influent),
maintenance required, and reliability (sensitivity of the system to malfunctioning of
equipment and instrumentation). Compared to waste these indicators are optional, like all
other indicators defined below.
Economic indicators:
We include the commonly used indicators: estimated investment costs and operation and
maintenance costs accounting for the estimated lifetime. Based on these one can derive secondary
indicators such as affordability and cost effectiveness.
Environmental indicators:
Our environmental indicators are split into 2 categories, namely (1) optimal resources and
(2) emissions. The optimal resource use is expressed in indicators such as water use,
energy use, bio-gas production, space requirements, the quality of the utilised space,
and the possibility to reuse nutrients in the form of fertiliser or soil conditioner. For water
use several indicators are available, namely total water use, drinking water use, household
water use, rainwater use, and possibility to reuse treated wastewater in the household or for
irrigation or infiltration. Emissions are expressed in the indicators discharge, waste, and
CSO. We would have liked to include emissions to air and the use of chemicals in
processes such as precipitation but due to the scarcity of available data we left those
indicators out for the time being.
Social-cultural indicators:
Both social and cultural indicators are difficult to quantify and are therefore often not
addressed. However, these indicators play an important role in the implementation of
technology. This is especially the case when the end-user is directly involved, like in water
use, sanitation, and on-site treatment. The social-cultural indicators included are:
• Acceptance: In different cultures, people will have a different perception of waste and
sanitation, resulting in different habits. New sanitation concepts in particular different
toilet systems may encounter social-cultural difficulties in the implementation. For
instance: the need to explain visitors how to use the separation toilet was one of the
reasons to remove these toilets from the houses of an ecological village (Fittschen &
Niemczynowicz 1997).
• Expertise: The selected technological solution requires a certain level of expertise for
installation and operation. If the expertise is not locally available it may be gained
through import or training.
• Institutional requirements: Different wastewater treatment systems will require
different regulations and control mechanisms. These requirements should fit in the
existing institutional infrastructure of the region.
• Participation: Participation of different actors, including the end-users, creates
ownership, responsibility, and awareness.
• Stimulation of sustainable behaviour: Sustainable behaviour can be stimulated by
tailoring the technological design such that sustainable behaviour is the most
convenient option. Sustainable behaviour can also be stimulated through increased
awareness.
23
Sustainable wastewater treatment
2 Assessing sustainability
The quantification of the sustainability indicators for the inputs to the model is given in
Chapter 4, and for the different technologies in Chapter 5. The next chapter describes the setup of the decision support tool in more detail.
Table 3: Sustainability indicators for the domestic water system.
Indicator:
Functional:
- Adaptability
- Maintenance
- Reliability
- Robustness
- Waste
Economic:
- Costs
Environmental:
Emissions:
- CSO
- Discharge
Resource Utilisation:
Energy:
- Energy use
– Gas
Space:
- Land area
- Quality of space
Nutrients:
- Fertiliser
- Soil conditioner
Water:
- Total water use
- Discharge
- Domestic reuse
- Drinking water
- Household water
- Infiltration
- Irrigation
- Rainwater use
Social-Cultural:
- Acceptance
- Expertise
- Institutional
requirements
- Participation
- Sustain. behaviour
6
Description:
Expressed:
- Indication of flexibility of the process with respect to the
implementation on different scales, increasing / decreasing of
capacity, and anticipate on changes in legislation etc.
- Indication of maintenance required.
- Indication of sensitivity of the process concerning
malfunctioning equipment and instrumentation.
- Indication of sensitivity of the process concerning toxic
substances, shock loads, seasonal effects etc.
- The effectiveness of the treatment is expressed in the
sustainability indicators that define reuse and waste. If reuse or
discharge is not allowed the stream is categorised as waste.
Qualitative
- Investment and operation and maintenance costs.
Euro
- Untreated wastewater discharged combined sewer overflow.
- Treated water that can be discharged (TSS, BOD, N, P)6.
m3
m3
- Energy used by treatment.
- Bio-gas produced in anaerobic treatment.
kWh
m3
- The total land area required.
- Indication of the possibilities to integrate the wastewater
treatments system (partly) in green areas.
m2
Qualitative
- Nutrients suitable for reuse, (P,Cu,Zn).
- Stabilised unpolluted organic matter (TSS, Cu, Zn).
kg
kg
- Sum of different water uses
- Treated water that can be discharged (TSS, BOD, N, P)
- Treated water suitable for domestic reuse (TSS, Cu, FC).
- Amount of drinking water used.
- Amount of household water used.
- Treated water suitable for infiltration (TSS, P, Cu, Zn).
- Treated water for irrigation (TSS, BOD, Cu, Zn, FC).
- Amount of rainwater used.
m3
m3
m3
m3
m3
m3
m3
m3
- Indication of the cultural changes and impacts: convenience and
correspondence with local ethics.
- Indication whether a system can be designed and built or can be
repaired, replicated and improved locally / in the country / or
only by specialised manufacturers.
- Indication of efforts needed to control and enforce the existing
regulations and of embedding of technology in policymaking.
- Indication of the possibilities for end user participation..
- Indication of stimulance in the design to behave sustainable.
Qualitative
See for details on the categorisation of outgoing streams as waste or reuse Table 13 on page 56
24
Qualitative
Qualitative
Qualitative
m3
Qualitative
Qualitative
Qualitative
Qualitative
Sustainable wastewater treatment
2 Assessing sustainability
References
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January 1997.
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sewage-treatment processes, J.CIWEM, 9, June, 317-325, 1995.
Foxon T.J. (2000). A multi-criteria analysis approach to the assessment of sustainability of urban water systems,
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Netherlands, pp. 119-128.
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sanitary engineering, Sweden.
Hellström D. (1997). An exergy analysis for a wastewater treatment plant - an estimation of the consumption of
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Hoek J.P. van der, Dijkman B.J., Terpstra G.J., Uitzinger M.J., and Dillen M.R.B. van (1999). Selection and
evaluation od a new concept of water supply for “IJburg” Amsterdam, Wat. Sci. Tech., vol.39, No.5, pp.3340.
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Physical Resource Theory, Göteborg, Sweden.
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Göteborg, Sweden.
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Technology, No. 9, pp 3-10.
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variations in sustainability of sanitary systems, Wat. Sci Tech. vol. 39 no. 5 pp 235-242.
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Mels A.R., Nieuwenhuijzen A.F., Graaf J.H.J.M. van der, Klapwijk B., Koning J. de, Rulkens W.H., (1999).
Sustainability criteria as a tool in the development of new sewage treatment methods, Wat. Sci Tech. vol.39,
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26
Sustainable wastewater treatment
3 Model based decision support tool
3 Developing a modelmodel-based decision support tool
The proposed methodology, as described in Chapter 2, is implemented in a model-based
decision support tool. The structure of this tool is visualised in Figure 5. It consists of four
parts: (1) data storage, (2) model of the domestic water system, (3) the quantification of the
sustainability indicators, and (4) optimisation. These four parts are described in the following
paragraphs
Figure 5: Outline of the model-based decision support tool.
3.1 Data
Input data is obtained through an extensive literature review (Balkema 2001). This
spreadsheet, named “Data2003”, summarises all data that is used in the calculations, including
inputs such as water use, contaminants added to the water, prices of commodities and
technologies, performance of technologies, normalisation factors, etc. The spreadsheet
provides decision makers with a complete overview and enables adaptation of the data (see
Appendix 1). The data can be transferred to Matlab-Simulink and are the input for the
calculations.
3.2 Model of domestic water systems
All calculations are performed in the Matlab-Simulink model, beginning with water use inside
the household all the way through wastewater treatment and discharge. The model is a
combination of streams and technologies, in which one can chose to split or combine streams
or change the composition of the stream by inserting technologies. The choices are either
made by hand to calculate the sustainability indicators for a single system or through the
optimisation procedure to select optimal systems for a given set of weighting factors. Note
that the software implementing this model can be downloaded from the TU/e internet site
(www.tue.nl/bib under Publications TU/e, Full-text Dissertations TU/e, look for my name
“van der Vleuten-Balkema”)
For instance, a particular domestic water system, including water use and wastewater
treatment is specified by choosing the water source one wants to use, whether or not to reuse
27
Sustainable wastewater treatment
3 Model based decision support tool
wastewater, to combine or separate wastewater streams, which transport option one wants to
use, and which treatment technologies are to be used. The models of the unit operations
chosen are coupled to calculate the mass balance, as well as energy use, costs, land use, etc.,
for the total domestic water system.
Figure 6: Superstructure.
3.2.1
Superstructure
To enable the comparison of a large variety of wastewater treatment systems, we want to keep
the number of possibly limiting preconceptions as small as possible. Therefore, we based the
model on a superstructure comprising most relevant water use and wastewater treatment
possibilities (see Figure 6). This structure fulfils the following requirements:
•
•
•
•
•
•
enable comparison of small- and large-scale systems;
enable separate treatment of different wastewater streams;
enable use and reuse different water streams, such as drinking water, greywater (water
lightly polluted by household use), household water (a secondary water quality),
rainwater, and treated wastewater;
include the different products that can be made, such as biogas, fertiliser, and water for
irrigation;
include the toilet and the sewer system for comparison of systems such as vacuum
toilets and compost toilets with conventional water based systems;
include bio-waste that can be treated in the dry sanitation systems such as composting.
The superstructure is constructed by superimposing a large number of known options for
domestic water use, water conservation and reuse, as well as wastewater treatment. As such,
the superstructure is an aggregated structure of a large variety of domestic water systems. In
28
Sustainable wastewater treatment
3 Model based decision support tool
the structure, we distinguish different types of domestic water use based on the water quality
required and the characteristics and destination of the wastewater (toilet flushing, kitchen
water use, personal hygiene, washing clothes, and outdoor water use). Based on the
characteristics of the different wastewater flows we included the option to treat three flows
separately; (1) water from the toilet, so-called blackwater, (2) yellowwater (= urine), and (3)
greywater, more diluted wastewater from the other household activities.
The superstructure shows the possible mass streams connecting different units. Two different
types of units are defined, namely (1) stream combiners & splitters (triangles), and (3)
processing units (squares).
3.2.1.1 Stream combiners and stream splitters
In our static model, most stream combiners in the superstructure are virtual ones that do not
physically exist but indicate a choice for a water source or a sequence of different sources
used over time. It is assumed that in combiners all incoming streams are added up to one
homogeneous outgoing stream (ideally mixed, no reactions). Like the combiners, splitters
indicate that different choices lead to different directions of flows. For instance directing
drinking water to toilet flushing and to the washing machine (two options of splitter d in
Figure 6). However, it can also mean that some of the water is consumed while the rest is
poured into the sink (splitter k2 in Figure 6). The outgoing streams of stream splitters have
always the same composition as the incoming stream (no separation of solids or chemical
reactions take place).
3.2.1.2 Stream definition
Streams are defined by their respective volumetric and mass flow rate (l/d and g/d) of the
most relevant components indicating possible harmful emissions or possible products.
Selected components are TS, TSS, BOD, Total N, Total P, heavy metals (Cu, Zn), faecal
coliforms (FC), and H2O. Due to lack of data it was not possible to quantify all components
we selected in the first stage, namely NH3-N,
technology choice
Total K, Ca, Mg, Cd, Pb, N2 / NOx, and the
Gas
gases CO2, CH4, H2O, and NH3, had to be left
Membrane
out (see paragraph 5.7).
Influent
3.2.1.3 Processing units
Precipitation
Effluent
Processing units contain technological unit
Sedimentation
operations obtained through segregation of
wastewater treatment systems. In processing size, costs,
units
separation
and/or
material energy use,
Recycle
Sludge
transformation takes place. These blocks are products,
stream
indicators
therefore defined by state information
Figure 7: Processing unit (bw1).
(temperature, components, etc.) and by
information about the nature of the block
(chemical reactions, physical separation, etc.). The technological unit operations are grouped
in the processing units. As such, the processing units are super-blocks containing models of
different unit operations that can be applied to fulfil the function of the processing unit.
Processing units can be found in the household, for instance the toilet, the rainwater system,
and the disinfection unit. Outside the household there are processing unit for wastewater
transport and treatment. There is an on-site treatment unit to allow small-scale wastewater
treatment before transport. Then, for blackwater treatment there are 4 treatment units and a
29
Sustainable wastewater treatment
3 Model based decision support tool
sludge treatment unit, while for greywater there are 2 treatment units and for yellowwater
only 1. For instance, the processing ‘blackwater treatment 1’ (bw1) contains three models,
namely membrane filtration, precipitation, and sedimentation. One of these technologies is
chosen to treat the incoming wastewater. It is also possible to choose a blank option meaning
that no treatment takes place in bw1 (see Figure 7).
Note that technologies can have a constraint with respect to the incoming wastewater, for
instance the concentration of solids in the influent of constructed wetland should be limited to
prevent clogging. If the concentration in the incoming stream is too high another type of
wetland is chosen (wwtconstraint(2,10)=360 mgTS/l see Appendix 1). To indicate that the
model overruled a choice made by the user or the optimisation procedure the model sets a
flag. When showing the results, the original choices and the choices corrected for these flags7
are shown.
The models used for the unit operations contained in the processing units are static black box
models based on data from literature (see Chapter 5). The models do not scale the
technologies, but different models are inserted for the same technology on different scales.
For instance, if the population is 100 persons, one can chose either the technology model for
100 persons or 20 times the 5 persons model for the same technology. However, transport and
treatment are coupled, if for instance the option “no transport” is chosen, the treatment is
restricted to small scale. If one chooses no transport in combination with large scale treatment
the model will overrule this by changing the transport into combined or separated sewer (the
rule is: if scale <= maxpphh (=maximum persons per household) than cBWT=1 (combined
sewer for blackwater) and if it concerns greywater cGWT=1 (separate sewer for greywater,
see also Appendix 1). Furthermore, the transport distance for any chosen transport is defined
by the scale of treatment chosen. Enlarging the scale of treatment automatically increases the
transport distance.
Instantiation of processing units and consecutive optimisation yields an instance of a
suggested wastewater treatment system, whereby whole sections of the superstructure are
eliminated.
3.2.2
Model implementation
All calculations are done in the Matlab-Simulink model called “dws2003”. There are 2
versions of the model, one to run with the optimisation routine and one to runs manual in
which the decision variables, normalisation and weighting factors have to be set by hand. The
mathematical description is given in Figure 8 on page 34. This paragraph provides a general
description of the model.
The first step in the calculation, in the block ‘Water demand & supply’, is the quantification
of the water demand and the amount supplied by the different types of water (drinking water
household water, and rainwater) to the different domestic uses (toilet, kitchen, personal
hygiene, washing, and outdoor water use). The second step is the quantification of the mass
balance. The blocks in the Matlab-Simulink model used for this purpose are similar to the
processing units in the superstructure. These are the water use, wastewater transport, and
wastewater treatment blocks. To verify this calculation step an overview is given in the blocks
‘Mass balance’ and ‘Water balance’.
7
Another reason to overrule a choice is when the incoming stream is empty, this is also indicated with a flag – read more
about this in Appendix 2.
30
Sustainable wastewater treatment
3 Model based decision support tool
The next step is the quantification of the sustainability indicators. The block ‘Indicators 1’8
quantifies the sustainability indicators that are based on the technology choices and incoming
streams, while the block ‘Indicators 2: Reuse or waste’9 quantifies the indicators that are
based on the quality of effluent and sludge that are produced. In the last set-up of the
calculations, the quantified sustainability indicators are normalised, weighted and summed up
to form the objective. This is done in the blocks ‘Normalisation & Weighting’ and
‘Objective’. The details on quantification and use of the sustainability indicators are given in
de next paragraph.
3.3 Sustainability indicators
The model of the domestic water system quantifies all outgoing streams, energy use, costs,
land area requirements, and summarises the social-cultural aspects of the chosen
configuration. Some of the model results are directly used as sustainability indicator, while
others are first converted or categorised. What is for instance the reuse potential of the
different outgoing streams? The categorisation of the outgoing streams, defining reuse
potential and waste, is based on restrictions for reuse and discharge that are described in detail
in Chapter 4. An overview of how different sustainability indicators quantified is given in
Table 3. The use of the indicators in optimisation is described in the next paragraph. But first,
we want to explain how we quantify the “qualitative” sustainability indicators.
The use of qualitative sustainability indicators:
Some of the indicators are not quantified but indicated qualitatively, for instance “Social
acceptance” and “Robustness”. These are used to indicate a potential advantage or
disadvantage of a certain technology. These indicators are labelled either 1 for a potential
advantage, 0 for neutral, or –1 for a potential disadvantage. The model calculates two sums,
namely the sum of all potential advantages and separate, the sum of all potential
disadvantages. One could add those two sums, however, it is not always true that a potential
disadvantage is made up for by a potential advantage. That is why the model keeps those
sums separated. These indicators are normalised by dividing them by the number of
technology choices contained in the domestic water system chosen (see also paragraph 3.4.3).
The procedure for weighting is no different from the weighting of other indicators.
3.4 Optimisation
3.4.1
Integer optimisation
We defined the selection of optimal systems as an integer problem. To quantify the mass
balances the model uses 24 decision variables. Four of these variables are used for water use
and conservation. These contain yes or no decisions for instance: Do you want to use
rainwater for toilet flushing, the washing machine, and outdoor water uses? If so x2 =1 if not
x2=0. The next 16 decision variables contain technology choices in an integer. For instance,
the first step of blackwater treatment is defined in the integer x13 also called ‘bw1’ that has a
lower bound of 1 and upper bound of 4. This decision variable contains the following choices:
Block “Indicators 1” quantifies: water use, costs, energy use, space occupation, acceptability, adaptability, expertise,
quality of space, institutional requirements, maintenance, participation, reliability, robustness, and stimulation of sustainable
behaviour
9
Block “Indicators 2: Reuse or Waste” quantifies: landfill, soil conditioning, fertiliser, irrigation, domestic reuse, discharge,
infiltration, waste, cso, and bio-gas.
8
31
Sustainable wastewater treatment
3 Model based decision support tool
(1) membrane filtration, (2) sedimentation, (3) precipitation, and (4) no treatment in bw1.
When x13=2, the black box model for sedimentation will be used to quantify the
sustainability indicators for the first step in blackwater treatment. The remaining 4 decision
variables define the direction of streams. One of these is x21 called“SplitGW1” that can be 0
or 1 directing the greywater either to greywater treatment or to on-site treatment where it is
mixed with blackwater.
These 24 decision variables and their lower and upper bounds are the input variables for
optimisation, together with the model and the input data discussed earlier, these quantify the
sustainability indicators that we want to optimise.
The general mathematical notation of our optimisation problem;
min
x
F ( x) = ∑
SI ( x) * weightsi
normalisationsi
s/t
xL ≤ x ≤ xU
x∈ I
In which:
x is the vector of the 24 decision variables that are all integers,
xL and xU are the lower bounds and upper bounds for x,
F(x) is the function to be minimised, which is the weighted and normalised
sum of the sustainability indicators.
SI(x) are the 37 sustainability indicators quantified by the model of the
domestic water system.
For a refined mathematical description of our optimisation problem see Figure 8 on page 34.
The question that rises now is: how can we optimise using indicators so different as waste,
land area and participation? This can be done through multi-objective optimisation as
discussed in the next paragraph.
3.4.2
Multi-objective optimisation
Essential for the sustainability assessment is that the indicators are not judged separately. The
judgment should balance the different objectives of sustainability. In our research, the
different objectives are for instance, minimal costs, maximal production of clean water and
fertiliser, and maximal end-user participation. Some of these objectives are conflicting: after
all it is not always possible to design a wastewater treatment system that minimises cost while
maximising performance. Therefore, one aims to find a set of Pareto-optimal solutions. These
are solutions where one objective can be improved only at the expense of others, thus
indicating the points where trade-offs between the different objectives start. In our
sustainability assessment, the objective function is therefore weighted sum of the
sustainability indicators (see also Figure 8 on page 34). How normalisation and weighting
factors are used is explained in the next paragraph.
3.4.3
Normalization and Weighting
Although many researchers try to avoid normalisation and weighting, it is a necessity because
without normalisation and weighting the analysis is not completed. Results that are not
32
Sustainable wastewater treatment
3 Model based decision support tool
normalised and weighted cannot be compared. When presented these results, ‘unaware’
readers will be ‘invited’ to make a ‘meaningless’ comparison.
The aim of normalisation is to allow unbiased addition of the different indicators into a single
objective for optimisation. For this purpose, all quantified indicators are scaled between 0 and
1, by dividing the indicators by the difference between the highest and lowest value possible
for this indicator. This range is calculated with the decision support tool for the indicators
costs, energy use, and space occupation. The qualitative indicators are normalised by dividing
them by the number of technologies that are chosen in the processing units, as each
technology can add an advantage or a disadvantage10.
There are different ways to determine normalisation factors, some normalisation factors can
be easily calculated on the basis of the data in spreadsheet (for instance the factor for water
use), while others can only be determined thought optimisation as these depend on the
solution space. We chose to use optimisation to determine the values for the normalisation
factors. By setting all weighting factors to zero except for 1 sustainability indicator, one can
find its maximum or minimum value. The normalisation factor we use is the difference
between the maximum and minimum value. The normalisation factors for different cases are
shown in the overview of the results in Appendix 4.
Weighting factors may be zero such that the indicator is left out of the optimisation, as it has
no influence on the objective. A high positive value for a weighting factor has a
correspondingly positive effect on the objective, for instance a high weight for “Participation
(advantage)” favours all technologies that do increase the participation of the end-user as this
increases value of the variable “objective”. While a large negative value for a weighting
factor expresses a negative influence on the objective, for instance negative weight for “Total
water use” favours values of decision variables that decrease the water use as this minimises
the value of the variable “objective”. Note that in optimisation we minimise: f=–1 * the
variable “objective”. This choice is made as the solvers are designed to minimise while we
wanted to set the weighting factors the way we did as this seem logical, a high positive weight
for the indicator one values and wants maximise.
Once all the sustainability indicators are quantified, normalised, and weighed, the objective
function for the optimisation is formed simply through addition.
3.4.4
Solvers used
To solve our optimisation problem we used 3 different solvers: (1) gclSolve, (2) mipSolve,
and (3) the genetic algorithm gaminI. Note that the software for the optimisation with the
genetic algorithm can be downloaded from the TU/e internet site (www.tue.nl/bib under
Publications TU/e, Full-text Dissertations TU/e, look for “van der Vleuten-Balkema”). The
other solver, the global solver and the mixed-integer solver, fall under the licence of Tomlab.
(1) Global solver: gclSolve
The first solver, gclSolve is a global stand-alone solver that was available from
the Tomlab Internet site (Tomlab 2000). This is a global solver and has therefore
the advantage that one can be sure that the optimum found is not a local optimum.
10
Note: it is possible that none of the technologies that are contained in a processing unit adds an advantage or disadvantage.
This means that the maximum value of this indicator is lower than the number of technologies chosen. Therefore, one could
chose to normalise differently by defining normalisation factors in the spreadsheet with input data.
33
etc.
Cost (1,1) ..

..


tsi ( x) =  ..
..
..

 ..

..
Sust
.
Behaviour
(13, 35 ) 

population = p
water use = wu
weighting = α
normalisation = η
%TS(1,1) ..

..


tr ( x) =  ..
..
..

 ..

..
%
H
2
O
(
10
,
35
)


sustainability indicators :
 TS1 
nˆ in =  M 
 H 2O10 
removal rates :
Data from spreadsheet:
Sustainable wastewater treatment
i=1
else
Sustainability
Indicators:
3 Model based decision support tool
Most sustainable
domestic water
systems for given
data set.
Solution:
nˆ out ( x) < restriction
( x) * tsi i ( x) )
Figure 8: Mathematical
34 description model and optimisation.
nˆ out ,a
 p

=  ∑ nˆ in , j 
 j =1

In each mixer:
 p

nˆ out ,a ( x) = xsplitterchoice *  ∑ nˆ in , j 
 j =1

 p

nˆ out ,b ( x) =  ∑ nˆ in , j  − nˆ out , a ( x)
 j =1

In each splitter:
p
if
out , i
SI j ( x) = 0


nˆ out , k ( x) = xtech.choice * tr ( x) *  ∑ nˆ in , j 

 j =1
In each processing
unit:
∑ (nˆ
i =1to 36
SI j ( x) = nˆ out
Model of domestic water system:
xtech.choice = technology to be used
xsplitter.choice = direction of stream
SI i ( x) =
αi > 0,η > 0,i = {1,2,...,27}
C = {x _ L < x < x _U; b _ L < A(x) < b _U}
x∈C
minF(x) = ∑αi *ηi *SIi (x)
Weighted27sum of sustainability factors.
Objective function:
Sustainable Wastewater Treatment
3 Model based decision support tool
The search strategy of this solver is based on the “Direct algorithm” described by
Jones 1993. This strategy is based on dividing the solution space into rectangles
and defining a set op potential optimum rectangles in each evaluation. However, it
seems that due to the discrete changes in sustainability it loses itself into extensive
search as this is the only way to be sure that the global optimum is found. Due to
the large number of domestic water systems that is contained in the super
structure, namely 7*1012, the calculation time is quite extensive, namely 1 to 2
seconds for a single evaluation on a regular personal computer (662MHz) thus
about 3.6*105 years for the whole set of domestic water systems. To limit the
calculation time, we explored 2 options, (1) decreasing the number of possible
solutions, and (2) using another solver. To decrease the number of evaluations, a
procedure is written to chose technologies that one wants to consider in
optimisation, thus creating a smaller solution space from which optimal systems
can be selected by gclSolve (see Appendix 2 – files gcl2003small.m and
in2003.m).
(2) Mixed-integer solver: mipSolve
A solver with another search strategy we used is the mixed-integer11 solver,
mipSolve that runs under Tomlab (Tomlab 2000). This solver uses the standard
techniques: Branch-and-Bound and Cutplane (see Adfjiman 1998, Biegler 1997,
and Drakos 1996). To check whether the found optimum is a local one, multiple
runs have to be made starting from different points in the solution space.
Therefore, the calculation time depends on the number of runs one decides to
make. Note that one is in principle never sure that the optimum found with this
solver is the global as the starting points are generated randomly without checking
earlier starting points. Similar as with the global solver, we included the option to
use the solver with a smaller solution space (see Appendix 2 – files
mip2003small.m and in2003.m).
(3) Genetic algorithm: gaminI
The search strategy of genetic algorithms is based on the concept of the evolution
of species. The population of the species is in our case a set different domestic
water systems and the score on the objective defines the individual fitness. The
fittest individuals mate to produce the next generations. Or in other words, the
solver combines the inputs that generate the best solutions into new inputs to
calculate the objective values for a new generation. From time to time mutations
are inserted and the best sofar is reinserted. The search for an optimum stops when
the maximum number of generations is reached or when no improvement is made
during a defined number of generations. As such there is no guarantee that the
optimum found is the global optimum, the optimum is “the best so far”.
The GA-solver used for the selection of optimal domestic water systems is an
adapted version of gamin.m, which is available on the Internet as freeware (Gordy
2001). The algorithm is described by Dorsey R.E. and Mayer W.J. (1995). The
most important adaptation to the solver we made is the use of integers as input
variables. To reduce calculation time, we also made a problem definition that
11
At first we defined our problem as mixed-integer optimisation and selected this solver. The problem is now
defined as integer problem but the same this solver can be used.
35
Sustainable Wastewater Treatment
3 Model based decision support tool
makes it possible to use a smaller set of decision variables (define your set of
decision variables with in2003.m and use ga2003small.m for the optimisation –
see Appendix 2).
All solvers save the results automatically an overview of the results can be generated by
running the following m-file: dws2003out, SortResults, and moreplots. For more detailed
information on the different files used to run the optimisation see Appendix 2.
3.5 Output of decision support system
The optimisation selects the choices for combiners, splitters, and processing units that
minimize the objective function. In this way, optimal combinations of technologies forming a
complete domestic water system can be selected for a particular situation. The output
provided lists the five best systems in the dataset set defined, and gives a detailed overview of
the sustainability indicators and the mass balance. Furthermore, one can save and plot the data
to get an overview of how the value of the objective changes with the different choices made.
For details on the output see Appendix 4.
A sensitivity analysis is preformed by varying the inputs, the solution space, and the
weighting factors. This provides insight in the sensitivity of the solutions and the trade-offs
made by choosing different combinations of technology characteristics, different
technologies, or different weights on the sustainability indicators.
References
Adjiman C.S., Schweiger C.A., and Floudas C.A. (1998). Mixed-integer Nonlinear Optimisation in Process
synthesis, in Handbook of Combinatorial Optimisation, Edited by DU, D-Z and Pardalos PM, 1998, Kluwer
Academic Publishers (downloaded from internet http://titan.princeton.edu/bib.html ).
Balkema A.J. (2001). Characterising wastewater treatment technologies, exploring the essence of technologies
to quantify sustainability indicators, Internal report, Technical University Eindhoven, April 2001
(unpublished but digital version available from the author).
Biegler L.T., Grossmann I.E., Westerberg A.W., (1997). Systematic methods of chemical process design,
Prentice Hall PTR, New Jersey, ISBN 0-13-49422-3.
Dorsey R.E. and Mayer W.J. (1995). Genetic Algorithms for estimation problems with multiple optima,
nondifferentiability and other irregularities, Journal of business & economic statistics, vol. 13, no.1, pp.5366, ISSN 07350015.
Drakos N. (1996). Tutorial Optimisation, version 96.1, Computer Based Learning Unit, university of Leeds,
version 96.1, downloaded 19-4-99 from Internet site: http://matgsia.cmu.edu/orclass/integer/node13.html .
Gordy M. (2001), adapted by A.J. Balkema (2002), a genetic algorithm implemented in Matlab available on the
Internet: http://econpapers.hhs.se/software/codmatlab/ga.htm.
Holström K. (1999). TOMLAB v2 Usersguide, September 20th 1999, on Internet at www.ima.mdh.se/tom .
Jones D.R., Perttunen C.D., and Stuckman B.E. (1993). Lipschitzian optimisation without the Lipschitz constant,
Journal of Optimization Theory and Applications, Vol.79, No.1, October 1993.
Schweiger C.A. and Floudas C.A. (1998). Process synthesis, design, and optimal control: a mixed-integer
optimal control framework, in the Proceedings of DYCOPS-5 on Dynamics and control of process systems,
pp 189-194, downloaded from internet http://titan.princeton.edu/bib.html
Tomlab (2000), developed at Mälardalen University in Sweden, Tomlab versions 1 to 2.2 were available as
freeware, Tomlab 3 became a commercial product, see http://www.ima.mdh.se/tom/tom-software/tomlab.htm
or www.tomlab.biz.
36
Sustainable wastewater treatment
4: Streams: quantifying inputs & categorising outputs
4 Streams: quantifying inputs & categorising outputs
Data required as input for the decision support tool is described in two parts, stream
definition described in this chapter and technology characterisation described in Chapter 5.
In this chapter on stream definition we quantify the incoming streams (drinking water,
household water, rainwater, bio-waste, faeces, urine, and contaminants) and the associated
sustainability indicators such as costs, energy use, space requirements, etc. Furthermore, we
determine restrictions to indicate the reuse potential of the outgoing streams. Hereto, we first
define the objectives of water services in paragraph 4.1, and then describe in 4.2 water supply
and in 4.3 wastewater, and in 4.4 the major threats defining the constrains for water services
and in 4.5 the resources in wastewater that define the opportunities for the water services.
4.1 Water services
Urban water systems are to maintain a healthy living environment through supplying safe
drinking water, providing hygienic sanitation, and protecting against flooding and surface
water pollution.
Focussing on domestic water systems, we find that water is essential in many of our daily
activities. It is the major constituent in our drinks, important in food preparation, and essential
in personal hygiene. It fulfils many functions; in the morning, you may use cold water to
wake up, it may refresh you after sporting, warm you up when feeling cold, and relax you
when being stressed. Furthermore, water is not only used for washing oneself, but also for
clothes, cars, and when flushing the toilet. In our country, water is so plentiful, so common,
that it is almost used unnoticed. Some people may even not be aware of its origin, the effort in
treatment, supply, use, collection, treatment, and the effects of wastewater discharge. While in
other societies water is very scarce, it may take hours walking to fetch 10 or 20 litres water.
Still, the functions drinking, cooking, washing and cleaning, as well as sanitation are fulfilled
one way or the other. These functions are the essence of the domestic water systems in the
decision support tool and therefore described and quantified in this chapter.
4.2 Water supply
Water can be drawn from the following different sources: groundwater, surface water,
rainwater, and wastewater. Groundwater has the advantage to be in most cases naturally
purified by the soil’s ecosystem and is therefore often of good quality. However, in some
cases there are groundwater quality problems due to high natural concentrations of arsenic12,
iron, fluoride13, nitrate, or extreme salinity. Furthermore, groundwater quality is threatened
through contamination with pathogens from domestic sanitation and life stock, and chemicals
from industry and agriculture such as fertiliser, pesticides, and heavy metals. The fact that
withdrawal often exceeds recharge is another problem with groundwater as source for water
supply. Surface water, if available, is often recharged quicker than groundwater. However, the
quality is usually less, thus requiring better treatment. Rainwater, if available in sufficient
quantities, can also be a sustainable water source. The quality of rainwater depends on the
12
The natural contamination of groundwater with arsenic in Bangladesh is the largest poisoning of a population in history
with millions of people exposed (Smith 2000).
13
Fluoride in groundwater affected nearly 100,000 villagers in the remote Karbi Anglong district in the north-eastern state of
Assam, many have been crippled for life. Fluoride levels in the area vary from 5-23 mg/l while the permissible limit is
1.2 mg/l [Times of India / UNI, 2 June 2000].
37
Sustainable wastewater treatment
4: Streams: quantifying inputs & categorising outputs
local air pollution as well as on the method of collection and storage. In cases where surface
water and rainwater are not available treated wastewater may serve as a water source.
The treatment of the water depends on the source as well as the intended use. There are a
number of standard treatment techniques such as micro-filtration, precipitation, floatation,
active carbon, and sand filtration. Additional disinfection can be carried out with chlorine,
ozone, UV, or membrane filtration, or through heating. Some constituents, for instance certain
pathogens (as for instance Cryptosporidium and Giardia) and pesticides, are difficult to
remove.
Water standards are set to safeguard drinking water quality. People need approximately 2
litres drinking water per capita for drinking and cooking per day. For other usage, such as
bathing and cleaning, the water quality can be slightly lower, for instance as indicated by the
European swimming water standards. If the contact with the water is limited, as for instance
in washing clothes with the washing machine or flushing the toilet, the water quality may
even be less. For these purposes alternative water sources as for instance lightly treated
surface water, treated greywater, or stored rainwater can be used. The main question is
whether this implies taking some health risks. Research by the governmental institute for
health and environment (Medema et al. 1994, 1996, and Versteegh 1997), indicate that there
is a potential risk especially when using alternative water sources for the shower, bath, garden
tap, and even for wash machine and toilet flushing, as there is a risk of aerosol infection. The
cited reports are based on limited literature resources, thus additional research is needed in
order to come to the definition of an appropriate water quality standard. At the same time,
rainwater use is accepted in Germany, for instance, while greywater reuse is gaining
acceptance in countries such as Japan, Australia, USA, Canada, UK, Germany, and Sweden
(see paragraph 5.3).
4.2.1
Domestic water use:
In the Netherlands domestic water use is on
average 130 litres drinking water per person
per day (47 m3 per person per year, see
Figure 9). This is about average in Europe,
but low compared to the USA were people
use on average 265 (l/(cap*d))(Cites et al.
1998). On the other hand, 130 litres is high
compared to many developing countries, in
Tanzania for instance domestic water use in
on average 10 l/cap/d (WRI 2000, p.277).
According to the United Nations, 50 l/cap/d
is minimum required for consumption, and
personal hygiene, independent of climate,
technology, and culture (see also footnote 15
on page 42).
Figure 9: Domestic water use in the Netherlands
categorized by function (NIPO 1999).
In the Netherlands, domestic water use slightly decreased in 1992 the average was 135
(l/(cap*d), in 1995 this was 134 (l/(cap*d)), and in 1998 128 (l/(cap*d)) (NIPO 1992, 1995,
1999). This decrease is due to the introduction of water saving measures as water saving
showerheads, smaller flush water reservoirs with flush interrupters, and water saving washing
38
Sustainable wastewater treatment
4: Streams: quantifying inputs & categorising outputs
machines. Several studies analyse domestic water use in the Netherlands and identify options
for water conservation. The general conclusions are that water conservation measures, such as
water saving toilets and showers, can reduce water consumption by 20% (Ewijk et al. 1998,
Geldof et al. 1997, 1997b, Haffmans et al. 1995, Kilian et al. 1996, Langeveld 1997). When
rainwater or treated greywater is used in addition, the total reduction in drinking water
consumption is about 60 to 80% (Ewijk et al. 1998, Geldof et al. 1997, 1997b, Herrmann et
al. 1997, Kilian et al. 1996, Langeveld 1997). Water conservation, rainwater and greywater
systems are discussed in detail in Chapter 5.
4.2.2
Economics of water supply:
Although, there is a trend in
privatisation, most water companies
are still governmental institutions,
primarily because health is a public
responsibility but also due to the
nature of investments, namely the high
cost of the necessary infrastructure.
This is also reflected in the public
investments dominating in the water Figure 10: Investment in water world-wide (Additional
sector (see Figure 10). As result of a
investment is an estimated amount required to counter
number of reasons, the prices for water
the water crisis, source: Cosgrove et al. 2000).
supply and wastewater management
may not reflect the actual costs, or the actual costs are not transparent to the users. Reasons
for this may be: subsidizing water, no metering of water use, no inclusion of environmental
costs, billing together with electricity, many of illegal connections, loss through leakage, etc.
Table 4: Costs of piped water supply.
Costs for treatment and supply:
Household water IJburg, Treatment
0.58
(Euro/m3) GWA 1997, p.50, and p.53)
Amsterdam
Supply
0.59
Drinking water
Source: groundwater
0.17
(Euro/m3), excl. groundwater tax of 0.15,
Source: surface water
0.30
Dufour 1998, p.146
Variable rate paid by Dutch consumers*:
Water Eindhoven and
Drinking water
1.11
(Euro/m3), incl. 0.20 ecotax and 0.13
Meerhoven
Household water
0.88
Euro/m3 groundwater tax, WOB 2001
Water supply IJburg
Drinking water
1.17**
(Euro/m3), GWA 1997, p.VII
Amsterdam
Household water
1.03**
Estimated price elasticity Drinking water
0.1 – 0.2 Noorman 1998 p193, Haffmans 1995 p8
Average costs (with fixed rate incorporated) paid by consumer:
Brussels
Drinking water
1.4
(Euro/m3), Dufour 1998, p.148, based on
Amsterdam
0.9
200 m3/year.
Washington
0.4
Rome
0.3
* Note: In the Netherlands the drinking water prices vary per region (0.77 to 1.57 Euro/m3), similar the fixed rate that is charged in
addition (12 to 66 Euro/(household*year)). An average Dutch household (2.5 person, 125 m3/year), pays between 111 to 319 Euro/year
for drinking water supply. Charges for wastewater treatment are often based on population equivalent, although the system is changing
to couple wastewater fees to water use. These charges also vary per region, in Eindhoven a household with 2 or more inhabitants, pays
218 Euro/year (WOB 1998).
** Note that it was apolitical decision to make household water cheaper and drinking water more expensive, these prices are totally
reflecting the production cost of the product. Household water has to be cheaper as it is a lesser product, and drinking water becomes
more expensive to make up for the loss and as less is sold while production capacity is available.
Original data in Dutch Guilders; 1 Euro = 2.20317 NLG.
39
Sustainable wastewater treatment
4: Streams: quantifying inputs & categorising outputs
The World Bank estimated that governments recover about one third of the cost of providing
drinking water services in developing countries. Often the relatively rich part of the
population living in the better neighbourhoods of the cities, are connected to the subsidized
piped water supply and sewerage. Whilst as the poorer part of the population mostly depends
on commercially sold water, typically 2 to 3$ per m3, that is 12 times higher on average than
the price of the subsidized municipal water (Moor et al. 1997, p.16). Table 4 gives an
overview of the costs of water supply.
Environmental impact assessment:
Besides water withdrawal, energy use is seen as one of the major environmental impacts of
drinking water supply. Reported figures on energy use14 are 0.7 kWh/m3 (electricity use
mainly pumping, Tarantini and Ferri 1998, Italy), 0.77 kWh/m3 (Bengtsson et al. 1997, p.20,
Sweden), and 0.13 to 0.22 kWh/m3 for treatment and 0.06 kWh/(m3*km) for transport
(VROM 1992, the Netherlands), and 2.2 ± 0.4 kWh/m3 for drinking water production and
supply using groundwater and 2.8 ± 1.4 kWh/m3 when using surface water (Ewijk et al. 1998,
p.25, the Netherlands).
Land requirement for drinking water production from surface water is estimated to be 0.01 to
0.03 m2/litre for the storage basins, 0.45 m2/litre for transport, and 0.0025 m2/litre for drinking
water treatment (VROM 1992). The land area for storage basins obviously depends on the
depth of the basins. For transport, the space occupation is mostly underground, meaning that
the land area can be used for other purposes with some limitations.
Table 5: Comparison of different supply systems for IJburg in Amsterdam (Hoek 1999, GWA 1997)
Scenario:
Description:
Reference
Rainwater
Drinking water supply.
Rainwater for the toilet substitutes 16% of
drinking water, 1 system per 4 households.
1 wetland per 24 houses for greywater
treatment supplying to toilet flushing,
substituting 21% of the drinking water.
Water from the IJ-lake treated by inline
coagulation, pellet softening, and UV
disinfection is used to flush the toilet and
supply the washing machine, substituting
37% of the drinking water.
Brackish groundwater is treated with
reverse osmosis and also substitutes 37%
of the drinking water.
Greywater
IJ-lake water
Brackish
groundwater
Costs:
(Euro/m3)
1.3
7.1
6.7
1.2
1. 1
Environmental impact*:
Only better score than reference
scenario on ozone depletion.
Only better score than reference
scenario on ozone depletion.
Better than reference scenario on
exhaustion of fuel, greenhouse
effect, acidification, eutrophication, smog forming, human
toxicity, and energy consumption.
Worse than reference scenario on
all points, especially high on
aquatic toxicity**.
* According to LCA methodology the following categories of environmental impacts are incorporated in the analysis:
depletion of resources, depletion of fuels, global warming, acidification, eutrophication, ozone layer depletion, aquatic
eco-toxicity, photochemical air pollution, and humane toxicity.
** The report gives little insight into the results of the LCA. The fact that brackish water scores so low is surprising. Two
treatment schemes for brackish water are proposed one based on ultra-filtration the other on flocculation using FeCl3
followed by filtration. May be the addition of this flocculant is debit to the bad score on aquatic toxicity. Aquatic toxicity
is a difficult category in LCA as it is difficult to weight different toxic effects, not all chemicals are included in the data
base, and in water treatment is not always clear what of the chemical remains in the water and in what form.
14
The figures of Ewijk et al 1998 do include all primary energy used for drinking water supply and distribution excluding
energy use for the production of piping materials used. Bengtsson et al 1997 give separate figures for electricity and fossil
energy and include the production of treatment chemicals as well, the other sources give no further details.
40
Sustainable wastewater treatment
4: Streams: quantifying inputs & categorising outputs
Figure 11: Institutions and their responsibility in the water sector (Note: arrows mean
those institutions fall under the ministry or that ‘upper’ institutions have
shares in the ‘lower’ one or are a branch organisation).
An example of an environmental impact assessment for water supply is given in Table 5. This
example considers the water supply for a new housing area in Amsterdam called IJburg. The
dual water supply system, supplying drinking water and locally treated surface water as
household water, was selected on costs and environmental impacts. Although selected, the
system is never implemented. When it came out that in Leidsche Rijn, a Dutch pilot project
with household water near Utrecht, a family drank household water for more than a year due
to a wrong connection, all other Dutch projects were stopped. Despite the fact that, luckily,
none of the family members' health was affected (NBMZ 2003).
4.2.3
Social- cultural aspects: Institutions for water resource management and water supply
Water demand depends on climate, available technology, culture, and also on personal
behaviour. Therefore, water supply and especially domestic water use have a strong socialcultural aspect.
Dutch water companies, responsible for production and supply of drinking water, are mostly
(partly) owned by provinces and/or municipalities. The provinces issue the licences for
groundwater withdrawal, while water boards issue licences for surface water intake. The
national government is responsible for public health and the environment. Waterworks are
inspected by the Ministry of Welfare, Public Health and Sports. Whilst, the Ministry of
Housing, Spatial Planning and the Environment (VROM) decrees policies and legislation
regarding drinking water supply and wastewater discharge. The Commission Integral Water
41
Sustainable wastewater treatment
4: Streams: quantifying inputs & categorising outputs
management (CIW) coordinates the water management tasks of the different governmental
institutions. Private companies play a role as suppliers of products and equipment and are
restricted and controlled by the standards and certificates issued by governmental institute for
certification and inspection of water appliances (KIWA). Figure 11 shows the institutional
set-up in the Dutch water sector.
Legislation relevant in drinking water supply is; ‘Waterleidingwet’ (Water supply law), ‘Wet
Bodembescherming’ (Soil protection law), and ‘Bouwbesluit’ (Building directive). The Dutch
legislation has to comply with the legislation of the European Union. For instance, the EU
Bathing water quality directive is often used as an interim quality standard for household
water (Directive 76/160/EEC 1975 to be revised in 2001).
The responsibility of the water companies ends at the main tap inside the household. The
water installation inside the house is under the responsibility of the house owner. Projects of
water companies aiming at more sustainable solutions mainly focus on water conservation
and alternative water sources such as surface water and rainwater. The IJburg project in
Amsterdam a good example. Within this project, an inquiry was made to explore the level of
acceptance of a lesser water quality by the end users. It revealed that 97% would accept the
water for toilet flushing, and 80% for the washing machine if clean laundry is guaranteed.
When the lesser quality is cheaper 90% is willing to use it in the washing machine.
Surprisingly, 70% was willing to pay as much or even more for the water than for drinking
water (GWA 1997, p.V).
The experiences with more environmentally friendly solutions in households in the
Netherlands are not always positive. In some cases, the systems are not used as intended or
even replaced. In the enquiry, however, people indicated to accept extra effort for a more
environmentally friendly solution (GWA 1997, p.13).
Water supply in the model based decision support tool:
The minimum qualities, as set by the different water standards, are constraints that must be
met by any water supply. This means that water supplied to the kitchen has to be of drinking
water quality, and water supplied for personal hygiene has to be of bathing water quality.
Another restriction is the minimum amount of water15 needed to maintain a healthy living
environment. However, it is hard to determine what the minimum amount of water is for
personal hygiene because this also depends on the climate, technology available, culture, etc.
In the model, the water demand calculated on the basis of average Dutch water use. The water
demand has to be met completely. The only way to lower water demand is to insert waterconserving technologies (see paragraph 5.2.2 Water conservation). Other parameters such as
costs, space requirements, acceptance, etc., are negotiable (see paragraph 4.5 Resources in
wastewater). Which solution to chose depends on the local situation and the preferences of the
actors.
15
One can also chose to use an absolute minimum supply for instance 50 l/(cap*d), of which 2 l/(cap*d) has to be of drinking
water quality. However, in the Netherlands water is plenty, which does not justify forcing a change in behaviour by limiting
water supply. Furthermore, setting a minimum is not evident, UNHCR and WHO guidelines were in 1960 30 litres per person
a day, in 1970 this was reduced to 20 litres. Regarded, as absolute minimum for survival is 7 litres. However, experiences in
refugee camp showed that 2.5 more diarrhoea occurred when supplying 10 to 15 litres instead of 30 litres (Roberts 1998).
The new standard of 50 litres per capita per day was set as a basic water requirements for drinking, sanitation, bathing and
cooking – in dependent of climate, technology and culture (Gleick 1998, p.44).
42
Sustainable wastewater treatment
4: Streams: quantifying inputs & categorising outputs
Different choices that can be made in the decision support tool concerning water supply are:
•
•
•
•
•
practise water conservation,
supply drinking water or household water to all intended water uses,
supply rainwater to the toilet, washing machine and uses outdoor and/or supply
rainwater to the kitchen and personal hygiene,
disinfection of household water and/or rainwater for use in the kitchen and personal
hygiene
reuse treated wastewater
4.3 Wastewater
Domestic wastewater is a mixture of the wastewater streams resulting from the different water
uses inside the household, with the quality varying accordingly. Generally, the following
streams are distinguished: blackwater (wastewater from the toilet), greywater (all domestic
wastewater excluding toilet wastewater), and rainwater (see also Figure 12).
Blackwater contains the human excreta, brownwater (faeces) and yellowwater (urine). Most
pathogens are contained in brownwater, while most nutrients are contained in yellowwater
(see Table 6). In conventional water-flush toilets these streams are mixed and diluted with
flush water. This results in a relatively large stream contaminated with pathogens (see Figure
12), while in source sanitation the blackwater is separated and low or undiluted, keeping the
pathogens in a relatively small volume. Urine separation toilets collect urine separately, which
leaves the nutrients uncontaminated. In addition, blackwater may contain toxic components
originating from cleaning agents, traces of medicines, and non-biodegradable solid sanitary
waste such as condoms, sanitary towels, tampons, etc.
Figure 12: Different wastewater streams and their approximate amounts
for the Netherlands (based on Winblad et al. 1980).
43
Sustainable wastewater treatment
4: Streams: quantifying inputs & categorising outputs
Table 6: Fertiliser value of faeces and urine (Polprasert 1989, in Bitton 1999, p.173).
Component:
Quantity wet
Quantity dry
BOD5
Moisture content
Total solids (TS)
Organic matter
Nitrogen (N)
Phosphorus (P2O5)
Potassium (K2O)
Carbon (C)
Calcium (CaO)
C/N ratio
(g/(cap*d))
(g/(cap*d))
(g/(cap*d))
(%)
(%)
(% of TS)
(% of TS)
(% of TS)
(% of TS)
(% of TS)
(% of TS)
-
Faeces
100 - 400
30 - 60
15 – 20
70 -85
15 - 30
88 - 97
5.0 – 7.0
3.0 – 5.4
1.0 – 2.5
44 - 55
4.5
6 - 10
Urine
1,000 – 1,310
50 – 70
10
93 –96
4 –7
68 – 85
15 – 19
2.5 – 5.0
3.0 – 4.5
11 – 17
4.5 – 6.0
1
Table 7: Costs of wastewater treatment.
Costs of sewers and wastewater treatment:
Average expenditure in the Public treatment:
35
(Euro/(cap*y))
CBS 1998, p.51
Netherlands
Exploitation:
63 %
Investment treatment: 33 %
Investment sewer:
4 %
Average cost Europe
Capital costs
11 to 33
(Euro/(cap*y))
Bode et al. 2000
Operation costs
32 to 57
(Euro/(cap*y))
Paid by consumer in the Netherlands:
Levy for treatment
Municipality Nuenen
117
(Euro/(Household*y))
Verhoeven 2000,
Levy sewers
0.62
(Euro/m3)
p.10
Cost municipal wastewater treatment, nutrient removal, anaerobic digestion (50,000- 200,000 pe)
Energy
10 kWh/(pe*y)
1.2
(Euro/(pe*y))
Nowak 2000
Chemicals
18 moles /(pe*y)
1.1
(Euro/(pe*y))
Sludge disposal
60 kg/(pe*y)
2.4
(Euro/(pe*y))
Maintenance
0.6% of construction
2.0
(Euro/(pe*y))
Personnel
4.2
(Euro/(pe*y))
Others
15%
1.7
(Euro/(pe*y))
Total
12.5
(Euro/(pe*y))
Economy of scale of centralised treatment:
100,000 to 300,000 p.e.
German data
23
(Euro/(cap*y))
Bode et al. 2000
50,000 to 100,000 p.e.
27
(Euro/(cap*y))
30,000 to 50,000 p.e.
31
(Euro/(cap*y))
Costs of nutrient removal:
Organic matter
Nitrogen
Phosphorus
German data
0.5 to 1
5 to 8
13 to 20
(Euro/kg COD)
(Euro/kg N)
(Euro/kg P)
Bode et al. 2000
German data
160-1,600 (Euro/ton dry solids)
1,050
(Euro/ton dry solids)
1,050 – 2,100
(Euro/ton ds)
Rüdiger 1998
209 – 523
314 – 418
Rüdiger 1998
Cost of sludge disposal:
Landfill
Dewatered
landfill
Incineration
sludge
Costs of nutrient reuse in agriculture:
Liquid sludge
Dewatered sludge
German data
(Euro / ton ds)
(Euro / ton ds)
Note: p.e. = population equivalent, ds = dry solids, all data reported by Bode et al. and Rüdiger were in German
Marks, 1 Euro = 0.95583 DEM.
44
Sustainable wastewater treatment
4: Streams: quantifying inputs & categorising outputs
Greywater is the dilute wastewater stream originating from domestic activities such as
showering, bathing, washing hands, tooth brushing, dishwashing, washing clothes, cleaning,
food preparation, etc. The water contains some organic matter, for instance, food remains,
hairs, and salvia, with pathogens, and inorganic material, such as detergents, sand, and salt.
Besides this, there is the runoff from roofs and other non-porous surfaces. This rainwater may
be contaminated with dirt accumulated on the surfaces originating from human activities (car
repairing), animals, trees, and soluble contaminants from air pollution, and roofing materials.
Heavy metals are the main problem, these originate from roofing materials such as zinc
coated corrugated iron sheets, lead slabs, zinc gutters, copper roofing tiles on monumental
buildings, etc. If the runoff is not drained the stagnant water, especially in tropical countries,
animals may be attracted and it can become a source of diseases (for instance malaria
mosquitoes, rodents, and snails).
4.3.1
Economics of wastewater
In 1996, the exploitation costs of wastewater treatment and discharge in the Netherlands was
approximately 0.7*103 million Euro, of which 28% was used for sludge treatment, 18% for
transport, and 55% for wastewater treatment (CBS 1998, p.49). Income is generated through
levies for the service ‘wastewater transport and treatment’. Little or no income is generated by
selling the products treated water and or sludge. Table 7 gives an overview of the different
cost aspects of wastewater treatment.
4.3.2
Environmental aspects of wastewater treatment
A Life Cycle Assessment comparing small-scale sewage treatment plants by Emmerson et al.
(1995) shows that, depending on the treatment process, environmental impact during
construction and demolition can be significant. While, for the activated sludge plant included
in the research, 95% of the energy was used during operation, for the biological filter systems
studied, using two circular filters with slag media, the energy use during construction and
operation was of the same order of magnitude (Emmerson et al. 1995). The research by
Bengtsson et al. (1997, p.59) indicates that the energy use is dominated by the operational
phase, however, for emissions both construction and operation are significant. The inclusion
of the construction and demolition phase in the decision support tool is difficult. One problem
is that there are different alternatives for construction and demolition. Other problems are that
little is reported in the open literature on material use, and that it is different to quantify
impacts of material use, as small amounts of certain materials may have a large impact.
Therefore, the construction and demolition phase of is not included in the decision support
tool.
General data about energy use16 of wastewater treatment plants are 35 kWh/(cap*y) (mean
Austria 1997, Nowak 2000), and 29 kWh/(cap*y) (on-site electricity use, mean Italy,
Tarantini et al. 1998). Bengtsson et al 1997 (p.20) report energy use for a specific treatment
plant: for conventional treatment: 44.5 kWh/cap/y and for a urine sorting system: 34.1
kWh/cap/y. Space requirements for centralised wastewater treatment in the Netherlands are
estimated by Mels et al. (1999) to be 0.1 m2 per population equivalent.
16
Figures by Bengtson et al 1997 include electricity as well as fossil energy used and take into account
wastewater treatment and transport and the energy used for the production of and transport of the chemicals used
for precipitation.
45
Sustainable wastewater treatment
4: Streams: quantifying inputs & categorising outputs
Table 8: Urban wastewater treatment directive
(91/271/EEC in Gray 1999, p.304).
BOD5
COD
SS
Total P
Total N
Minimum
concentration: (mg/l)
25
125
35
1*
2**
10*
15**
Minimum
reduction: (%)
70 – 90
75
90
80
80
70 - 80
70 – 80
* 10,000 to 100,000 pe, ** > 100,000 pe
4.3.3
Social and cultural aspects: Institutions for wastewater management
In the Netherlands, the municipality is responsible for the sewerage system, while the Water
Board is responsible for wastewater treatment and water management in general. Both
institutions have a democratically elected management. However, especially the turnout at the
elections of Water Boards can be low as the civilians may not always be aware of and/or
concerned with the functioning of the Water Boards (turnout: 10 to 50 %, Boogers 1998).
Policies and legislation are defined by the Ministry of Transport, Public Works and Water
Management (V&W) and the Ministry of Housing, Spatial Planning, and the Environment
(VROM) (see Figure 11 on page 41). Important legislation concerning wastewater
management in the Netherlands incorporates: ‘Wet Verontreiniging Oppervlaktewater’
(surface water pollution), ‘Wet Bodembescherming’ (soil protection), and ‘Meststoffenwet’
(nutrients). The Dutch laws comply with the directives of the European Union, for instance,
the directive on wastewater treatment effluent (see Table 8).
Wastewater in the model based decision support tool
The constraint on the wastewater treatment and discharge in the model is the ability to meet
the effluent standards as prescribed by the European Union and the health guidelines of the
World Health Organisation (see Table 8 and Table 10). The possible choices in the model are:
•
•
•
•
•
•
•
dry sanitation or the use of flush water,
separation or mixing of yellowwater, brownwater, greywater, and rainwater,
on-site treatment or transport of wastewater,
scale of treatment ranging between 5 persons and 100,000 persons.
combined or separate of organic waste and wastewater,
reuse treated wastewater for domestic reuse, irrigation, infiltration, fertiliser, and/or
reuse sludge as soil conditioner,
discharge wastewater and/or use sludge as landfill.
4.4 Threats to water services
There are in fact many threats to water services, for instance toxic compound naturally present
such as arsenic or fluoride, or toxic compounds discharged by industries. Flooding can be
threat as well, and salinity can form a threat especially to land fertility. As the main focus of
this research is domestic wastewater treatment, we limit ourselves to the main threats in
domestic wastewater these are (4.4.1) pathogens that can be a threat to heath, and (4.4.2)
heavy metals being a restriction in nutrient recycling.
46
Sustainable wastewater treatment
4.4.1
4: Streams: quantifying inputs & categorising outputs
Pathogenic organisms
More than 10 million people die each year from cholera, typhoid, dysentery and other
diarrhoeal diseases caused by poor water supply and unhygienic sanitation. This is equivalent
to 70 jumbo jets crashing with total loss of all passengers every day of the year (IRC 1997).
Most of the pathogens causing these diseases originate from human and animal faeces. There
are many different routes that lead from excretion of pathogens to infection. The contact with
the pathogens can be through ingestion, contact with the skin, or in some cases inhalation of
aerosols. To prevent diseases a wide scale of hygiene measures must be taken; supply of safe
drinking water as well as hygienic sanitation, hygienic food storage and preparation, hygiene
in animal keeping, hygienic waste management, and flood protection through storm water and
surface water management. General information on pathogens is summarised in Table 9.
Removal mechanisms for pathogens are: adsorption, chlorination, filtration, ‘natural’ die-off,
ozonation, predation, sedimentation, and solar radiation. Up to 99.9 % reduction of enteric
bacteria can be obtained by storage, depending on temperature (higher temperature results in
higher natural die-off), retention time, availability of food, pH, excretion of antibiotics from
roots of plants, and the other toxics present in the water (Gray 1999, p.272).
Helminths and protozoa are the largest pathogens, and can therefore be filtered out effectively
by the soil (Fourie et al. 1995, Mara 1996). Clay sized particles are small enough to filter out
most of the bacteria. Viruses are too small for even the finest grained clays to be filtered out
by the soil. The main removal process for viruses in soil is adsorption. In raw sewage 50 – 70
% of the coliforms are associated with particles with settling velocity > 0.05 cm/s. This
implies that in primary sedimentation in treatment plants significant removal of enteric
bacteria is achieved.
Sedimentation transfers the pathogens to the solid phase, therefore, sludge must be handled
with care. Stabilisation, desiccation and solar radiation can be used to reduce the pathogens in
the sludge (Reed et al. 1995, p.72, 75, 199).
Conventional treatment removes up to 80-90 % of bacterial pathogens, tertiary treatment can
increase this to > 98 %, and additional disinfection results in > 99.99 % removal. Still there
will be significant numbers of pathogens present in the final effluent (Gray 1999, p.271).
Advanced treatment technologies such as micro- and ultra filtration may achieve >6-log
reduction (99.9999% removal) in comparison with 3-log (99.9 % removal) achieved by welloperated conventional water treatment plants (Butler et al. 1996).
4.4.1.1 Pathogens in the decision support model
Although technologies are available, pathogens are the major threat to water supply. Since
pathogens are mostly invisible, consumers cannot easily distinguish safe water from unsafe
water. In the decision support tool, European and WHO water standards (Mara et al. 1989) are
used as a constraint to control water quality, although in practice it is difficult to protect
people with these standards as many people world-wide still depend on informal water
supply. Furthermore, knowledge and behaviour are important factors in hygiene.
Microbiological guidelines for reuse of wastewater in agriculture are presented in Table 10.
47
Sustainable wastewater treatment
4: Streams: quantifying inputs & categorising outputs
Table 9: Pathogens.
Bacteria
Protozoa
Sizeb:
Multiplyb
Small; 0.3
to 2 µm
multiply
outside
host
Realtively
large
form cysts
that are
very
resistant
Helminths
Typically
> 25 µm
Viruses
Very small
colloidal
particles;
25-350nm
Unable to
multiply
outside
host
Surivald:
(in days at 20 to 30°C)
Water
Soil
<60,
<70,
usual <30 usual
<20
Faecal
coliforms: 2 to 3
~3
months3
months,
Cholerae
~1
montha
<30,
usual <15
Entamoeb
a: 25
daysa
Many
months
<120,
usual <50
Enteric: 3
monthsa
Dosea:
Crops
<30,
usual
<15
ID50*
>104
<70,
usual
<20
<10,
usual
<2
10 -100
Many
months
<60,
usual
<30
<60,
usual
<15
1 -100
<100,
usual
<20
Faecal
coliform
~109,
Cholera
e ~ 108
Enteric:
100
Wastewaterd:
(/100ml)
Bacteria in
faeces: 1012
/gramb,
Faecal
coliforms:
104-109,
Faecal
streptococci:
104-106,
Shigella: 1103,
Samonella:
400-8,000
Entamoeba
histolitica
cysts: 0-10,
Giardia
lambia cysts
50-104
Helminth
eggs: 1- 800
Enteric
viruses: 10050,000
* ID50 = dose leading to clinical effects for 50% of the population.
Literature sources: (a) Aalbers 1999 ID50 from Franceys et al. 1992, (b) Bitton 1999, (c) Fourie 1995, (d) Tiberghien 1999
survival times from USEPA 1992, number of pathogens in wastewater from Stott et al. 1996.
Table 10: Microbiological quality guidelines for wastewater use in
agriculture (Mara et al. 1989).
Reuse conditions:
Exposed
group:
Intestinal
nematodesa:
Fecal
coliforms:
(Arithmetic
mean no. of
eggs per literb):
(geometric
mean no. per
100 mlb)
A
Irrigation of crops
likely to be eaten
uncooked, sports
fields, public parks.
Workers,
consumers
, public.
<= 1
<=1,000b
B
Irrigation of cereal
crops, industrial
crops, fodder crops,
pasture and trees.
Localized irrigation
of crops in category
B if exposure of
workers and public
does not occur.
Workers.
<=1
No standard
recommende
d.
None.
Not
applicable.
Not
applicable.
C
a
Ascaris and Trichuris species and hookworm. b During irrigation period.
48
Wastewater treatment
expected to achieve the
required microbiological
quality:
A series of stabilization
ponds designed to achieve
the microbiological
quantity indicated, or
equivalent treatment.
Retention in stabilization
ponds for 9-10 days or
equivalent helminth and
faecal coliform removal.
Pretreatment as required
by the irrigation
technology, but not less
than primary
sedimentation.
Sustainable wastewater treatment
4.4.2
4: Streams: quantifying inputs & categorising outputs
Heavy metals
Besides pathogens, heavy metals form a major restriction for the reuse of water and nutrient.
In the Netherlands, two thirds of the heavy metals in wastewater originate from materials used
in the water chain; copper pipes, zinc roof tiles, zinc gutters, leaden strips on the roof, etc.
(DTO 1997). Other sources are: industries, households, offices, traffic, building materials,
treatment of wastes, and waste deposits. In conventional wastewater treatment, approximately
20 to 50% of the heavy metals entering the wastewater treatment system are discharged with
the effluent, the rest remains behind in the sludge (DTO 1997).
Although, metals are necessary for the growth of biological life, some are classified as
priority pollutants due to their toxicity. Mechanisms for removal of metals are similar to those
of phosphorus, namely plant uptake, adsorption, complexation, and precipitation
(Tchobanoglous 1991, p.88). Treatment such as precipitation concentrate the metals in sludge,
if the metal content in sludge is too high the sludge can no longer be used in agriculture as
soil conditioner (see Table 11). As most of the metals originate from materials used in the
water system, replacing these materials is therefore an effective pollution prevention measure.
Table 11: Appearance and restriction on selected metals in soil and sludge.
Concentration in…… (ppm) (a,b,d)
Fresh
Waste
Soil:
Sewage
water:
Water:
sludge:
Sewage sludge directive(c)
Sludge:
Soil:
Applimg/kg
mg/kg
cation:
kg/ha/y
40
3
0.15
1.25d
1750
140
12
75d
25
1.5
0.1
0.75d
400
75
3
30d
1200
300
15
100d
4000
300
30
300d
Cadmium
< 0.001
0.005
0.35
3.8
Cd
Copper
0.0010.2
30
424
Cu
0.010
Mercury
2*10-51*10-6
1*10-5 –
1.9
-4
Hg
1*10
(e)
5*10-4
Nickel
0.001
4*10-6
50
36
Ni
(e)
Lead
0.001 –
0.09
0.002 –
206
Pb
0.010
0.200
Zinc
0.010
0.51
30 – 1000
1094
Zn
Remarks:
Cadmiun: Carcinogenic and teratogenic. Used as anti-corrosion, in batteries, and present in tobacco.
Byproduct of zinc mining. Strongly scavenged by marine organisms.
Copper: Essential trace element for all species, component of several metaloenzymes and other proteins.
Toxic to many micro-organisms and used as fungicide, for water pipes, and sometimes used as
roofing material.
Mercury: Natural concentrations of mercury are low. Hg is extremely toxic. It is used in thermometers,
dental amalgams, electronics, as fungicide, and catalyst.
Nickel: Nickel is probably the seventh most abundant element on Earth. It is an essential trace element,
also for anaerobic bacteria. It is fairly toxic to many plant species and may enhance cancer in
humans
Lead: Atmospheric concentrations in urban areas can be high due to use of lead compounds in petrol,
smelting, and refuse incineration.
Zinc: Essential trace element for life. Moderate abundant. Used in anti-corrosion, roof cladding,
batteries, and some specialised alloys.
Sources: (a) Cox 1995, (b) Emsley 1998, (c) 86/278/EEC also in Gray 1999 pp.516, (d) NVA 1994 p.9,
(e) average brownwater concentration in excel database.
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Sustainable wastewater treatment
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4.4.2.1 Metals in the decision support tool
Copper and zinc are selected for inclusion in the decision support tool as those metals appear
in the water system, in piping and roofing materials. Lead and cadmium are also interesting
due to their high level of toxicity, however, little data on fate of these metals in wastewater
treatment is available. Although very toxic, mercury is left out as its concentration in
wastewater is very low. The effluent and sludge restrictions by European Union are
incorporated in the decision support tool (see Table 13 on page 56). Note that we chose to
overrule some of the restriction namely the copper to phosphorus ratio, this we increased from
60 to 70 mg Cu/kg P, and the restriction for compost and landfill respectively from 60 to 90
mg Cu/kg TS and from 75 to 100 mg Cu/kg TS, for Zn from 200 to 300 mg Zn/kg TS and
from 300 to 350 mg Zn/kg TS. We made this choice since in our opinion the restrictions are
too tight if reuse of natural resources such as urine and uncontaminated17 and treated
blackwater or blackwater sludge is not allowed.
The metal restriction for domestic reuse, irrigation, and infiltration, are also limiting,
however, if one assumes no metal input from piping and roof materials, which can be
established in new houses by using plastic pipes and gutters, one can use rainwater and treated
greywater for these purposes. This lowers the metal content in drinking water, rainwater, and
in the contaminants added to the different wastewater streams.
4.5 Resources in wastewater
The main resources in wastewater are nutrients, energy, and water. Energy and water are
described per technology in the next chapter. A general description of nutrients and the
possibility for reuse is given in this section. The fate of nutrients in wastewater treatment is
described in more detail in the next chapter on technology characterisation.
4.5.1
Nutrients
Like water, nutrients are essential to life on earth. Important nutrients are carbon(C), nitrogen
(N), phosphorus (P), sulphur (S)18, potassium (K), calcium (Ca), and magnesium (Mg). In
addition, life depends on trace elements, including iron, zinc, and copper. In natural
ecosystems, the flow of nutrients is conserved as much as possible, input and loss are usually
small compared to the amount circulating within the system (Groot 1993). In urban areas,
however, the input, in the form of fertiliser and food, and output, waste, is often large.
In the urban water chain most of the organic matter is contained in faeces, while the other
nutrients, such as nitrogen, phosphorus, and potassium are mainly contained in urine.
Nutrients can be ‘lost’ by dilution to surface water, as gaseous emissions, or by capturing
them in toxic sludge, while at the same time the application of artificial fertiliser is
increasingly important in the world food production. In 1997, world wide almost 85 million
tonnes nitrogen, 30 million tonnes phosphorus, and 20 million tonnes potassium were applied
as fertiliser in agriculture (Matthews et al. 1999, p.14). In the Netherlands, where both
agriculture and life stock breeding are practised very intensively, approximately 750,000
tonnes nitrogen is applied in the form of manure and 500,000 tonnes nitrogen as fertiliser
(about 480 kg/ha), while the estimated crop uptake is less than 300,000 tonnes (Matthews et
al. 1999, p.17, see also Figure 13 on page 53).
17
Note we use only domestic wastes, no contamination by industrial wastewater.
Sulphur is not discussed in this paragraph, as it is relevance in domestic wastewater treatment is limited. Main issues in the
sulphur cycle are emissions form fossil fuel burning, and natural processes in the atmosphere, soil, and oceans.
18
50
Sustainable wastewater treatment
4: Streams: quantifying inputs & categorising outputs
4.5.1.1 Carbon
A wide range of organic compounds can be found in wastewater: proteins, carbohydrates,
fats, oils, and greases, surfactants, and pesticides. These organic compounds contain carbon,
hydrogen, oxygen and some also nitrogen, sulphur, phosphorus and iron. The amount of
organic matter in wastewater is expressed as biochemical oxygen demand (BOD) and
chemical oxygen demand (COD) (Tchobanoglous 1991, p 70-83).
Biodegradable organics are removed as solids or biologically degraded under aerobic or
anaerobic conditions. Under aerobic conditions the organics can be converted to CO2 and
biomass, while under anaerobic conditions methane can be formed. Biomass can be used as
soil conditioner, if algae, fish or plants are used, these maybe used as food for life stock or
humans. Biomass or CH4 can also be used for energy production. When the organics are
emitted as gases such as CO2 or CH4, the carbon source is lost for reuse and contribute to
global warming19.
4.5.1.2 Nitrogen
Nitrogen occurs in the natural environment in various chemical forms, which are all parts of
the natural recycling processes. Human activities are brining extra amounts of certain nitrogen
compounds into the environment (see Table 12), which are to some extent, ‘neutralized’ by
these recycling processes (Groot 1993, p.54).
Each year an estimated 140 million tons
of nitrogen is removed from the soil in
crop production worldwide (Groot 1993,
p.54). Some biological processes
involved in the nitrogen cycle can return
some of this nitrogen to the soil, notably
through fixation of atmospheric nitrogen
by certain plants and nitrification of
ammonia by certain bacteria (Groot
1993, appendix I94). Thus more than 90
million tons of this deficit is made up for
by biological nitrogen fixation, which
thereby also represents a considerable
economic value (Groot 1993).
Table 12: Human alteration of the global nitrogen
cycle (Vitousek et al. 1997).
Anthropogenic sources:
- fertiliser
- legumes and other plants
- fossil fuels
total:
Natural sources:
Soil bacteria, algae, lighting, etc.
Annual release of
fixed nitrogen:
(million tonnes)
80
40
20
140
90-140
Urea and proteins are the principal sources of nitrogen in wastewater. Total nitrogen
comprises organic nitrogen, ammonia, nitrite and nitrate, often referred to as total Kjeldahl
nitrogen. The organic N is typically associated with urea, proteins, peptides, nucleic acids,
and biomass such as algae. The initial removal of this material as solids (TSS) is usually quite
rapid. Much of this organic N then undergoes decomposition or mineralisation and releases
ammonia to the water (Tchobanoglous 1991, p.85-86, Reed et al. 1995 p.191).
Ammonia in the wastewater can be converted in two related forms: ammonia ions (NH4+) and
dissolved ammoniac gas (NH3); the balance between these two forms depends on the pH
19
It can be argued that the emission of CO2 by wastewater treatment processes is a natural event, and therefore CO2 ‘neutral’.
However, if the wastewater was treated differently the organics could have been applied to the soil and increase soil fertility.
It is however fair to state that the contribution to global warming by direct CO2 emissions out of wastewater is relatively
small compared to other sources.
51
Sustainable wastewater treatment
4: Streams: quantifying inputs & categorising outputs
(alkalinity is required to support biological nitrification) and temperature (Tchobanoglous
1991, p.85-86, Reed et al. 1995 p.192). Ammonia can be converted to nitrite:
NH 4+ + 1.5O2 → NO2− + 2 H + + H 2 O
Nitrite (NO2-) is relatively unstable and easily oxidises to nitrate (NO3-). Therefore, nitrite is
rarely present in high concentrations. However as nitrite is extremely toxic to most fish and
aquatic species, it is considered an important constituent in wastewater. The opportunities for
nitrification exist where conditions are aerobic, where there is sufficient alkalinity, the
temperature is suitable, and after most of the BOD has been removed so that the nitrifying
organisms grow at a low enough sludge age. The nitrifying organisms are also thought to
prefer attachment to the surface. At temperatures less than 10°C nitrification slows down
considerably and at 0°C nitrification activity ceases completely.
NO2− + 0.5O2 → NO3−
Nitrate (NO3-), the most highly oxidised form of nitrogen found in wastewater, can be
removed through biological denitrification. Biological denitrification requires anoxic
conditions, because nitrate serves as electron acceptor, and the availability of an adequate
carbon source. The biological nitrification step typically requires prior removal of much of the
BOD (to 20 mg/l or less). So there may not be much of the original available organic carbon
present in the wastewater by the time denitrification is expected to occur. As a rule of thumb,
it is estimated that 5-9 g of BOD is required to denitrify 1 g of NO3-N (Reed 1995, p.195).
Nitrate is an essential nutrient to plants; however, in high concentrations (>10 mg/l) it is toxic
(Rowe 1995, p81 and p.443). When it is converted to N2 through denitrification it is lost for
reuse. As N2 is present in high concentration in the air it is not seen as a harmful emission.
However, natural binding of N2 occurs on limited scale and ‘artificially’ it is an energy
intensive process (10 to 13 kWh/kg N fertiliser, and 7 to 11 kWh/kg P fertiliser, Bengtsson et
al. 1997, p.20, Kärrman 2000, p.151).
About 80% of the nitrogen in domestic wastewater originates from human urine (Adamsson
et al. 1996). Therefore, an effective way to recycle nitrogen would be the separate collection
of urine for use as fertiliser in agriculture. This means that in temperate regions urine has to
be stored during winter season as most fertiliser is applied in spring (0.5 – 1 m3 urine per
person) (Adamsson et al. 1996). During storage of urea, the dominant nitrogen source in urine
may decompose into ammonia (50 to 90% of the nitrogen in human urine is contained in urea,
depending on the proteins in the diet, Adamsson et al. 1996). Ammonia is more toxic and may
‘escape’ as a gas resulting in a nitrogen loss, therefore, treatment of urine maybe necessary.
Gijzen and Mulder (2001), indicate that there is no other element cycle as disrupted by human
impact as the nitrogen cycle. A parallel is drawn with the greenhouse gas built up in the
atmosphere and the massive introduction of reactive forms of nitrogen in the biosphere,
although the greenhouse gas effects are far better understood. The gradually developing
dangerous bio-geo-chemical effects on public health and the environment by forms of reactive
nitrogen are not recognised. Furthermore, Gijzen and Mulder (2001) find it unrealistic to
expect that nitrification and denitrification processes for waste and wastewater will resolve
this unbalance, as these processes are very costly. Therefore, more severe and long-term
eutrophication processes worldwide are predicted. The solution Gijzen and Mulder (2001)
52
Sustainable wastewater treatment
4: Streams: quantifying inputs & categorising outputs
have in mind for human survival on the long term is a combined waste minimisation and
effective reuse strategy for manure and human sewerage; “Alike nature, we will have to learn
to operate ion a resource efficient way as this is a driving force of evolutionary selection”. In
Figure 13, the Dutch nitrogen balance is sketched.
4.5.1.3 Phosphorus
Phosphorus is required in ecosystems in quantities only about one-tenth of nitrogen.
Nevertheless it is essential to the metabolism of most living organisms. Despite the relatively
small quantities needed, phosphorus is probably the limiting nutrient in more circumstances
than any other element because of its scarcity in accessible form in the biosphere (Groot 1993,
p56). Two chemical properties of phosphorus are responsible for this natural scarcity, which
is much more acute than one might expect from the size of the total phosphorus pool in
sediment rocks. One is that phosphorus does not form any important gaseous compound under
conditions encountered in the environment. The second is the insolubility of the phosphate
Figure 13: Nitrogen and phosphorus balance for the Netherlands
(RIVM/CBS 2000, C5.5, C5.6, C5.7).
53
Sustainable wastewater treatment
4: Streams: quantifying inputs & categorising outputs
salts (PO42-) and the common cations Ca2+, Fe2+ and Al2+. The lack of gaseous compounds
deprives the phosphorus cycle of an atmospheric pathway linking land and sea and thus slows
the closing of the circle to the very sluggish pace of sedimentation, uplift, and weathering.
That phosphate forms insoluble compounds with constituents of most soils retards its uptake
by plants and slows its removal and transport by surface water and groundwater (Groot 1993).
The usual forms of phosphorus found in aqueous solutions include the orthophosphate,
polyphosphate, and organic phosphate. The orthophosphates, for example, PO4-3, HPO4-2,
H2PO4-, H3PO4, are available for biological metabolism without further break down. The
polyphosphates include those molecules with two or more phosphorus atoms, oxygen atoms,
and in some cases hydrogen atoms combined in a complex molecule. Hydrolysis of
polyphosphates to orthophosphate is usually quite slow. Organically bound phosphorus is
usually of minor importance in domestic wastewater (Tchobanoglous 1991, p.86-87).
Detergents and urine are the main sources for phosphorus in domestic wastewater (67-68 % of
P in toilet water originates from urine, Lind et al. 2001, Hanæus et al. 1997 report P: 48% in
urine, 24% faeces, 28% greywater).
Phosphorus removal in natural systems occurs by means of plant uptake, adsorption,
complexation, and precipitation, and is very effective in soil filters such as subsurface flow
wetlands. Phosphorus discharged in the effluent is ‘lost’ for reuse. When the phosphate is
contained in a non-contaminated liquid or solid fraction it can be recycled to agriculture, for
instance through urine separation. Phosphate recycling has a two-fold objective, prevention of
eutrophication in surface water and sustaining world-wide food-production. This last
objective has two sides, on one hand it is true that 80% of the phosphates is used as fertiliser,
and that still millions of people are undernourished. On the other hand however, fertilisers are
often applied inefficiently: in Western Europe and the USA approximately 4 to 5 million
tonnes of mineral fertiliser phosphate can be replaced by more efficient use of manure (NHM
1998). Furthermore, meat consumption is an ineffective way to satisfy nutrient demand. The
estimations of phosphate depletion range from zero reserves in 2060 to decreasing demand
and reserves lasting for at least 100+ years (NHM 1998). Still, closing the phosphate cycle by
recycling phosphate to agriculture is the only sustainable solution. As an example, Figure 13
shows the Dutch phosphate balance.
4.5.1.4 Potassium (K), calcium (Ca), and magnesium (Mg)
Potassium is an abundant and widely distributed element in the Earth’s crust, in life, and in
natural waters. Potassium is usually found in the ionic form, as the salts are highly soluble. It
is readily incorporated into mineral structures and accumulated by aquatic biota.
Concentrations in natural water are usually less than 10 mg/l (Chapman 1992, p.83).
Industrial discharges and runoff from agricultural land are main sources for potassium in
surface waters. The main sources of potassium in domestic wastewater are urine and faeces.
Potassium is a major nutrient for plants, and is naturally recycled in ecosystems. Intensive
agriculture disrupts this recycle and therefore, requires inputs in the form of potassium
fertiliser.
Calcium and magnesium are also essential nutrients, although sometimes indicated as
secondary nutrients, because they are required in lesser quantities and are not scarce20.
20
Magnesium is the third most abundant element on Earth and the sixth in the crust, calcium is the fifth most
abundant element in the curst of the Earth (Emsley 1998, Cox 1995).
54
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4: Streams: quantifying inputs & categorising outputs
Furthermore, they do not play an important role in environmental problems such as
eutrophication. Calcium and magnesium have a positive influence on the soil structure and
therefore on soil fertility (Ca and Mg reduce the sodium adsorption ratio that is used as
restriction in using treated wastewater for irrigation, see Rowe et al. 1995, p.26).
Calcium is present in all waters as Ca2+, and is especially abundant in groundwater and
surface water. Industrial, as well as water and wastewater treatment processes contribute to
the calcium in surface water. Furthermore, acidic rainwater can increase leaching of calcium
from soils. Natural concentrations are typically less than 15 mg/l (Chapman 1992, p.84). The
salts of calcium, together with those of magnesium are responsible for the hardness of water.
Magnesium is common in natural waters as Mg2+ in concentrations ranging from 1 to >100
mg/l depending on the rock types in the catchments (Chapman 1992, p.84).
4.5.1.5 Nutrients in the decision support tool
We want to include nutrient recycling in the decision support tool and therefore included
options such as separation of blackwater and yellowwater, processes such as composting and
infiltration. Furthermore, we want to indicate the reuse potential of the outgoing streams, for
instance can these streams be used for fertilisation, soil conditioning, or irrigation. But how
can we indicate the fertiliser value of urine and of sewage sludge? According to European
legislation (76/116/EEC), the minimum concentration of nutrients in NPK-fertilisers should
be 20% (N + P2O5 + K2O), of which the minimum for the nutrients is 3% N, 5% P2O5, and
5% K2O. For liquid fertilisers these percentages are lower, namely 15% (N + P2O5 + K2O),
and 2% N, 3% P2O5, 3% K2O. If we check the composition of the dry solids in urine in Table
6 on page 44, we see that urine contains enough nitrogen but that phosphorus and potassium
are lower than NPK-fertiliser norm in the European directive. This means that urine has to be
concentrated, or dried, and that a nitrogen loss is acceptable or that additional phosphorus and
potassium has to be added. Further dilution of urine with flush water and other wastewater
streams will decrease the fertiliser value. However, application of fertiliser and irrigation
water may be practical.
European restriction for land application of nitrogen is 170 kg/ha in 2003 (CBS 2000b). The
restrictions for pathogens and metals are given in Table 10 and Table 11 respectively. For the
application of urine, nitrogen application may be the restricting factor, because urine is low in
pathogens and heavy metals. Since the application of urine as fertiliser is still experimental,
we consider urine to be a potential fertiliser that may be optimised through concentration and
possibly by addition of nutrients. Therefore, we overrule the restriction for the copper to
phosphorus ratio, this we increase form 60 to 70 mg Cu/kg P. This choice is made as we agree
that the application of urine and sludge in agriculture should be restricted by heavy metals,
but in our opinion the restrictions are too tight if reuse of natural resources such as urine and
uncontaminated21 and treated blackwater or blackwater sludge is not allowed.
Note that besides pathogens and metals other factors are important in wastewater reuse, for
instance the salt content, expressed in the sodium adsorption ratio (SAR) is an important
factor in long-term soil fertility. Furthermore, practical problems may occur, for instance
odour, or solid content and the irrigation technique causing clogging (see Crook 1991, or
Rowe et al. 1995). These are not taken in consideration in the decision support tool.
21
Note we use only domestic wastes, no contamination by industrial wastewater.
55
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4: Streams: quantifying inputs & categorising outputs
4.6 Water services quantified in the decision support tool
In this chapter, the water services are quantified defining the inputs as well the classification
of the outputs of the model the domestic water system.
Part of the input to the model are incoming streams, these are: drinking water quality,
household water quality, rainwater quality and quantity, and the waste qualities and quantities
including faeces, urine, bio-waste, and contaminants. Other inputs to the model are for
instance costs, space requirements, energy use, and social-cultural aspects of the water
services. These inputs are listed in Appendix 1, which shows part of the spreadsheet where
data are stored and can be changed by decision makers to adapt the analysis to their insights
and the local situation.
The classification of the output reflects the threats and opportunities present in the water
sector. The decision support model uses the restrictions on metal concentrations, pathogens,
BOD, and nutrients to categorise the outgoing streams of the model, using the following
categories; domestic reuse, irrigation, infiltration, discharge, combined sewer overflow,
fertiliser, soil conditioner, landfill, and waste
Table 13, summarises the restrictions that are applied by the decision support tool. Note that
streams are classified as waste if excluded from all other categories. The red figures indicate
that we some times chose not to use the figures based on legislation and guidelines, as we do
not want to prohibit the reuse of natural resources such as compost, urine, rainwater, etc.
Table 13: Classification of effluents by the decision support tool.
Domestic
reuse
(mg/l)
Solids:
TS *
TSS *
Organics:
BOD
Pathogens:
FC
Metals:
Cu
Zn
Nutrients:
N-total
P-total
Irrigation
(mg/l)
Fertiliser
value
(mg/kg P)
Infiltration
Discharge
Compost
Landfill
(mg/l)
(mg/l)
(mg/kg TS)
(mg/kg TS)
> 500m
> 100n
< 0.5d
< 35a
<60m (90)
<200m(300)
< 75m (100)
<300m(350)
e
< 10k
< 20l
h
< 10i (30)
(no./l)
< 10,000g
(no./l)
< 20,000b
< 2c
< 0.2f
< 2f
< 25a
<60o (70)
< 500o
15 mg/g
TS
< 0.015d
< 0.065d
j
d
< 10a
< 1a
< 0.4
d
j
Notes for Table 13:
(a) 91/271/EEC in Gray 1999, p.304, COD < 0.5 mg/l, for discharge to surface water Strauss 1996
reports BOD of < 200 mg O2 /l for streams and estuary, and < 500 mg/l for rivers and seas,
Veenstra 1996 reports < 10 to 50 depending on the sensitivity of the water.
(b) 76/160/EEC.
(c) 98/83/EEC.
(d) Dutch infiltration act 1993 (Infiltratiebesluit bodembescherming van 20 april 1993, in Wet
bodembescherming), in Bosman 1997, Nitrate < 5.6 mg N/l and Ammonia < 2.5 mg N/l.
(e) Rough estimate based could be < 6,000 mg TS /l on 4 times the drinking water norm, drinking
water norm of 1,500 mg TS /l in Europe and UK (Mustow 1996), and 1,000 mg TS /l in Dutch
water act and measured by VEWIN (Bosman 1997).
<continued on following page>
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4: Streams: quantifying inputs & categorising outputs
Notes table 13 continued:
(f) EPA restrictions for long-term irrigation, in Rowe 1995, p.32, also norm in USA and by FAO, in
Saudi Arabia 0.4 mg Cu /l and 4.0 mg Zn /l (Bosman 1997, Appendix 15). WHO guideline for
unrestricted irrigation in Mara et al. 1989, see Table10 page 48.
(g) Restriction in USSR is < 3 mg O2/l reported by Chapmann 1992, for roof-runoff 2 to 19 mg/l is
reported by STORA 1990 ( this is also a potential source for water to be used in the household).
(h) BOD < 10 mg/l for irrigation is reported by EPA in Mustow 1997, and by Ogoshi 2000 for
environment water (ponds etc), and for irrigation in Saudi Arabia (in Abu-Zeid 1998), in Tunisia
< 30 mg/l for irrigation (also Abu-Zeid 1998), 30 mg/l for landscape irrigation by EPA in
Mustow 1997, 100 mg/l by Veenstra 1996. We sometimes used 30 mg BOD/l to allow irrigation
with rainwater that is contaminated by mixing with water used for outdoor purposes.
(i) Concentration in freshwater for both Cu and Zn around 0.01 ppm see Table 11 on page 49.
(j) Hulsmann et al. 1998, proposed norm for household water 1 to 10 mg TSS/l, drinking water
norms are 1 in NL and 0 in EU (Bosman 1997).
(k) Abu-Zeid 1998, in Saudi Arabia 10 mg TSS/l, Tunisia 30 mg TSS/l, in Egypt depending on type
of irrigation: mo limit (trees), 350 mg TSS/l (cotton, flowers), 40 mg TSS /l (crops not eaten
raw), 20 mg TSS/l (all crops).
(l) NVA 1994, BOOM, very clean compost 25 mg Cu/kg, and 75 mg Zn/kg, composting provides a
dry solid content of about 50 to 60% (p.29). Note that the European norm (86/278/EEC) allows
higher concentrations see Table 11 on page 49. Instead of 60 and 75 mg/Cu/kg TS we use 90
and 100 mg Cu/kg TS for compost and landfill to allow the reuse of a natural resource (compost)
and have slightly higher norm for a less sensitive use (landfill). Similar we used for Zn: 300
mg/kg TS in compost and 350 mg/kg TS in landfill.
(m) Typical TS content of digested sludge 10 % (Tchobanoglous 1991, p.771).
(n) We derived a norm of <60 mg Cu/kg P based on Driessen et al. 1996, average values of three
artificial fertilisers (DAP, NP, TSP), P measured as P2O5. Assume urine contains 60 g TS/cap/d,
and 1 g P /cap/d, and we allow maximum a 10% loss than > 15 mg P/g TS. However, we later on
used 70 to allow the reuse of urine as potential fertiliser.
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cooperation with General Sciences of Technology Management, The Netherlands, May 2000 (in Dutch).
Versteegh J.F.M., Evers E.G., and Havelaar A.H. (1997). Gezondheidrisico's en normstelling voor
huishoudwater (Health risks and standards for household water), RIVM, Bilthoven, The Netherlands,
September 1997, RIVM Reportnumber: 289202019 (in Dutch).
Vitousek P.M. et al. (1997). Human alteration of the global nitrogen cycle: causes and consequences, Issues in
Ecology, No. 1 (1997), pp. 4-6.
Winblad U. and Killama W. (1980). Sanitation without water, Reversed edition, published by SIDA, Stockholm,
ISBN 9158670084.
VROM (1992). Milieu-effectrapport, Beleidsplan drink- en industriewatervoorziening, Basisrapport 3:
oppervlaktewater(Environmental impact report, Policy plan drinking water and industrial water, report
surface water), Ministry of Housing, Spatial planning, and the Environment, report by IWACO, ’s
Hertogenbosch, August 1992, report number 102.6100 (In Dutch).
VROM (1986). Beperking van zware metalen in zuiveringsslib door concentratie in een deelstroom (Limiting
heavy metal content in wastewater treatment sludge by concentration in part of stream), by Tauw Infra
Consults, Ministry of Housing Spatial Planning and Environmental Management, ISBN 90-346-0835-2 (in
Dutch).
WOB (2001). Prices on Internet, see http://www.nvwob.nl/ (accessed: 27/02/01).
WOB (1998). Gemiddelde samenstelling van drinkwater per pomp station 1997(Average composition of
drinking water per pumping station 1997), Published September 17th 1998, WOB (Waterworks East
Brabant), Den Bosch , The Netherlands (in Dutch).
WRI (2000), A guide to World resources 2000/2001, People and ecosystems, the Fraying web of life, World
Resource Institute, Washington, ISBN 1-56973-443-7.
Zobirst J. (2000). Quality of roof runoff for groundwater infiltration, Water Research, Vol.34, no.5, pp.14551462.
60
Sustainable wastewater treatment
5 Processing units: technology characterisation
5 Processing units: technology characterisation
This chapter introduces the most essential sustainability indicators per technology. A
complete overview of the functional, economic, environmental, and social-cultural indicators
is given in technology data sheets included in Appendix 3. A detailed description of the
literature inventory is given in an internal report (Balkema 2001).
5.1 Selection and characterisation
In this chapter, we characterise the technologies that are included in the decision support tool.
The technologies are selected to represent a wide variety of water use and wastewater
treatment options. This selection includes technologies used in the ‘conventional’ centralised
wastewater treatment systems as well as so-called ‘alternative’ small-scale systems. Note that
the characteristics of wastewater treatment technologies are mostly not linear with scale.
Therefore we chose to include characteristics of the same technology on different scales.
Small-scale systems begin at household scale (5 pe), many systems are characterised for
neighbourhood scale (100 to 1,000 pe), while some are available on large scale up to 100,000
pe. Probably more important than the effect of scale is that a decentralised approach
enhancing source control in the form of water conservation, use of rainwater, and separation
of wastewater streams. These options are also incorporated in the decision support tool.
5.2 Water supply
In Chapter 4, we discussed different options for water supply, such as piped supply of
centrally treated drinking water, and the supply of alternative water qualities such as
household water or rainwater. To use these alternative water sources for drinking, additional
treatment is needed. Small-scale techniques that can be applied on household scale to
disinfect the water is discussed in the next paragraph.
5.2.1
Water disinfection
There are many technologies that can be used to disinfect water on household scale (see Table
14). However, for the production of drinking water, the most suitable disinfection processes
are slow sand filtration, membrane filtration, and heating the water to > 75°C, because only
these processes are effective in removing viruses and chloride resistant pathogens such as
Giardia and Cryptosporidium (Jørgensen 1998, Medema et al. 1996). Furthermore, with these
techniques no traces of chemicals remain in the water, which is an advantage as no toxics are
consumed. However, in storage and distribution networks this may be a disadvantage as traces
of chemical disinfectants can prevent regrowth of pathogens.
In the following paragraphs we describe small-scale disinfection units based on membrane
filtration and UV. These technologies are selected because they are suitable to disinfect
household and rainwater and these are commercial available as compact systems for domestic
use.
5.2.1.1 Membrane filtration
Membranes can remove viruses, bacteria and other micro-organisms from water and wastewater either
completely (UF/NF/RO) or significantly (MF), including chlorination resistant ones as Giardia and
Cryptosporidia (Madaeni 1999, Randles 1996). However, due to pore size distribution, ultra-filtration
(UF) and even reverse-osmosis (RO) may not form a complete barrier to viruses (Urase et al. 1994 and
61
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5 Processing units: technology characterisation
referring to Sober 1972). However, Lopez-Pila 1996, indicates that even micro-filtration (MF), known
to significantly eliminate bacteria and protists from wastewater, can also filter out viruses. Although
smaller than the pore size of the micro-filtration membrane, viruses are reduced too because they are
mostly bound to larger particles.
Potential advantages of membrane filtration are: (1) production of superior and constant water
quality, (2) independency of removal efficiency from flow rate, and (3) modular adaptation to
different scales. Potential disadvantages are: (1) possible after growth, (2) possibly retention
of small species such as colloids, and viruses, (3) removal of valuable components form the
water, and (4) flux decline as result of fouling.
A unit for production of drinking water inside the household may consist of different filters that pretreat the water, a reverse-osmosis unit, and a post filtration unit. This relatively compact unit is
capable of producing about 200 litres of drinking water a day. The high rejection rate is a
disadvantage; only 5 to 15% of the inflowing water passes through the membrane.
5.2.1.2
Ultra-violet disinfection
UV-radiation, at a wavelength of approximately 260 nm, damages microbial DNA and
therefore blocks DNA-replication and effectively inactivates micro-organisms. UV-radiation
is effective against bacteria and viruses (2 to 5 10log reduction), but not Cryptosporidium
oocysts or Giardia cysts22 (0 to 1 10log reduction) (Medema et al. 1996)
Table 14: Commercial available small-scale disinfection units for drinking water production.
Description:
Chlorination
Ceramic filter
Distillers
Ozone
Reverse
osmosis
Sand filtration
UV
(electric light)
UV + Heat
(sunlight)
22
Can be done by adding sodium hypochloride (household bleach) to the water or
by producing this from salt (NaCl) through electrolysis. Cheap method, but not
effective against Giardia and Cryptosporiudium, changes taste, and can produce
carcinogenic by-products.
Disinfection through sedimentation, filtering, and chemical if colloidal Ag is added.
Advantages are: local production in developing countries, simple in use, and
inexpensive.
By condensing steam, 99.9% pure distilled water is obtained. Advantage: also
effective to remove Fe, Mn, F, As, and salts. Disadvantage: high energy requirements,
loss of valuable compounds.
Energy is used to produce the toxic gas ozone in the water that kills most pathogens.
Can also degrade organic micro-pollutants. Relatively expensive.
Water is pressed through a membrane, requires energy for pressure built up. Removes
most solids, metals, organics, etc. May reject a large amount of the water. Relatively
expensive.
Disinfection through filtering, and adsorption in upper layer. This layer is removed
from time to time to prevent clogging. Removes most pathogens and also toxic as Fe,
Mn, and As.
UV-radiation destructs DNA in micro-organisms and is 99.9% effective against
bacteria and viruses, short contact time, no residual effect, requires high
transparency of the water (TSS <30 mg/l).
Effective and can be very cheap for instance when using a bottle painted one side
black and leaving this for 5 hours in intense sunlight. Disadvantages; requires low
turbidity, possible regrowth.
For inactivation of protozoan cysts higher dosages is necessary see Table 6.4 in Bitton 1999 p.162,.
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5 Processing units: technology characterisation
Advantages of UV-disinfection are: (1) the physical process is more user friendly and less
harmful to humans and the aquatic environment, and (2) the contact time is short (20 to 30
seconds with low-pressure lamps). In contrast to chlorine disinfection, UV-treatment does not
produce any known undesirable mutagenic, carcinogenic, or otherwise toxic by-products
(Bitton 1999, p.165). The main disadvantages are (1) that turbidity23 can render UV
disinfection ineffective and (2) that organisms sometimes reverse the destructive effects of
UV, so-called ‘photoreactivation’ or ‘dark repair’ (EPA 1999b).
A simple and low cost way to apply UV-disinfection is by using sunlight, for instance by
placing water in a bottle on a black surface for 5 hours in intense sunlight (≥ 600 W/m2,
Bitton 1999, p.166). Besides UV-radiation, also heating occurs, which has a disinfective
effect itself 24. The major disadvantage of this method is that it is difficult for the end-user to
judge the water quality. For this purpose a water pasteurisation indicator was developed, in
the form of soybean fat that melts at 69°C, indicating that the pasteurisation temperature was
reached (Rolla 1998). However, this indirect indicator that cannot protect people against
ineffective disinfection due to turbidity of the water or regrowth of pathogens. A more
centralized approach such as the application of solar heaters for water pasteurisation in
Tanzania (see Picture 1) may enhance control. The costs for the solar drinking water
pasteurisation in Tanzania are reported to be < 0.008 US$ per litre (Jørgensen et al. 1998,
p.446). The cost can be lowered considerably through local production of solar collectors.
Jørgensen et al. (1998) use advanced Danish products, such as Rockwool insulation, a UVresistant polycarbonate plate, an aluminium box, and a Danfoss thermostat.
Another example of a small-scale UV-disinfection system for domestic water purification is
an active carbon filter followed by an UV-lamp. Such system is described and tested by
Serpieri et al. 2000. The benefits of the combination of those two technologies is that
activated carbon removes suspended solids, adsorbs priority pollutants, and improves the taste
of the water but diminishes the bacteriological quality of the water, while UV-disinfection
removes the bacteria and viruses to very low levels (Serpieri et al. 2000, p.18).
Picture 1: Solar water heaters in Tanzania disinfecting of surface water (photo by K. Nørh).
23
Influent for UV-disinfection has to have a TSS < 30 mg/l and turbidity < 30 NTU (EPA 1999b).
Jørgensen et al. (1998 p.446), report elimination of the following pathogens (T>75° C); Escheria coli, Streptococcus
faecalis, Salmonella typhimurium, Cliforms, Thermotolerant coliforms, and from literature: Shigella dysenteriae, Viberio
cholerae, Mycobacterium tuberculosis, Legionella, Aeromonas, Pseudomonas, Giardia lamblia, Entamoeba histolytica, and
Cryptosporidium. Potential problems: Hepatitis A, and some spores (for instance of B. Cereus and B. Subtilis).
24
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Sustainable wastewater treatment
5.2.1.3
5 Processing units: technology characterisation
Water disinfection in the decision support tool
The three above described disinfection systems, membrane filtration and the two options for
UV-disinfection, are incorporated in the decision support tool. For details see the Technology
Data Sheets 1,2, and 3 in Appendix 3.
5.2.2
Water conservation25
Technically and environmentally, it makes sense to conserve water because this reduces the
pressure on water resources, and the effort of treatment and transport of water and
wastewater. However, economically it can make sense to use water wastefully, if water is
cheap and water conservation asks for high investments. From the perception of the end-user,
the amount of water supplied is not important the main issue is that water services are
fulfilled. Some engineers may have the opinion, that saving water for toilet flushing is not
worthwhile, compared with increase in water consumption for showering when people tend to
use the shower longer and more often. However, to the end-user this makes perfectly sense,
flushing the toilet with less water does not affect comfort. Long showers may be a required
service for relaxing, warming up, or cooling down. Restricting this, especially in areas where
water is plentiful, may be considered as a unacceptable sacrifice by the end-user. Table 15
quantifies water conservation for the Dutch situation.
Table 15: Water conservation measures in the Netherlands.
Function:
Bath
Dishes by hand
Dishwasher
Food preparation
Shower
Toilet
Wash basin
Washing by hand
Washing machine
Others
Total:
Water consumptiona:
(l/use)
108.69
7.66
25.21
56.89
6.72
(use/d)
0.18
0.64
0.21
0.68
5.80
(% pb)
0.46
1.00
0.17
0.99
(l/(cap*d)
9.0
4.9
0.9
2.00
38.3
39.0
3.96
42.00
96.88
-
1.08
0.05
0.28
-
0.98
1.00
0.94
-
4.2
2.1
25.5
8.20
134.1
Water conservation
measure:
Description:
(%)c
New (14 l/use)
0.5
6 l/minf
6e
Flush interrupter
9e
Booster
(15e)
Vacuum toilet
(26)
Separation toilet
(26)
Compost toilet
(32e)
Tap heads
3
New (39 l/use)
11.4
Water buttg
7
29.5
Costs:
(Euro)d
590
30
45
590
45
(a) Source NIPO 1995,( b) Percentage of population = penetration, (c) Percentage of total average Dutch water
use conserved, (d) Estimated investment in Euro – for instance total cost for buying a new washing machine, not
additional costs, (e) source Geldof 1997, (f) ‘normal’ showers use about 10 l/min, saves approximately 50 m3 gas
for heating as well, source VEWIN 1995,(g) estimated savings 5 m3/household/year source: VEWIN 1995.
25
Note: Water demand is seen as restricting that has to be fulfilled, the decision support tool does not allow supplying less
than the demand (see Chapter 4). The only way to lower demand is through water conserving technologies (described in this
paragraph), furthermore water can be reused (see next paragraph). Therefore, water use is not described here but in Chapter 4.
64
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5.2.2.1
5 Processing units: technology characterisation
Water conservation in the decision support tool:
In the decision support tool, the water demand can be lowered by applying of a water efficient
washing machine, a water conserving shower, and different types of toilets (see paragraph 5.4
Sanitation). The main impact considered is water conservation, the energy conservation and
the material intensity of the solutions is not taken into account.
Drinking water conservation can also be done through water reuse and the use of rainwater,
this is described in paragraph 5.3.3 on Greywater systems, 5.3.4 on Closed water systems, and
5.3.2 on Rainwater systems.
5.3 Water reuse
5.3.1
Dynamics of domestic water use
To quantify possibilities for the use of rainwater or reuse of wastewater it is necessary to
know how much water is used and when it is used. Research done by for instance Dixon
1999, and Verhoeven 2000 describe the dynamics of domestic water use. Data on dynamics
can be based on statistical data sets. These are useful to indicate what average conservation
can be achieved using different storage tank sizes, roof sizes, number of people in the
household, rainwater data, etc. However, behaviour of people can differ significantly,
therefore, insight into specific situations requires measured data. This is illustrated by the
graphs of water use in Figure 14 and 15. The construction of statistical and measured data sets
is described in the next two paragraphs;
(1) Statistical data:
A statistical database is generated by randomly selecting data out of a probability distributions
for the number of uses per day, the time of use, and the amount of water used. For our
simulations we used Dutch averages on water use (NIPO 1999) and the probability
distributions described by Butler (1993) corrected26 for the Dutch situations.
(2) Measured data:
Experimental data are gathered with a water meter linked
to the computer (see Picture 2). The time is registered for
every 0.07 litre water used. In this way, the water flow (l/s)
can be calculated and with the known characteristic flows
of the toilet, shower, and washing machine, the complete
water use pattern can be reconstructed, while measuring at
a single point in the house only.
Dynamic simulation of domestic water use:
Picture 2: Water meter sending
pulses to the computer.
With the two data sets we carried out dynamic simulations27 for three scenario’s, (1) rainwater
use, (2) greywater reuse, and (3) closed water cycle. The results for different households are
shown in Table 16, 17 and 18. Table 16 gives the results for the three scenarios for a 2
26
The amount of water used per use is multiplied by the following correction factors: toilet 1.3, bath 0.6, shower 3, and
washing machine 2. No corrections to washbasin and sink, and additoional data for dishwasher and outdoor water use.
27
In these simulations we used a toilet with a flush interrupter (on average 5.5 l/flush) and new washing machine (49
litres/wash). The rainwater data have been measured in the Netherlands (Lemmer 1999, 886 mm, Dutch average = 775 mm).
The roof area is set to 50 m2, in literature: 57 m2 for a town house and up to 357 m2 for a large urban villa (NOVEM 1999).
However, for apartment buildings the 50 m2 roof per household will not be available.
65
Sustainable wastewater treatment
5 Processing units: technology characterisation
persons household based on a statistical data set, Table 17 for 5 persons. The results in Table
18 are based on measured data in a 2 persons household.
Table 16: Comparing simulation results for a two persons household.
Case:
To storage:
(% of total water use)
From storage:
(% of total water use)
Water savings for different storage volumes:
(m3/household/year (% of total water use))
0.2 m3
0.4 m3
0.6 m3
1.4 m3
Rainwater
Rainwater (62%)
Rainwater (62%)
13 (23%)
16 (27%)
15 (26%)
18 (32%)
16 (28%)
19 (34%)
17 (29%)
22 (38%)
Greywater
Closed
cycle
Wash.machine (8%)
Shower (19%)
Greywater (61%)
Greywater +
rainwater (123%)
Toilet (29%)
Toilet + washing machine
(38%)
Toilet (29%)
Toilet (29%)
Total water use (100%)
Total water use (100%)
4 (8%)
11 (19%)
35 (61%)
50 (89%)
4 (8%)
11 (19%)
35 (61%)
53 (93%)
4 (8%)
11 (19%)
35 (61%)
54 (95%)
4 (8%)
11(19%)
35 (61%)
57
(100%)
Simulation parameters: statistical data set, 2 persons household, water use:,57 m3/ year, toilet: 5.5 litres/flush, washing
machine 49 litre/wash, rainwater harvest: 35.44 m3/ year (data Lemmer 1999, roof area 50 m2, runoff coefficient 0.8)..
Table 17: Comparing simulation results for a five persons household.
Case:
To storage:
(% of total water use)
From storage:
(% of total water use)
Rainwater
Rainwater (45%)
Rainwater (45%)
Toilet (33%)
Toilet + washing machine
Greywater
Closed
cycle
Wash.machine (15%)
Shower (11%)
Greywater (59%)
Greywater +
rainwater (104%)
Water savings for different storage volumes:
(m3/household/year (% of total water use))
0.2 m3
0.4 m3
0.6 m3
1.4 m3
17 (21%)
20 (25%)
21 (27%)
25 (31%)
20 (26%)
25 (32%)
27 (35%)
31 (40%)
(50%)
Toilet (33%)
Toilet (33%)
Total water use (100%)
Total water use (100%)
12 (15%)
9 (11%)
47 (59%)
66 (84%)
12 (15%)
9 (11%)
47 (59%)
70 (88%)
12 (15%)
9 (11%)
47 (59%)
72 (91%)
12 (15%)
9 (11%)
47 (59%)
75 (96%)
Simulation parameters: statistical data set, 5 persons household, wate ruse: 79 m3/ year, toilet: 5.5 litres/flush, washing
machine: 49 litre/wash, rainwater harvest: 35.44 m3/ year (data Lemmer 1999, roof area 50 m2, runoff coefficient 0.8,).
Table 18: Comparing simulation results for a household with two people both working 40 hr/week.
Case:
To storage:
(% of total water use)
From storage:
(% of total water use)
Rainwater
Rainwater (104%)
Rainwater (104%)
Toilet (28%)
Toilet + washing machine
Water savings for different storage volumes:
(m3/household/year (% of total water use))
0.2 m3
0.4 m3
0.6 m3
1.4 m3
8 (25%)
9 (28%)
10 (28%)
10 (28%)
10 (30%)
12 (35%)
13 (37%)
13 (38%)
(38%)
Greywater
Closed
cycle
Wash machine (10%)
Toilet (28%)
3 (10%)
3 (10%)
3 (10%)
3 (10%)
Shower (39%)
Toilet (28%)
9 (28%)
10 (28%)
10 (28%)
10 (28%)
Greywater (43%)
Total water use (100%)
15 (43%)
15 (43%)
15 (43%)
15 (43%)
Greywater +
Total water use (100%)
28 (83%)
30 (90%)
32 (94%)
34
rainwater (147%)
(100%)
Simulation parameters: measured data, 2 persons household (Annelies & Frank), both working outdoors 40 hours a week,
water use: 34 m3/ year, toilet: flush interruptor (max. 9 litres/flush), washing machine: 49 litre/wash, rainwater harvest:
35.44 m3/ year (data Lemmer 1999, roof area 50 m2, runoff coefficient 0.8).
66
Sustainable wastewater treatment
5 Processing units: technology characterisation
Note that the figures in bold indicate the maximum conservation, which is limited by inflow
or outflow of the storage tank. It is for instance indicated that for an average water use a
storage tank of 1.4 m3 can replace 17 m3 drinking water per household per year with
rainwater. This is 29% of the total drinking water consumption, and the maximum for the
chosen systems as only the toilet is connected to the rainwater system and the total yearly
flush water volume is supplied with rainwater out of the storage tank. For this case, a larger
storage tank volume will result in spare capacity only, not resulting in a larger volume of
water conserved.
Figure 14: Dynamics of water use for an average Dutch household with 2 persons.
Figure 15: Dynamics of water use measured in a Dutch household with 2 persons, A&F.
67
Sustainable wastewater treatment
5.3.2
5 Processing units: technology characterisation
Rainwater systems
Rainwater systems collect, filter and store rainwater to supply this water to the toilet and/or
washing machine. With additional treatment it is also possible to produce drinking water and
use the water for all domestic purposes, this is discussed in paragraph 5.2.1. The advantages
of rainwater are; the free supply and the relatively good quality (depending on roof surface
and local air pollution). The disadvantage is the unpredictable and often irregular supply. The
average rainfall in the Netherlands is 775 mm/year, and is available through out the year (see
Figure 16). This means that in the Netherlands approximately 26% of the domestic water use
could be covered by rainwater (assuming a roof area of 50 m2, a runoff coefficient of 0.8 and
no correction factor, and water use of 117.5 m3/(household*year)).
Figure 16: Rainfall measured in Limbricht, The Netherlands, in 1998.
Remarkable is that a small storage volume of a regular rainwater barrel (0.2 m3) can already
replace 13% of the yearly drinking water use by rainwater. These kinds of systems can be
easily installed and maintained by house-owners and will be very cost effective. Due to the
small size, these systems can be installed inside the house or at balconies or roofs if these are
strong enough to carry the weight. Furthermore, cleaning of the tank is easy. The question is,
however, how to ensure a fail proof and hygienic installation. The main issue is to avoid
contact between the rainwater and the drinking water to avoid contamination of drinking
water.
5.3.2.1
Water quality and hygiene
Rainwater is relatively pure, containing only soluble compounds and dust it collects from the
atmosphere. However, the water becomes contaminated when it comes in contact with the
roof, the rainwater gutter, and the storage tank. Most of the contamination is contained in the
first batch of rainwater, which is often rejected by rainwater collection systems28. The
28
These are the so-called first flush systems, where for instance the first rain falls through, as the rainwater filter has to be
wetted to become permeable.
68
Sustainable wastewater treatment
5 Processing units: technology characterisation
contamination depends on the materials used and the type of storage. The roof and rainwater
gutters may contain heavy metals that will dissolve in the rainwater. Furthermore, dirt, for
instance birds' faeces and leaves from trees, may accumulate on the roof or in the gutters and
form a source for pollution. The storage tank can be underground guaranteeing dark and cool
storage, or in the case of a gravity system the storage may be in-doors or in the open air at
higher temperatures.
The RIVM (Versteegh 1997, p.23) reported high infection risk (>10-2) for all purposes of
rainwater use (toilet flushing, washing machine, and outdoor tap). However, this conclusion is
based on one single scientific article. Holländer et al. (1996), analysed about 1,600 samples
from 102 water cisterns, and found that more than 95% of all samples met the quality
standards for bathing water set by the European Community. Therefore, they concluded that
the use of rainwater for toilet flushing, garden irrigation, and laundry washing presents no
unacceptable risk to public health. Similar, the conclusion of Haffmans et al. (1995, p.54) is
that the use of rainwater is safe with respect to pathogens, however, problems may be
accumulation of heavy metals in the sludge at the bottom of the tank and in traffic intensive
neighbourhoods PAH’s may become a problem. Haffmans et al. (1995, p52-53), based on
German and Austrian literature, state that washing with rainwater is safe as most pathogens in
the washing process originate from the laundry itself. Furthermore, most micro-organisms in
the rainwater storage tank are non-coliforms such as fungi, which are less threatening to
human health. These micro-organisms are mainly in the sludge that forms at the bottom of the
storage tank. Therefore, one should avoid mixing the tank's contents and have an outflow
above the bottom of the tank. Generally, the growth of bacteria is limited below 15°C and
above 55°C (SEV 1999, p.54). While the growth rate often increases at higher temperatures,
the persistence of bacteria is higher at lower temperatures. However, the main restricting
factor for growth of pathogens is probably the nutrient availability (Holländer et al.1996). For
instance, infective bacteria from animal faeces, such as Salmonella, Clampylobacter, and
Yersinia are unable to multiply in rainwater due to its low nutrient level (5 mg COD / l,
Krampitz and Holländer 1998/1999). Therefore, temperature of the rainwater storage may be
less important than the roof and rainwater filter, as these determine the nutrient inlet in the
storage (Nolde 1999, p.68). An advantage of larger storage facilities is that the longer
residence time of the water may result in higher die-off (Nolde 1999, p.68).
The main point of concern is to prevent mixing of rainwater and drinking water. Precautions
have to be taken, for instance through the use of different pipes, jackwalves or intermediate
decoupling tanks, to avoid interconnections of the two water systems.
5.3.2.2
Economic aspects of rainwater systems
In this research, we include two types of rainwater systems, namely (1) a small gravity system
and (2) a larger and more advanced pumped system. The small gravity system consists of a
350-litre storage tank, a simple plastic rainwater filter, taps, a second water level controller in
the flush water storage tank, and some pipes or hoses. This kind of system can be made from
components available at the do-it-yourself-stores for approximately 120 Euro. While these
systems are commercially available for 500 Euro (excluding installation costs of
approximately 1,000 euro/system, Haffmans 1995, p34, and HEWA 2001). The more
advanced system consists of a large underground storage tank (2 to 3 m3), a stainless steal
rainwater filter, pumps, level controllers, switches, pipes, and possibly a display. This system
is commercial available at the costs of 2,000 to 5,500 Euro with expected maintenance costs
of 15 to 40 Euro/year (Bie 2000, Herrmann 1997, Wouterse 1999, p.47). In projects (20 to
600 houses), prices are typically lower, between 600 and 2000 Euro (Jacobs 1997).
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Sustainable wastewater treatment
5 Processing units: technology characterisation
Communal systems for 6 houses are estimated to cost 1300 and 1700 Euro/household
(Wouterse 1999, p.45). Operation costs can be very low especially when no pumps are
involved.
Which system to chose depends on rainwater availability, the size of the household, and the
end user’s preferences. Simulations show that with the favourable weather conditions in the
Netherlands small rainwater systems can achieve relative large drinking water savings. For
instance a system with a 1.4 m3 storage volume can achieve maximum water conservation.
While smaller system (0.2 m3 storage) can already conserve 20% of the total drinking water
used (see Table 16, 19 and 20). In cases where a significant amount of the personal water
demand is fulfilled outside the house, for instance at work, sports facilities, restaurants, etc.,
small systems (0.4 m3) can cover the total flush water need in the household (see Table 20).
5.3.2.3
Social and cultural aspects
Although, there is a widespread idea that it is a waste to flush toilets with drinking water,
penetration of rainwater systems is low. A reason for this may be a lack of institutional
backing, for drinking water companies decentralised systems are probably difficult to manage.
As the house owner is by Dutch law responsible for the in-house water distribution.
Rainwater is, according to the Dutch law, a free commodity; rainwater falling on your
property can be collected and becomes your property. A more workable approach could be
promoting rainwater systems through the Dutch building directive (Bouwbesluit, article 69).
Figure 17: Fluctuating water level in rainwater storage tank (350 l, for a 2 persons
household, both working 40 hr/week, see also Table 18).
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That now prescribes a drinking water connection for the toilets and washing machines, but
permits equally good solutions. There is a Dutch norm on safety, prohibiting direct
connections between alternative water systems and drinking water supply and prescribing
clearly marked pipes (NEN 100629). Another Dutch safety regulation exists, namely the test
and certificate for technical safety of rainwater systems by the government (KIWA)(SEV
1999, p.64).
Dutch housing associations, municipalities, and project developers, expressed a positive
attitude towards rainwater systems in a telephonic enquiry. Rainwater utilisation in
households was acceptable for 84% of the respondents (333 respondents = 100%, Wouterse
1999). Utilisation for garden use scored highest (98%), next was toilet flushing (93%),
washing machine (42%), and shower (26%). Remarkable is that 34% indicated that it would
be acceptable to them to use rainwater for in-house drinking water production with additional
purification step (Wouterse 1999, p.93). Furthermore, 75% of the respondents indicated that a
30% savings of drinking water would already justify the installation of a rainwater system. If
the rainwater system costs less than 1,000 euro, this would be acceptable to 44% of the
respondents. On the question what secondary water quality systems one would prefer, the
answers were as follow; a greywater system 11%, a collective rainwater system 23%, a dual
piped supply 28%, and an individual rainwater system 38% (Wouterse 1999, p.94).
5.3.2.4
Rainwater systems in the decision support tool
In the decision support tool, two types of rainwater systems are included; (1) a small-scale
gravity system with a 0.35 m3 storage tank, and (2) a more advanced pumped system with a
3m3 underground storage tank. The small-scale system substitutes approximately 65 to 90%
of the drinking water used for toilet flushing and or washing machine (assumptions 50m2
roof, household 2 to 4 people, all water used at home, water saving toilet with on average 4.5
litres flush, and washing machine using 39 l/wash). This implies that approximately 35 to
55% of the total rainwater that can be collected by the roof is utilised. For the more advanced
system, the total rainwater volume collected can be utilised. Therefore, the rainwater available
and the water demand are limiting for the volume used, not the storage tank volume as in the
case of the small-scale rainwater system.
5.3.3 Greywater systems
When rainwater is not available, for instance in dry climates or densely populated areas, one
can consider a greywater system to reuse the lightly polluted wastewater inside the household.
The advantage of reusing greywater is that the supply is regular. The disadvantage is,
however, that the water quality is less than rainwater and treatment is needed to remove
pathogens, soap, and dirt. Simulations show that in this case storage volumes can even be
smaller than water used for the shower and washing machine are supplied regularly in
quantities similar to the flush water volumes needed for the toilet (see Table 16, 17, and 18).
In some cases wastewater from the kitchen is not recycled as it contains more contaminants
than the rest of the greywater. Also wastewater from the washing machine may be excluded
from reuse as pathogen concentrations can be high, for instance in the case were baby diapers
are washed (Nolde 2000, p.279).
The social-cultural acceptance differs per culture. While greywater systems are applied by
governmental directive rule in Japan (Ogshi et al. 2000), other governments are reserved,
29
In Germany, rainwater systems have to comply with the DIN-standards 1988 and 1986.
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5 Processing units: technology characterisation
arguing that public health is at stake (Dutch (VROM) and German in Nolde 1999, p.275). In
the meantime, two thirds of the experts questioned on “water technology in the year 2010”,
considered greywater systems technically feasible without public health risks (Delphi 1999, in
Nolde 2000).
Greywater treatment technologies that are included in the decision support tool are:
membrane bioreactor, rotating bio-discs, trickling filters, constructed wetlands, UVdisinfection, and infiltration systems. These technologies are described in paragraph 5.6 on
Wastewater treatment.
5.3.4
Closed water systems
When water is extremely scarce one may consider a ‘closed water’ system, approaching the
life-support system of a spaceship. All domestic wastewater is collected and treated to meet
drinking water quality enabling reuse for all purposes. Technically, it is possible to recycle
also the water contained in urine and faeces, however, if it is possible to make up for small
losses30 this maybe preferred, as the value of urine and faeces is not in water content but in
nutrient content. Evaporation losses may be recovered through air conditioning. In extreme
cases, as for instance in a spaceship, personal hygiene can be almost dry using dry pads for
body hygiene31.
Technologies that can be applied in a domestic closed water system are similar to those used
for greywater recycling (for instance constructed wetlands, membrane-bioreactors),
wastewater treatment (anaerobic digestion and aerobic treatment) and drinking water
production (membrane filtration, disinfection, etc.). For space missions, high tech solutions
such as vapour compression distillation and reverse osmosis are considered for the recovery
of water from urine and faeces (Saulmon 1996). In short-term missions, however, human
wastes are usually stored for post flight treatment or disposed using an overboard-vacuumsystem.
On long term space missions it will be necessary to close not only the water cycle but also the
food cycle, for this purpose bio-regenerative life support systems that are being designed
(Blüm 1995, Nelson 1994, Parker 1973, Saulmon 1996). The only32 example of full-scale
implementation is Biosphere 2 (Nelson 1994). In Biosphere 2, the complete regeneration of
human and animal waste products is accomplished by an in-vessel composting system for
inedible crop residues and animal manure, and by a created wetlands system for handling of
human wastes. The aquatic waste treatment system consists of anaerobic holding tanks
followed by aerobic mash lagoons. The plants used in these lagoon systems are fast growing
and are periodically cut for fodder and used in composting. After passing through the marsh
waste treatment system, the water is added to irrigation supply for agricultural crops, thus
utilizing the remaining nutrients (Nelson 1994). It is of course nearly impossible to engineer a
stable closed ecosystem. The Biosphere 2 experiment, where 8 people lived in the closed
ecosystem for 2 years, fell short in food production and oxygen. During the experiment 19 of
the 25 animal species had become extinct and many plant species were dying (Scholten 1993).
30
Normally, approximately 2 to 3 litres water per capita per day is consumed and approximately 10% of the water used in
washing and cleaning evaporates (Kilian 1996).
31
Even showering in space could be done with extremely little water (approximately 1.5 litre) as the water forms a thin film
around the body due to lack of gravity (Parker 1973).
32
In the Russian experiment, Bios-3, relative large amounts of food were imported and some waste was exported.
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To close the domestic water and nutrients cycles it is not necessary to create a closed
ecosystem, this is for instance demonstrated by integrated farming systems and living
machines. In these artificial aquatic ecosystems, wastewater treatment and food production is
combined by growing fish and plants in anaerobic pre-treated wastewater (Blüm 1995).
The technologies that can be applied in closed water system are discussed in paragraph 5.6 on
Wastewater treatment.
5.4 Sanitation
In this section, we discuss different toilet system and the consequences for wastewater
treatment.
Picture 3: Different types of toilets; Lady P the women’s urinal by
Sphinx (4 litres/flush), double flush by Gustavsberg (2 or 4
litres/flush), and the urine separation toilet by Dubletten (0.2 litres
flush for urine, 4 litres for faeces).
5.4.1
Different types of water closets
In many cultures water is associated with hygiene and there is no doubt that water flush toilets
are comfortable and hygienic for the user. However, the technology has its draw back as well,
the pathogens are diluted and may, if not treated well, spread in the environment and become
a threat to drinking water supply. Furthermore, valuable resources in the excreta such as
nutrients are also diluted and probably lost for reuse. Conventional flush toilets use large
amounts of water. For more information on the water flush toilets, see Table 15 on page 64,
and Picture 3. The following sections discuss composting toilets, dry toilets, and urine
separation toilets.
5.4.1.1
Toilets in the decision support tool:
Water flush toilets that are included in the tool are: (1) an old toilet (9 to 12 l/flush), (2) a new
toilet (6 to 9 l/flush), (3) a toilet with flush interrupter (6 to 9 for faeces and 3 l/flush for
urine), (4) low flush toilet (0.8 to 1.5 l/flush). Details on the different types of toilets are
included in the decision support system are given in the Technology Data Sheets 4 to 9 in
Appendix 3.
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5 Processing units: technology characterisation
Composting toilets
Composting toilets are either low flush or dry toilets. A wide range of composting toilets is
commercially available. One of the main differences between composting toilets is the
handling of urine and flush water. The urine is sometimes separated in the toilet (for instance
in the Dubletten system), or collected with the flush water before entering the composting
chamber (as done in the Aquatron systems). In other systems the moisture leaches through the
composting chamber (sometimes referred to as tea, see for instance Ecolet), the moisture can
be actively evaporated (see Sun-Mar), or in the case of no flush toilets the urine can be kept in
the composting chamber (Clivus Multrum). Separating the urine improves conditions for
composting by decreasing the moisture content and increasing the C:N ratio (see also
paragraph 5.6.3). This can also be obtained by adding more bulking agent and/or evaporation
of the moisture. Most composting toilets have different compartments or are built with a slope
to move the compost in a plug flow to make sure that first in gets first out.
Simple composting toilets can be built by the user him or herself (see Winblad 1985). More
complex toilets include ventilation, heating, and sometimes a rotating drum system for mixing
(Sun-Mar Centrex three chamber system), or an UV-disinfection unit for the separated flush
water (Aquatron). Depending on climate, height of water table, frequency of use, availability
of water, electricity, and budget, an appropriate system can be chosen.
Composting toilets provide hygienic sanitation, are low in resource use, and enabling safe
reuse of the valuable nutrients in the excreta. In the composting process, micro-organisms and
heat break down the waste to 10 to 30% of its original volume (NSFC 1998). According to a
limited number of measurements faecal coliforms range from undetectable to 35 per gram
compost of (CM 2000, NSF’s standard is < 200 /g, Ekolet 2000). To conserve nutrients there
are two strategies (1) separate urine and use it as fertiliser, and (2) keep the leachate with the
compost and control the C:N ratio optimising the composting process and consequently
minimising the nitrogen losses (see paragraph 5.6.3).
Prices of commercial composting toilets range from 1,000 to 6,000 Euro for household
systems (NSFC 1998, Burkhard et al. 2000, and product information manufacturers).
Depending on the system, size, and usage the compost compartment has to be emptied every
month up to once every 2 years (Naturum product info, NSFC 1998).
Energy use for electric ventilation is about 22 to 330 kWh/y33, and for heating about 880 to
1750 kWh/y34, or even higher when all excess liquid is evaporated.
Space requirement varies with system, for instance the Naturum toilet is relatively compact,
merely a big toilet, while systems such as the Clivus Multrum has a relatively large
composting chamber of 2 to 3 m2. This composting chamber may be placed in ‘low quality’
space such as a basement, as long as it is placed straight underneath the toilet. This makes it
unattractive to place a larger number of toilets, for instance in a high rise building as each
toilet needs it own straight down pipe to the composting chamber. Low-flush systems offer
greater freedom with respect to placing the composting chamber as the waste is flushed and
therefore, turns in pipes are no problem. Low flush systems do however require another
33
For instance: 2.6 Watt Naturum, 166 kWh/y for Separett Villa 6000, 21 to 37 Watt in Fittschen et al. 1997.
For instance: 100 to 200 Watt in Fittschen 1997, 1000 kWh/y Lange 1997 (fan + heating), Sun-Mar Excel 150 Watt fan +
heating.
34
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5 Processing units: technology characterisation
solution for moisture control, for instance urine separation, leaching, or drying through
heating (see 5.4.4 Urine separating toilets).
Social-culturally composting toilets are often disputed, especially in regions where water is
plenty and the sewerage system is present. Some see the composting toilet as a low-tech
uncomfortable solution for remote areas, while other perceive the toilet as the most
environmental friendly solution for sanitation. In cultures, where water is strongly associated
with hygiene, or where seeing the faeces is a taboo, the conventional no flush composting
toilet may be unacceptable. Furthermore, problems such as flies, smell, and disturbances of
the composting process resulting in too wet compost strengthened the public perception that
this technology is still experimental and alternative (Huizinga 1993, GD 2001). In the
Swedish eco-village Toarp, urine separating composting toilets were removed because the
energy use for heating was perceived un-ecological, furthermore people were uncomfortable
in handling the waste and about informing their guest how to use the toilet (Fittschen et al.
1997).
5.4.2.1
Composting toilets in the decision support tool:
Two composting toilets are incorporate in the decision support model, (1) a low flush urine
separating composting toilet and (2) a conventional dry composting toilet that does not
require electricity for mixing or heating. Details on these systems are summarised in the
Technology Data Sheets 5 and 6 (see Appendix 3).
5.4.3
Dry toilets
There is a variety of dry toilets on the market, for instance pit latrines, chemical toilets,
composting toilets, and toilets that burn the waste. However, in this paragraph we focus on
toilets that use the sun to dry the excreta. In this type of toilet urine is separated and possibly
stored or dried by mixing it with dust, or diluted with treated greywater and directly used for
irrigation. Faeces are stored in a closed compartment of the toilet, where a solar heater is used
to dry the faeces completely. Due to the heat, retention time and dry conditions most
pathogens will be eliminated, although worm eggs and spores may remain. The dried faeces
can be used as soil conditioner.
5.4.3.1
Dry toilet in the decision support tool:
In the decision support tool a solar drying toilet can be implemented for single households,
though in hot dry climates only. The cost of this type of toilet is estimated 600 US$ per
household, which is twice the cost of a VIP latrine as reported by Loetscher (1999, p.147).
5.4.4
Urine separating toilets
About 80% of the nitrogen, 50% of the phosphorus, and 60% of the potassium in domestic
wastewater originates from human urine (Czemiel 2000, Fittschen et al. 1998, Hellström et al.
1999, Larsen et al. 1996). Therefore, an effective way to recycle nutrients is the separate
collection of urine for use as fertiliser in agriculture. An additional advantage is that the
nutrients are provided suitable for uptake by crops, nitrogen as urea, phosphorus as
orthophosphate, and potassium as ionic potassium. Furthermore, urine is almost free from
pathogens and heavy metals (Drangert 1997, Jönsson et al. 1998).
To utilise these nutrients in agriculture urine has to be collected, stored, and transported to the
field. Special toilets are designed for separate urine collection as shown in Picture 3. This
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5 Processing units: technology characterisation
toilet may have some social-cultural implications, as it is different in use than ‘normal’ toilets
(Fittschen et al. 1997). Another option for urine separation is the use of urinals, or to collect
the urine less pure though by separating it just after the toilet.
Local or centralised storage are both feasible options, both require transport of urine. For
centralised storage separate sewers for urine are required, or in some cases flushing the urine
through nearly empty sewers overnight maybe an option (Larsen et al. 1996). There are two
main reasons for storage of urine; (1) to wait for the growing season when fertilisers are
demanded in agriculture, (2) to facilitate natural die-off of pathogens and possibly break down
of hormones and traces of medicines that are present in urine. In temperate regions, this
implies that urine has to be stored during winter season (0.5 – 1 m3 per person) (Adamsson
1996). The disadvantage of long-term storage is that urea35, the dominant nitrogen source in
urine, decomposes into ammonia (Jönsson et al. 1998). Ammonia is more toxic and may as a
gas ‘escape’ resulting in a nitrogen loss. Besides storage, treatment of urine maybe
considered, options described in literature are: precipitation of MgNH4PO4 (struvite),
stripping of NH3 gas, concentration of urine by freezing (Lind et al. 2001), or drying of urine
(Hellström et al. 1999, Larsen et al. 1996, Otterpohl 2000).
5.4.4.1
Urine separation in the decision support tool:
The options for urine separation in the decision support model consist of a separating toilet or
a normal toilet and a urinal, and long-term storage of urine as treatment before utilisation of
the nutrients in agriculture (Technology Data Sheets 8 and 27 in Appendix 3).
5.5 Wastewater transport
5.5.1
Conventional sewer
Sewerage is in many cases perceived as indispensable as it hygienically and conveniently
transports our wastewater and also prevents flooding. However, sewerage is expensive,
Otterpohl (2000, p.3) estimates the costs for sewerage systems to be approximately 70% of
the total wastewater transport and treatment costs in more densely populated rural and periurban areas in Germany. Furthermore, during heavy rains the sewerage systems cannot cope
with the large amounts of water and diluted wastewater is discharged directly into surface
water, the so-called combined sewer overflows (CSO). To reduce pollution from combined
storm water overflows, storm water and wastewater can be transported separately. In rural and
peri-urban it may possible to infiltrate storm water. Where surface water is nearby, the storm
water may be discharged directly into this surface water. It is also possible to use a so-called
improved separated system, here the first flush of the storm water carrying most of the
pollutants is directed to the wastewater treatments system while the rest of the storm water is
infiltrated or directed to surface water. Another option to reduce combined storm water
overflows is to include extra storage capacity in the sewers and/or at the treatment plant to
store the water during heavy rains.
Costs of sewerage systems listed in Dutch literature range between 104 and 390 Euro/m, see
Table 19.
35
Approximately 50 to 90% of the nitrogen in human urine is contained in urea, depending on the proteins in the
diet, (Adamsson 1996).
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5.5.1.1
5 Processing units: technology characterisation
Sewerage systems in the decision support tool
In the decision support tool it is possible to chose for combined, separate, or vacuum
sewerage systems. More details on sewerage systems are summarised on the technology data
sheet 12 (see Appendix 3).
5.5.2
Vacuum sewer
Vacuum sewers are commonly used in boats, airplanes, and trains. These are cases where the
reduced water use and the smaller diameter of the pipe are decisive advantages. Other
advantages are that wastewater can relatively easily be transported upwards and that in case of
leakage water is sucked into the pipe rather than wastewater flowing into the environment.
Potential disadvantages are energy use, and the need for separate systems for large flows such
as rainwater.
Vacuum system can be operated in any size. Small systems of 4 to 5 toilets can be feasible in
special situations, for instance when the toilet are situated under the water level, when water
saving is a priority issue, or when weight and space occupation are major issues. The most
frequently used system however is for approximately 100 people. The pump for this system
uses about 4 kW, and is only running to make up for vacuum ‘losses’ occurring during
flushing. Estimated energy use ranges 25 and 117 kWh per person per year (QUA-VAC 2000,
Otterpohl 2000, p.13, and Otterpohl 1997).
5.5.2.1
Vacuum sewers in the decision support tool
In the decision support tool vacuum sewerage is one of the transport options (Technology
Data Sheet 12 in Appendix 3).
Table 19: Costs indication of different sewerage systems in the Netherlands.
System
Gravity sewer mixed
Gravity sewer separated
Gravity sewer improved
separated
Pressure sewer
Vacuum sewer
Storage settling sewer
Investmenta
(Euro/m)
153 – 213
Depreciation and
maintenancea
(Euro/m/y)
3.7 – 4.5
60 –93
60 – 110
1.8 – 2.4
3.3 – 4.5
Storage settling basin
Investmentb
Maintenanceb
(Euro/m)
270c
365c
390c
(Euro/m/y)
3.4
4.5
5.0
(Euro/m3)
800 – 1,900
(Euro/m3)
400 – 2,300
approx. 5%
of investment
(a) Assuming: 150 m sewer per plot, 170 plots, no interest is taken into account, original figures per plot,
source: RIONED 1998.
(b) Maintenance is approximately 25 to 30% of depreciation and maintenance, average sewer length per
household 10 m (note original costs given per household), very few data available, costs separated
1.35*costs mixed, improved separated costs are 1.24* costs mixed, source RIONED 1996.
(c) In Geldof et al. 1997b, pages 23-27 investments: mixed 2,700, separated 3,600 and improved separated
4,100 Euro/household, 50 years lifetime and for improves separated system still 70 to 80% of rainwater
to wastewater treatment. Note: original figures in Dutch Guilders, 1 Euro = 2.20371 NLG.
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5.5.3
5 Processing units: technology characterisation
Truck
In cases where sewerage is not available, wastewater can be treated onsite and transport of
products such as sludge or urine to centralised treatment or reuse can be done by truck. The
amount of fossil fuels used for sludge transport is estimated to be: 0.8*10-3 kWh/kg/km and
for urine spreading: 0.46 kWh/m3/km (Bengtsson et al. 1997, pp.17+20).
5.5.3.1
Truck transport in the decision support tool
In the decision support tool truck transport is a possibility for sludge and urine that are stored
on-site (Technology Data Sheet 13 in Appendix 3).
Table 20: Overview infiltration systems (RIONED 1998).
Size
Land are
Investment
Energy
COD/BOD
TN
TP
(pe)
(m2)
(Euro/house)
(Euro/ houses
for clusters)
(kWh/y)
(%)
(%)
(%)
Filtration
System
5, 10. 20 or 50
4-5, 8-10, 16-20, 4050
3,600-7,300
for 50 pe:
< 21,300
350 - 1325
85-90 / 90-95
60 – 90
Infiltration field
5, 10. 20 or 50
350 - 1325
90 / 90
10 – 15
Constructed
wetlanda
5, 10. 20 or 50
15-25, 30-50, 60-100,
or 150-200
3,600 – 7,300
for 50 pe:
< 21,300
350 - 1325
90-99 / 80-95
70 – 90
85 – 90
(a) Vertical flow, note that according to this study, horizontal flow wetlands are larger and more expensive.
Investment costs include installation and septic tank.
Note: original cost data in Dutch guilders, 1 Euro = 2.20371 NGL.
5.5.4
Onsite disposal
Onsite disposal is only permitted if wastewater is treated such that it can be infiltrated, reused
or discharged. Options for treatment and reuse are discussed in paragraph 5.6 and 5.3, in this
paragraph we shortly address infiltration. Technologies that can be used for infiltration are
swales (in Dutch wadi’s), infiltration ditches or trenches, soak away pits, and infiltration
fields. Information on these systems is summarised in Table 20 and technology data sheet 14
(see Appendix 3).
5.5.4.1
Infiltration in the decision support tool
In the decision support system infiltration is an option for rainwater and/or treated
wastewater. It is obvious that local soil conditions and ground water level are crucial for
implementing infiltration systems.
5.6 Wastewater treatment
5.6.1
Activated sludge
Activated sludge is an aerobic treatment process in which the organic substances in the
wastewater are degraded by micro-organisms in the presence of oxygen. Naturally occurring
micro-organisms convert soluble and colloidal material into a dense microbial mass that can
readily be separated from the purified liquid using conventional sedimentation. The
breakdown of organic matter relies on two processes, (1) oxidation or respiration forming a
mineral end product, and (2) syntheses producing new biomass.
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Generally, two types of processes can be distinguished: suspended growth and attached
growth. In suspended growth processes the micro-organisms are present in suspension, for
instance in activated sludge, aerated lagoons, sequencing batch reactors, and the aerobic
digestion process. In attached growth processes, the micro-organisms grow on an inert
medium such as rock, ceramics, or plastic, for instance in trickling or percolating filters,
roughing filters, rotating biological contactors, and fixed film nitrification reactors.
In this paragraph we discuss activated sludge, an aerobic suspended growth process. Attached
growth processes such as fixed bed reactors, rotating biological contactors, and trickling
filters are discussed in the paragraphs 5.6.5, 5.6.7 and 5.6.10.
Important in activated sludge is the adequate mixing and aeration, to sustain the high
concentration of suspended sludge, which is a mixture of micro-organisms and the organic
matter or substrate that is serving as food. The degradation of the organic matter occurs
through adsorption on the micro-flocs, assimilation (conversion into new microbial cell
material), and mineralisation (complete oxidation). The recycling of a large proportion of the
biomass is an important characteristic of the activated sludge process. This makes the mean
cell residence time (i.e. sludge age) much larger than the hydraulic retention time, maintaining
a large number of micro-organisms to effectively oxidise organic compounds in a relatively
short time (detention time aeration tank: 4 to 8 h, Bitton 1999, p.182).
Most virus particles (>90%) are solids-associated and ultimately transferred to sludge;
however, viruses are also inactivated in the process (Bitton 1999, p.203). Removal rates
measured in field studies are: 90 to 99% removal of enteric viruses (Bitton 1999, p.203). The
removal rates for bacteria are about 80 to 99% (Bitton 1999, p.202). Removal of parasitic
protozoa such as Giardia cysts is > 98%, and for Cryptosporidium Parvum occyst 80 to 84%
for the activated sludge process, up to 96.8% for the total plant (Bitton 1999, p.204).
Helminth eggs are removed during sedimentation, although generally parasite eggs are not
detectable in the effluent, some investigators reported Ascaris and Toxocare eggs in effluents
of activated sludge plants (Bitton 1999, pp.206).
Nitrification in activated sludge processes depends on the growth rate of nitrifiers, which is
lower than that of heterotrophs in sewerage, therefore, a high sludge age (> 4 days) is
necessary for the conversion of ammonia to nitrate (Hawkes 1983 in Bitton 1999, p.197).
Nitrification proceeds well in a two-stage activated sludge system where BOD is removed in
the first stage, while nitrifiers are active in the second stage (Bitton 1999, p.197). The next
step, denitrification, the removal of nitrogen by conversion to nitrogen gas, is accomplished
under anoxic conditions. Denitrification, is often restricted by the concentration of biomass
and electro-donor (methanol or ammonium) in the wastewater, as the nitrate concentration in
mostly abundant. Due to the required anoxic conditions denitrification is accomplished in
another reactor or another reactor zone than nitrification.
For the aerobic processes, oxygen is supplied mostly through active aeration by blowing or
mixing air into the wastewater. Such aeration processes are the major energy consumers of the
activated sludge process. Typical power requirements for maintaining a completely mixed
flow regime with mechanical aerators vary from 19 to 39 kWh/103 m3, depending on the
design of the aerator and the geometry of the tank (Crites 1998, p.463, Nowak 1999).
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5 Processing units: technology characterisation
Land requirements depend on the scale of the activated sludge systems. Van Nieuwenhuijzen
(2000), for instance, reports a land use of 0.02 m2/pe for a 100,000 pe system while a 5 pe
system may require 0.4 m2/pe (see Table 28 on page 87, and Table 32 on page 91).
Investment costs for large-scale activated sludge systems are estimated to be 21 Euro/pe/y,
and maintenance 15 Euro/pe/y for operation (Nowak 1999). While smaller scale systems for
instance a 10 pe system costs approximately 460 Euro/pe, see also Table 21 (Alexandre 2000,
Geenens and Thoeye 2000).
Table 21: Approximate costs of small-scale treatment systems in Belgium (Geenens 2000).
Activated sludge*
Rotating biological contactor
Submerged aerated filter
Trickling filter
5 pe
Euro
4000
5800
3400
5800
10 pe
Euro
4600
7400
4100
-
20 pe
Euro
8300
8600
6000
-
O&M
Euro/pe/y
97
108
* for 1,000 to 2,000 pe systems, investments costs are approximately 200 to 350
Euro/pe.
Table 22: Summary of different process conditions in anaerobic treatment (Oldenburg 1997).
Process
condition:
Process set-up
Variations:
Advantages & disadvantages:
§ Standards rate or single state
+ simple process, can handle slow degradable
substrates, lower investment costs
- larger digester volume, instable if C/N<15
+ smaller digester volume, more stable process
- higher variable costs, more process control
+ simple processing
+ possibility to employ high performance
reactors in the second state (for instance
UASB)
- more expensive reactors, possible failure when
phase separation is not sufficient
§ High rate or two (multiple) state
Phase
separation
§ Single phase
§ Solid and liquid phase separated
after hydrolysis
Temperature
§ Psychrophyl (<25°C, optim. 10°C)
§ Mesophyl (35-38 °C)
§ Thermophyl (55°C)
Water content
substrate
§ Dry digestion (TS>25%)
§ Wet digestion (TS<12-15%)
Mixing
§ Mixed
Hygienasation
§ Mixed by biogas production
§ Plug-flow
§ In digester (thermophyl)
§ Pre-hygienasation
- low gas yield
+ low energy input, stable process
+ higher yield, higher CH4-content (Converti 99)
- higher energy input, higher NH3 production
+ volume efficient use of digester
- higher expenses filling and emptying reactor
+ commonly used technology
- higher expenses preparing substrate
+ many variants possible
- higher energy input
+/- horizontal mixing difficult
+ short circuiting impossible
+ no extra costs
- uncertain effect in relation with retention time
and in case of short circuiting
+ controllable process, mesophilic digestion
possible, some hydrolyse and mixing takes
place
- extra costs, extra energy input
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Sustainable wastewater treatment
5 Processing units: technology characterisation
Aerobic treatment is widely accepted. Activated sludge is probably the most common process
for centralised wastewater treatment plants. The process requires quite some expertise for
design as well as for operation. Participation of the end user is limited. The mostly large scale
of the process makes it quite robust, however, the process heavily depends on operation and
control. This technology does not by itself stimulate sustainable behaviour, especially when
implemented on large scale. In combination with sewerage systems, it makes people unaware
of problems associated with wastewater, and in way stimulates misuse through flushing waste
that cannot be treated such as chemicals, condoms, tampons, etc.
5.6.1.1
Activated sludge in the decision support tool
In the decision support tool, activated sludge is included at both household, neighbourhood
and large-scale (namely: 5, 50, 400, 1,000, and 100,000 pe, see Data Sheet 15 in Appendix 3).
5.6.2
Anaerobic digestion
Anaerobic digestion involves decomposition of organic and inorganic matter in the absence of
molecular oxygen. The main advantages of anaerobic digestion are: methane production, low
sludge production, highly stabilised sludge, low operational costs, and low nutrient
requirement due to the lower growth rate of anaerobes, and the production of mineralised
compounds (NH4+, PO43-, S2-) (Gray 1999, p.443). While the main disadvantages are: low
sludge activity, low reactor capacity, high capital costs, energy use for heating, long retention
times, the need for post-treatment (high residual BOD, COD, N, and P), inhibitory effects,
and the production of malodorous compounds (H2S) (Gray 199, p.443). For different process
condition see Table 22.
Anaerobic digestion mainly removes carbon. Nitrogen is generally released as ammonium and
sulphur as H2S-gas. However, the emission of H2S is reduced if the waste contains ferrous
compounds (precipitation chemicals) forming FeS.
The normal yield of biogas on per capita basis is 0.015 to 0.022 m3/(cap*d) in primary
treatment plants for domestic wastewater, and about 0.028 m3/(cap*d) for secondary sludge
digestion (Metcalf 1979, p.825, Gray 1999, p.454). Approximately 65% of this gas is
methane, therefore, the low heating value of the gas is approximately 6.4 kWh/m3 (Gray
1999, p.454, Metcalf 1979, p.826). Otterpohl et al. (1997) calculated an energy production of
43 kWh per person per year for the total system for Flintenbreite in Lübeck Germany,
including deduction of energy use for greywater treatment in a constructed wetland, and
blackwater treatment using vacuum toilets, vacuum sewers and anaerobic co-digestion of the
excreta and biowaste (biogas yield: 110 kWh/(cap*y)).
5.6.2.1
Anaerobic digestion in the decision support tool:
In the decision support tool we include anaerobic digestion for wastewater and sludge
treatment both as large-scale and small-scale process, namely on 5, 40, and 50,000 pe, see
Technological Data Sheet 16 in Appendix 3.
5.6.3
Composting
Composting is a stabilisation process used for organic wastes such as sewage sludge, animal
and agricultural wastes, and household refuse. Composting systems are aerobic, therefore, the
main products are stabilised sludge, water, carbon dioxide, and heat. The most important
81
Sustainable wastewater treatment
5 Processing units: technology characterisation
operational factors are aeration, detention time, temperature, moisture, carbon-nitrogen (C:N)
ratio, and pH. The overall volume reduction is 30 to 35% (Bengtsson et al. 1997, p.44).
Heat inactivation is one of the main factors in pathogen removal in composting, therefore, the
main challenge is to make sure that all material is exposed to significant high temperatures
(>50°C) for certain time (>10 days) during the composting process. Composting can reduce
virus concentration to below their detection limit (0.25PFU/g ds, Haug 1993, p.192). Also
pathogenic bacteria, Ascaris eggs, and other helminths eggs can be eliminated effectively in
composting. However, regrowth of bacteria such as faecal coliforms and Salmonella may
occur.
The energy use of composting ranges between 7.5 and 93 kWh/(cap*y) depending on size and
type of process (Bengtsson et al. 1997, p.42, 44, Kärrman 2000 p.89, 151). Due to the long
retention times (7 to 14 weeks and up to two years for composting toilets) relatively large
amounts of space are required.
Social acceptance can play an important role in the decision to apply of composting. If the
waste to be composted is biodegradable kitchen and garden waste, people have to participate
by separating waste inside the household. This is mostly no problem except for densely
populated urban areas were people live in high-rise building with no possibility to store the
bio-waste outside. When sewage sludge is composted the acceptance lays at the other side of
the chain, namely the farmers to whom the compost is offered as a soil conditioner. The
farmer has to be convinced that the compost is safe with respect to pathogens, heavy metals,
etc.
5.6.3.1
Composting in the decision support tool
In the decision support tool we include two composting processes (1) small-scale composting
in composting toilets and (2) large-scale composting as sludge treatment, see Technological
Data Sheets 5, 6, and 17 in Appendix 3.
5.6.4
Constructed wetlands
Generally, two types of wetlands are distinguished: free-water surface (FWS) wetlands and
subsurface flow wetlands (SF), see Figure 18. For the subsurface flow wetland there are 2
possible feeding orientations: from one side (horizontal flow) or from just below the soil
surface (vertical flow). Horizontal flow beds are fed continuously, while vertical flow is fed
intermittently, such that the bed dries for short periods allowing oxygen to penetrate into the
bed.
Wetland systems can effectively treat high levels of biodegradable compounds (BOD),
suspended solids (SS), and nitrogen, as well as significant levels of metals, trace organics and
pathogens. Phosphorus removal is minimal due to the limited contact opportunities with the
soil. Basic treatment mechanisms are sedimentation, chemical precipitation, and adsorption,
and microbial interactions (BOD and Nitrogen), as well as some uptake by the vegetation
(Reed 1995, p186). Performance of wetlands is summarised in Table 23.
Removal of pathogens in wetland systems is due to natural die-off, predation, filtration,
sedimentation, adsorption, excretion of antibiotics from roots of plants, and solar radiation
(FWS type only). Removal of pathogens in a FWS-wetland was measured to be 95% of the
faecal coliforms and 92% of the viruses, for a SF-wetland the removal was >98 and >99%
82
Sustainable wastewater treatment
5 Processing units: technology characterisation
respectively. There does not appear to be any consistent seasonal effect on removal
performance, since the initial removal is due to physical separation even though the actual
die-off is temperature dependent. A long hydraulic detention time and effective settling and
minimise the risk of parasitic infection from effluents, some risk may occur when removing
the sludge for disposal. Sludge stabilisation, desiccation, and solar radiation can be used to
reduce the pathogens in the sludge (Reed 1995, p.72, 75, 199).
Nitrogen removal in wetlands can range up to 79%, of which probably less than 16% is
removed by plant uptake (Reed 1995, p.91). Although, research findings are not conclusive
about the significance of nutrient removal through plant up take, some researchers mark it as
insignificant while others conclude that it is the dominant pathway for nitrogen removal
(Billore et al. 1999, p.168). The organic nitrogen entering a wetland is typically associated
with particulate matter such as organic wastewater solids and/or algae. The initial removal of
this material as suspended solids is usually quite rapid. Much of this organic nitrogen then
undergoes decomposition or mineralisation and releases ammonia to the water. Plant detritus
and other naturally occurring organic materials in the wetlands can also be a source of organic
nitrogen, resulting in a seasonal release of ammonia as decomposition occurs (Reed 1995, p
191).
Geldof et al. 1997b, p.54 estimate the space requirements of SF-wetlands at 0.5 to 5 m2 per
person. In the Flintenbreite project, vertically fed constructed wetlands for greywater
treatment are constructed with sizes of 2 m2 per inhabitant, these could have been smaller 1
m2/capita but space was available (Otterpohl 2000, p.7 and 11). Although, wetlands do require
a relatively large surface area, the area is not completely lost as an aesthetic green space is
constructed that enhances biodiversity (plants and small animals) and the grown reeds can be
harvested.
Costs for constructed wetlands vary with size and the nature of the wastewater. Some data are
summarised in Table 24.
The social acceptance of wetland is generally good; as wetlands are natural systems that can
be integrated in the landscape and when carefully designed there will be no nuisance by smell
and insects. However, depending on the system and the wastewater treatment one should be
more or less careful not to perceive the wetland as an area for recreation as the systems
contain pathogens.
Figure 18: Constructed wetlands (left: subsurface flow, right: free water surface).
83
Sustainable wastewater treatment
5 Processing units: technology characterisation
Table 23: Purification efficiency of wetlands for the treatment of domestic wastewater.
Horizontal flow wetland
removal efficiency (%)
a
b
80
65
source:
Vertical flow wetland
removal efficiency (%)
c
d
96
99
Wetland general
removal efficiency (%)
e
f
> 95
90-99
BOD
66
73
40
34
COD
TSS
TN
NH4-N
NO3-N
TP
TC
Zn, Pb
32
99.7g
78
79
2
58
87
90
38
66
97
99
58
96
> 95
98
75
80-95
58
99-99.9h
40
>99%
90
80 – 95
80
(a) Based on 71 installations in Denmark, Scierup 1990 in Haberl 1995, (b) Billlore 1999, P is ortho, (c)
Average over 4 installations (Haberl 1995), (d) Hartjes and Geurts van Kessel 1996, (e) Geldof et al. 1997b,
p.54, (f) RIONED 1998b, p.22, (g) Ecoli removal at residence time of 48 h, Green 1997, (h) FC estimation
based on filters without plants, Buuren 1998
Table 24: Size, energy use, and investment costs for small wetlands (RIONED 1998b,
p.32, 39, *note investment includes septic tank as pre-treatment).
Type:
Vertical flow
Horizontal flow
5.6.4.1
Size:
(pe)
5
10
20
50
5
10
20
50
Size:
(m2)
15 – 20
30 – 50
60 – 100
150 – 250
15 – 30
30 – 60
60 – 120
150 – 300
Energy use:
(kWh/y)
30
60
120
300
same as
above
Costs*:
(Euro/system)
3,600 – 7,300
5,400 – 10,000
< 12,000
< 21,000
same as above
Constructed wetlands in the decision support tool
In the decision support tool subsurface flow wetlands are included for greywater and
blackwater treatment on varying scales, namely 5, 200, and > 1,000 pe (see Technological
Data Sheet 18 in Appendix 3).
5.6.5
Fixed bed reactors
Fixed bed reactors are an attached growth aerobic process, in which the micro-organisms
grow on a porous filter median that is submerged in wastewater. Conceptually and
operationally the process is similar to the trickling filter process, although oxygen is supplied
actively by blowing air through the water (see paragraph 5.6.10). Data on size, performance,
costs, land and energy use can be found in Table 21 on page 80 (costs), Table 28 on page 87,
and Table 32 on page 91.
5.6.5.1
Fixed bed reactors in the decision support tool
In the decision support system we include fixed bed reactors at the scale of a single household
and a block of houses (at 5 and 50 pe). For more detail see Technology Data Sheet 19 in
Appendix 3.
84
Sustainable wastewater treatment
5.6.6
5 Processing units: technology characterisation
Membranes
Membrane filtration is used to physically filter out minute particles including viruses and
some ions. Pore size is the most critical parameter is removal of particles and pathogens,
however, it is not the only mechanism, sieve retention and adsorption sequestration also play
a role. It has been shown that pore size can be greater than particle size for significant
retention and also that bacteria may pass through membranes with smaller pore size than the
bacteria diameter (Madaeni 1999).
Different membrane types and configurations are used for different purposes. Membranes are
often used in combination with other treatment technologies, as for instance in the membrane
bioreactor where the membrane replaces the secondary clarifier, however, it is also possible to
use a combination of membranes, for instance micro-filtration (MF) and reverse osmosis
(RO), to configure a complete wastewater treatment and water reuse systems. One could even
go a step further and use more specialised membrane to separate valuable substances from
wastewater water as certain enzymes, hormones, etc. for application in medicines or
biotechnology.
In the next two paragraphs we discuss membranes applications that are included in the
decision support system, namely membrane filtration for disinfection of wastewater and
membrane bioreactors for the treatment of mixed wastewater or greywater.
5.6.6.1
Membranes for wastewater disinfection
Membranes are finding increasing application in disinfection processes for raw water and
municipal effluent reuse. Membranes are capable of removing viruses completely
(UF/NF/RO) or significantly (MF). Ben Aim et al. (1993) report the following removal
efficiencies for a MF-membrane to treat secondary treated sewage effluent; l00% removal of
total coliforms, faecal streptococci, and entroviruses, furthermore, a 83% removal of BOD, a
92% removal of turbidity, and a 22% removal of total phosphorus. More details are
summarised in Table 25, and Technology Data Sheet 20 in Appendix 3.
Table 25: Membranes after activated sludge (STOWA 1998b, p.18).
Type
Pressure
DS max. in effluent
Concentration max.
Energy use
Flux
Exploitation cost
Membrane cost
(bar)
(g/l)
(%)
(kWh/m3)*
(l/(m2*h))
(Euro/m3)*
(Euro/m2)
Life-time
(y)
Clogging
-
Dead-end
MF/UF
0.4 – 1.0
0.1 – 0.2
90 - 95
0.05 – 0.2
50 - 100
0.2 – 0.4
110 capillary
160 tubular
4 capillary
5 tubular
Sensitive
Cross-flow
MF/UF
1-6
30
90 - 95
2-4
100 - 150
0.2 – 0.4
110 capillary
160 tubular
4 capillary
5 tubular
Non-sensitive
* per m3 permeate, note: original figures in Dutch guilders, 1 Euro=2.20371 NLG.
85
Hybride
MF/UF
0.4 – 1.0
0.2
90 – 95
0.1 – 0.3
70 – 120
0.2 – 0.4
160 tubular
4 capillary
5 tubular
Sensitive
Sustainable wastewater treatment
5.6.6.2
5 Processing units: technology characterisation
Membrane Bioreactor Aerobic
There are different configurations of membrane bioreactors possible, in these configurations
the membrane establishes the liquid-solids separation, for instance replacing the secondary
clarifier in a conventional activated sludge processes. The advantages of the membrane
bioreactor is that the complete uncoupling of the hydraulic retention time (HRT) and sludge
retention time (SRT), the absolute retention of all micro-organisms insures an increase in
sludge concentration and longer contact time enhancing the treatment of low biodegradable
pollutants. Furthermore, the membrane produces an effluent almost free of pathogens suitable
for reuse. Due to the absence of a secondary clarifier and the high sludge concentration the
overall size of the treatment plant is reduced significantly. For details on performance see
Table 26 and 27.
5.6.6.3
Membrane Bioreactor Anaerobic
The option of a membrane bioreactor is simple and straightforward. The influent is pumped
into the reactor where it gets in contact with the micro-organisms. The mixed liquor is then
pumped through the membrane filtration unit where the biomass and the treated effluent are
separated. The treated effluent (permeate) leaves the process and the biomass (concentrate) is
returned back to the reactor. More than 95% of the applied COD load can be removed by
anaerobic membrane bioreactor for mass loading rates up to 0.8-0.9 kg COD/kgVSS/day
(Beaudien 1996).
Table 26: Performance of an aerobic membrane bioreactor (Parameshwaran 1999).
Influent
TS
TSS
BOD
COD
TN
TKN
TP
TC
FC
(mg/l)
(mg/l)
(mg/l)
295-375
530-625
26-165
26-165
2.2-9.0
>107
>105
(mg/l)
(mg/l)
(mg/l)
(count/ml)
(count/ml)
Effluent
0
0
<4
<25
<6
<3
0.2-4
ND
ND
Note: ND = not detected
Table 27: Membrane bio-reactors (STOWA 1998b, p.24).
Type
Flow
Medium
Pressure (under pressure)
DS max. in effluent
Energy use
Flux
Exploitation cost
Membrane cost
Life-time
Clogging
Capillar
MF/UF
0.1 – 0.6
Air / water
0.1 – 0.8
15 - 25
0.2 – 0.1
20 - 35
0.6
110
2-3
Less sensitive
(m/s)
(bar)
(g/l)
(kWh/m3)*
(l/m2/h)
(Euro/m3)*
(Euro/m2)
(y)
-
Plates
MF/UF
0.1 – 0.6
Air / water
0.1 – 0.6
15 – 20
0.2 - 1.5
30 – 45
0.5 – 0.6
140
2–3
Less sensitive
* per m3 permeate, note: original figures in Dutch guilders, 1 Euro=2.20371 NLG.
86
Sustainable wastewater treatment
5 Processing units: technology characterisation
Table 28: Overview aerobic treatment techniques (Geldof 1997b, p.45-53).
Activated
sludge
Size
Land are
Investment
Maintenance
Energy
Sludge
SS
COD/BOD
TN
TP
Cu
Zn
Oil
PAC
(pe)
(households)
(m2)
(Euro/house)
(Euro/ house for
clusters of houses)
(Euro/y)
(Euro/y)
(Euro/ton)
(%)
(%)
(%)
(%)
(%)
(%)
(%)
(%)
> 100
> 20
20,000d
1,810 – 2,040e
910 – 1,130d
Rotating
biol.
contactors
5 or larger
1 or more
5 – 10
600
300
41/pe
?b
?b
80
> 90
80
60 – 90
50
50
> 60 –80a
> 60 –80a
91 – 225
32 – 113
23 – 36
80
> 90
50
< 50
50
50
> 60 –80a
> 60 –80a
Fixed bed
reactor
Trickling
filter
1
< 16
3 to 4
4,500 – 6,800
2,300
340
?b
?b
90c
90c
> 70
50
50
50
> 80
> 80
23 – 36b
Low
?b
80
90
50
50
50
50
> 60 – 80a
> 60 – 80a
(a) The technology is used for the treatment of domestic wastewater, domestic wastewater with some rain ,
greywater, or rainwater runoff, for the last to application Oil and PAC removal is estimated to be > 80% for the
first 2 > 60%, (b) Costs for energy and/or sludge disposal included in maintenance, (c) For rainwater runoff
treatment somewhat lower approximately 80%, (d) For a 100,000 pe plant, (e) For smaller plants, 100 pe and
larger. Note: original cost data in Dutch guilders, 1 Euro = 2.20371 NGL.
Table 29: Performance of sedimentation and precipitation based literature.
Retention time
Surface-loading
Sludge production
Removal of:
TS
TSS
VS
BOD
COD
TN
NH4+-N
TP
Cd
Cu
Pb
Zn
(h)
(m3/m2/h)
(kg DS/m3)
(kg DS /(cap*y))
(%)
(%)
(%)
(%)
(%)
(%)
(%)
(%)
(%)
(%)
(%)
(%)
Pre-Sedimentation:
1.5 to 2.5
1.5 to 3 or
< 1.6e or < 2.0e
50 – 64e
30 – 40 or 50 -70c 85d
a
Precipitation:
0.27 ± 0.2b
34.5 ± 16b
60 – 80a or 91b 75d
20 – 30a or 24 – 40c
or 25 –33e
20 – 30a or 25 – 33e
5 – 10a or 9e
20 – 30a or 81b
10 – 20a or 11e
60 – 80a or 94b
57b
81b
64b
50b
20 – 30a or 73b
20 – 30a or 28b
(a) STOWA 1998, p.26, Table 13, refferring to STOWA 1996 – 20; (b) Ødegaard 1992, p.261 and p.263, (c)
Crites et al. 1998, p.300, (d) Kärrman 2000 p.83, (e) German design standards ATV-DVWK A131 in COST
WG5 (2000), note lower values at retention time 0.5 to 1 h, higher values retention time1.5 to 2 h, lower value is
horizontal throughflow, higher is vertical throughflow, TN in this case TKN.
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Sustainable wastewater treatment
5 Processing units: technology characterisation
Membrane bioreactors seem expensive, however, on small-scale they can be more economical
than activated sludge or anaerobic digestion. For instance, Davies et al. (1998 in Stephenson
et al. 2000 p.172) compared the economics of a membrane bioreactor and an activated sludge
process for design flows of 2,350 and 37,500 population equivalents. The capital costs for the
lower flow membrane bioreactor are reported to be approximately 880,000 Euro, of which
78% was attributed to the membrane system. The activated sludge systems costs for this flow
were 160% of the membrane bioreactor, but is cheaper at higher flows, costs approximately
54% of the membrane bioreactor.
5.6.6.4
Membranes in the decision support tool
In the decision support tool, we include membranes in drinking water production (see
paragraph 5.2.1), effluent disinfection, and aerobic membrane bioreactors for both domestic
wastewater treatment and greywater treatment (see Technology Data Sheets 20 and 21 in
Appendix 3).
5.6.7
Rotation biological contactors
Rotating biological contactors are an attached growth aerobic process, in which the microorganisms grow on disks that rotate such that the micro-organisms are in alternating contact
with wastewater and air. Conceptually and operationally the process is similar to the trickling
filter process with high rate of circulation (see paragraph 5.6.10). Data on size, performance,
costs, land and energy use can be found in Table 21 on page 80 (costs), Table 28 on page 87,
and Table 32 on page 91.
5.6.7.1
Rotating biological contactors in the decision support tool
In the decision support system we include rotating biological contactors at household and
neighbourhood scale.
5.6.8
Sedimentation
Sedimentation is commonly used as preliminary treatment and for solid-liquid separation after
biological treatment. Efficient designed and operated primary sedimentation tanks should
remove 50 to 70 % of the suspended solids and 24 to 40% of the BOD (Crites 1998, p.300).
Normally, primary sedimentation tanks are designed to provide 1.5 to 2.5 hours of detention
(Crites 1998, p.309). Primary sedimentation tanks are loaded with 1.5 to 3.0 m3/(m2*h), while
sedimentation tanks used after biological treatment have a lower surface loading, ranging
from 0.7 to 1.5 m3/(m2*h) (STOWA 1998. p.25). Sludge from sedimentation generally has a
dry matter content of 0.5 to 2.5% (STOWA 1998, p.25, Kärrman 2000 p.83).
Bacteria and viruses are removed significantly by sedimentation, as these are mostly solidsassociated (Bitton 1999, p.203). For ova (eggs) and cysts of parasites sedimentation and
filtration are the only significantly removal processes in wastewater treatment (Gray 1999,
p.274).
5.6.8.1
Sedimentation in the decision support tool
In the decision support tool sedimentation is implemented as pre-treatment and as post
treatment and as pre-treatment with the addition of chemicals (precipitation) (see
Technological Data Sheet 23 in Appendix 3).
88
Sustainable wastewater treatment
5.6.9
5 Processing units: technology characterisation
Septic tanks
Septic tanks are small rectangular chambers in which household wastewater is retained for 1
to 3 days. The waste inside the tank stratifies into four layers, (1) an upper layer of scum, (2) a
sedimentation zone, (3) a sludge digestion zone, and (4) on the bottom a sludge storage zone.
Septic tanks are commonly used as onsite primary treatment for domestic wastewater.
Strengths and weaknesses of septic tanks are presented in Table 30. The removal mechanisms
in a septic tank are sedimentation, digestion, and floatation. The digestion is single-phase wet
digestion, at lower temperatures. Performance and data on size and costs are presented in
Table 31 on page 90.
Costs for septic tanks depend on size, type, soil etc, but can roughly be estimated to be 1,500
to 8,000 US$ (average 4,000) (EPA 1999, Loetscher 1999, p.154). Maintenance costs for a
proper functioning septic tank system is on average 100US$, or on average 9% of investment
per year (EPA 1999, Loetscher 1999, p.156).
For details on the septic tank that is included in the decision support tool see Technological
Data Sheet 24 in Appendix 3.
5.6.10 Trickling filters
In trickling filters, the biofilm growing on the porous filter media degrades the organic matter
in the wastewater that trickles on top of the filter and percolates through. The depth of the
filter is approximately 1 to 2.5 m (Bitton 1999, p.234). For optimal degradation there has to
be a large surface area for biofilm growth but also enough inner space to provide aeration and
to prevent ponding due to filter clogging through biomass or freezing. In the filter a
stratification of micro-organisms takes place, in the upper part where the BOD concentration
in the wastewater is higher, heterotrophic bacteria dominate, while in the lower parts of the
filter nitrifying bacteria dominate.
Removal of pathogens by trickling filters is generally low and erratic, for bacteria varying
from 20% to >90% depending on operation (Bitton 1999, p.242). Removal of Salmonella 75
to 95%, total coliforms 92 to 95%, enteroviruses 59 to 95%, and bacterial phages 40 to 90%
(Bitton 1999, p.242). Nitrification is virtually eliminated by hydraulic loadings of domestic
wastewater > 2.5 m3 * m-3 * d-1, due to enhanced heterotrophic growth extending to the depth
of the filter overgrowing the slower growing nitrifing bacteria (Grey 1999, p.364). Other
limitations for nitrification are too little oxygen supply and low temperature (<10°C).
The water has to be sprayed on top of the filter, therefore, if the landscape does not allow for a
design with natural level differences, pumps are needed. However, as aeration is passive, the
energy requirement for this process is low. For data on energy use, land area required and
sizes see Table 29 on page 87 and Table 32 on page 91.
Trickling filters are more economic on larger scale, as the investment for a 1,000 pe system is
150 Euro/pe while a 100 pe systems costs about 600 Euro/PE, the running cost are estimated
6.9 Euro/year for 1000 pe systems and 11 Euro/year for smaller systems (Alexandre 2000).
Geenens (2000) estimated the costs for a 5 pe system at 5,800 Euro.
5.6.10.1 Trickling filters in the decision support tool
In the decision support system trickling filters are incorporated for treatment of settled or
otherwise treated wastewater. Sizes included are 5, 50, and 400 pe.
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5 Processing units: technology characterisation
Table 30: Strengths and weaknesses septic tanks and digester (Loetscher 1999, p.45-46).
Description
Septic tank
For treatment of domestic
wastewater, 2 compartments.
Strength
+ accepts greywater
+ can be connected to inhouse toilets
Weakness
- skilled labour for
construction
- water-tightness may prove
difficult
- regular desludging
required
- facilities for disposal of
effluent required
* water needed to flush
excreta
* not suitable in very high
population densities
* access required for
desludging
Constraint:
Septic tank for excreta reuse
Designed to treat human and
animal exreta, slurry from tank
can be used as fertiliser, 3
compartments (longer retention
time)
+ produces liquid fertiliser
+ can be connected to in-house
toilets
+ accepts animal faeces
- skilled labour for construction
- water-tightness may prove
difficult
- regular desludging required
- facilities for disposal of effluent
required
Biogas digester
Produces biogas for cooking and
lighting, needs human and cattle
excreta, long liquid retention time
results is safe effluent slurry for
fertilisation.
+ produces biogas and fertiliser
+ can be connected to in-house
toilets
+ accepts animal faeces
- skilled labour for construction
- water-tightness may prove difficult
- biogas production fall off at low
ambient temperatures
*restricted to rural areas
* accepts only small amounts of
greywater
* water needed to flush excreta
* requires fertiliser demand
* access required for desludging
*restricted to rural areas
* accepts only small amounts of
greywater
* water needed to flush excreta
* requires fertiliser and biogas
demand
Table 31: Performance septic tank for domestic wastewater.
Size
Land are
Investment
Maintenance*
SS
COD/BOD
TN
TP
Cu
Zn
Oil
PAC
(m3)
(household)
(pe)
(m2)
(Euro/house)
(Euro/y)
(%)
(%)
(%)
(%)
(%)
(%)
(%)
(%)
Geldof 1997b
p.44
6**
1
RIONED
1998
6 or 12
5 or
5–6
2,300 – 2,900
30 –70
30 –70
10 - 15 ***
10 - 15 ***
50
50
50
50
10
2,700 – 6,800
40
15
15
* To be emptied regularly – once every 2 years.
** 6 m3 for 3 pe, or 12 m3 for 5 pe (RIONED 1998b).
*** 15 % according to RIONED 1998b, and for blackwater treatment.
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Sustainable wastewater treatment
5 Processing units: technology characterisation
Table 32: Sizes and costs of small-scale aerobic treatment (RIONED 1998, p.50-54).
Activated sludge
Rotating biological
contactors
Submerged / fixed
bed
Trickling filter /
oxidation bed
Size
(pe)
5
10
20
50
5
10
20
50
5
10
20
50
5
10
20
50
Size
(m2)
2
4
5
10
1-6
2-6
6-15
10-20
2
3
5
8
4
5
5
5
Energy*
(kWh/y)
375
750
1900
4875
350
350
525
1325
550
1825
2200
2550
375
750
1500
3750
Sludge
(m3/pe/y)
primary:
0.2 –0.4
secondary:
0.4 – 0.8
primary:
0.2 – 0.4
secondary:
0.2 – 0.4
primary:
0.2 – 0.4
secondary:
0.4 – 0.8
primary:
0.2 – 0.4
secondary:
0.2 –0.4
Costs**
(Euro)
4,500-5,400
5,400-6,600
9,500-11,000
17,500-20,200
4,300-6,800
5,000-10,000
7,700-13,600
16,800-31,300
6,300-7,300
11,300-12,500
13,800-15,700
19,500-22,200
5,000-5,900
6,800-7,900
10,900-12,700
20,000-22,700
* Energy figures original in costs based on 0.2 NLG/kWh, for transport wastewater onsite to treatment 5
to 10 kWh/y has to be added. ** Costs are inclusive installation and exclusive a septic tank.
Note original; figures in Dutch guilders, 1 Euro=2,20371 NGL.
5.6.11 UV-disinfection
A general introduction to UV-disinfection is given in paragraph 5.2.1, there it was discussed
as technology to produce drinking water. Another option is to disinfect treated wastewater for
safe use in irrigation or as recreation water. A summary of advantages and disadvantages of
UV-disinfection is given in Table 34. A main disadvantage, or constraint for this technology
that the influent has to be low in turbidity (TSS < 30 mg/l, turbidity < 30 NTU, EPA 1999).
This means that prior to UV-disinfection the wastewater has to be treated in a filter or
sedimentation process. Energy use can be a disadvantage too, although energy use can be low,
especially in tropical countries were solar energy can be used for UV-disinfection. The costs
of UV-disinfection are quantified in Table 33.
5.6.11.1 UV-disinfection in the decision support tool
UV-disinfection is in the tool an option for in-house production of drinking water if a reliable
water source is available (Technological Data Sheets 2 and 3) and as an option for wastewater
disinfection (see Technology Data Sheet 26 in Appendix 3).
Table 33: Costs or UV-disinfection of wastewater (EPA 1999).
UV Lamps
3,800 – 19,000 m3/d
19,000 –38,000 m3/d
75,700 – 380,000 m3/d
construction
($/lamp)
($/lamp)
($/lamp)
(% lamp costs)
575 (397 – 1,365)*
475 (343 – 594)*
400 (274-588)*
150 (75 – 200)*
* typical value and between brackets the range. Lamps are usually replaced after
12,000 using hours, ballasts after approximately 10 years, and quartz sleeves
generally every 5 years.
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5 Processing units: technology characterisation
Table 34: Advantages and disadvantages of UV-disinfection (Crites 1998, p.887).
Advantage:
Effective disinfectant
Disadvantage:
No immediate measure of whether
disinfection was successful
No residual effect
Less effective in inactivating some
viruses, spores, and cysts at low
dosage used for coliform
organisms
Relative expensive
Large number of UV-lamps
required
No residual toxicity
More effective than chlorine in
inactivating most viruses, spores, and
cysts
Improved safety
Requires less space
5.7 Technology characterisation in the decision support tool
The wide range of technologies for the domestic water system characterised in this chapter are
contained in the processing units of the decision support tool. We chose a simple and
comparable characterisation for all technologies allowing quantification of the sustainability
indicators. Three main problems in gathering data for the technology characterisation were:
(1) Incomplete data sets:
There are many different technology configurations that can be applied in domestic
water systems, all with their own advantages and disadvantages. Literature describes
technology mostly in the context of specific applications, local circumstances, and with
different research objectives in mind. Consequently, the respective descriptions vary
significantly, making it impossible to get a complete overview of all technologies and
all the sustainability indicators we defined in Chapter 2. Some of the indicators we
chose have been quantified in most descriptions, while others have not even been
quantified in a single one. As a result, some of the indicators could be estimated quite
easily while others could only be guessed, or not quantified at all. We, for instance,
decided to leave out the nutrients potassium, calcium and magnesium, and all gaseous
emission because no data were found in our literature inventory. Furthermore, the heavy
metals cadmium and lead were seldomly mentioned, similarly ammonia (NH4+). These
are left out as well. Faecal coliforms removal is also difficult to quantify but is too
essential to leave out. Another problem occurs with BOD as this indicator is only
interesting for wastewater treatment not for sludge. It would have been easier for a mass
balance to work with total carbon instead of BOD or COD, however, total carbon is
often not indicated. As such we used BOD throughout the model, and have to make
rough estimates for BOD removal in sludge processes such as composting.
(2) No data on qualitative aspects:
For the qualitative indicators on functional and social-cultural aspects it is even more
difficult to find literature references, these are not reported systematically. Furthermore,
not all the qualitative indicators are interesting for all technologies. For instance
‘acceptance’ is a main issue in composting toilets, while for most treatment technology
such as sedimentation it is neutral. Some of these indicators are associated with scale,
for instance ‘institutional requirements’ mainly distinguishes between centralised and
decentralised treatment. It was difficult to indicate ‘labour’, this indicators was used
only once, for transport by trucks. Furthermore, labour intensity can either be positive
and negative. Therefore, ‘labour’ is left out.
92
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5 Processing units: technology characterisation
(3) Comparability and accuracy of data from different literature sources
Obviously, many indicators have a local character and can only be quantified accurately
through local research including economic, environmental, social-cultural, and technical
inventories. However, in this research we aim at a generic comparison of technologies,
to identify strengths and weaknesses of technologies and technology combinations as
well as trade-off with respect of the different sustainability indicators. For this purpose,
the technology characterisation based on a combination of theoretical data and data
from different case-studies described in literature is sufficiently accurate.
An overview of the characterised technologies and their place in the processing units in the
model of the domestic water system is given in Figure 19. A summary of the data per
technology can be found in Appendix 3. The internal report (Balkema 2001) gives a detailed
overview of the literature inventory.
Figure 19: Overview of the technologies included the model of the domestic water system.
93
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5 Processing units: technology characterisation
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5 Processing units: technology characterisation
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98
Sustainable wastewater treatment
6 Sustainable domestic water systems
6 Sustainable domestic water systems
The decision support tool is used best in connection with all involved actors, who adapt the
data that include the constraints, set the solution space by selecting the technologies to
include, and set the weighting factors in the objective function. In this way, a consensus can
be reached on what system to select in the particular situation based on the discussion of the
outcomes generated by the decisions support tool. Note that the tool is not an expert system,
meaning that an expert is necessary to check the solutions calculated by the tool.
It is not the objective of this study to find an optimal system for a defined situation. In this
research we seek to provide insight into the sustainability of possible solutions for problems
in the water sector and to identify and highlight necessary trade-offs and decisive variables.
We are in particular interested in affordable systems and systems that close the water and
nutrient cycles as we believe those are needed to fulfil the challenge of this millennium, which
is providing safe drinking water and hygienic sanitation to all by 2025. Therefore, we selected
systems for the two following scenarios: (1) closing nutrient cycles to avoid scarcity of
nutrients in agriculture and excess of nutrients in surface water, described in section 6.1, (2)
closing the water cycle on relatively small-scale to cope with water scarcity, in combination
with nutrients recycling this results in selecting what we call life support systems. This second
scenario is described in section 6.3. In addition we select the most affordable systems in both
scenarios, as we are aware that poverty is still a main issue in large parts of the world.
6.1 Closing nutrient cycles
This first scenario is devoted to an environment in which we have an excess of nutrients in the
surface water and a need for fertiliser in the agriculture; obviously a case in which recycling
of nutrients back to the fields is of primary interest.
6.1.1
Solution space
In order to keep the computation times within reasonable limits, we selected a solution space
that contains only the most interesting choices. The used decision variables and their upper
and lower boundaries are given in Table 35 for the two scenarios, namely solution space NR1
for nutrient recycling and the solution space LS1 for life support systems that focus both on
water and nutrient recycling. Latter will be described in section 6.2.
6.1.2
Selected systems
In Table 36 an overview of the systems selected by the decision support tool for nutrient
recycling is given. This selection is based on the optimisation with the following objective:
f = −objective = ∑
weight * sustainability indicator 100 * F
50 * SC − 100 * W
=
+
+
4
norm
2.2 *10
1.2 *10 3 4.9 *10 4
in which :
F = RoW (3,1) = fertiliser
SC = RoW (2,1) = soil conditioner W = RoW (8,1) = waste
Fertiliser and soil conditioner are both defined by the restrictions. All output streams that have
more than 15 mg Ptot per kg total weight and less than 70 mg Cu per kg P and less than 500
mg Zn per kg Ptot are classified as fertiliser. The restrictions for soil conditioner are Total
101
Sustainable wastewater treatment
6 Sustainable domestic water systems
Solids > 500 mg/kg and Cu < 110 and Zn <350 mg/kg (see also Table 13 on page 56). Waste
is defined as all outgoing streams that are not classified otherwise.
Table 35: Solution spaces defined to select domestic water systems for nutrient recycling
(NR1) and water reuse (LS1) (differences in bold).
Variables:
x1
x2
x5
x6
x7
Household water
Rainwater for toilet,
washing, and outdoors
Rainwater for kitchen
and personal hygiene
Water saving shower
and washing machine
Rainwater system
Disinfection unit
Toilet
x8
On-site treatment
x9
x10
x11
x12
x13
Blackwater transport
Yellowwater transport
Greywater transport
Black sludge transport
Blackwater treatment 1
x14
Blackwater treatment 2
x15
Blackwater treatment 3
x16
Blackwater treatment 4
x17
Greywater treatment 1
x18
Greywater treatment 2
x19
x20
x21
x22
x23
x24
Rainwater treatment
Sludge treatment
Greywater splitter
Rainwater splitter (R7)
Rainwater splitter (R8)
Yellowwater splitter
x3
x4
Total of possibilities contained in the decision
support system:
Number water systems included (total in tool 7*1012)
0=Off, 1=On
0=Off, 1=On
Solution spaces:
NR1:
LS1:
25,920
20,736
0
0
0
1
0=Off, 1=On
0
1
0=Off, 1=On
0
1
3
4
5,6,8
1,2,3*
1,3,4*
6,8,10
6
1,2,3,4
1,2,3,4*
3
1,2*
2
1,2,3,4
1,2,4*
3
1,2*
2
4
2,4,5,6,
13, 14
1,5,11,
14
3,7,8
1,4,8
7
4,6*,7
1,2,4,8,9
1,9
5,8
4,7,8
3,4*
3,4*
0,1
0
1
0,1
4
3,4*
1
1
0
1
1=Small,2=Large, 3=No
1=RO, 2=Sun, 3=UV, 4=No
1=Old, 2=New, 3=Interrupt, 4=Low flush,
5=Vacuum, 6=Urine sep., 7=Urinal, 8=Compost low
flush, 9=Compost dry, 10=solar dry
1=AS5, 2=AD5, 3=Membrane, 4= MBR, 5=Septic
tank, 6=No
1=Combined sewer, 2=Separate, 3= Vacuum, 4=No
1=Separate sewer, 2=Truck, 3=No
1=Separate sewer, 2=No
1=Truck, 2=No
1=Membrane, 2=pre-Sedimentation,
3=Precipitation, 4=No,
1=AS50, 2=AS400, 3=AS1000, 4=AS100000,
5=AD400, 6=AD500000, 7=FB5, 8=FB50,
9=Membrane, 10=MBR, 11=RBC5, 12=RBC50,
13=RBC400, 14=No
1=CW5, 2=CW200, 3=CW>1000, 4=TF5 5=TF50,
6=TF400, 7=TF1000, 8=No
1=Soak away, 2=Swales, 3=Infiltration field,
4=Membrane, 5=Post Sedimentation, 6=UV, 7=No
1=MBR, 2=RBC5, 3=RBC50, 4=RBC400, 5=TF5
6=TF50, 7=TF400, 8=TF1000, 9=No
1=Soak away, 2=Swales, 3=Infiltration field,
4=CW5, 5=CW200, 6=CW>1000, 7=UV, 8=No
1=Soak away, 2=Swales, 3=Infiltration field, 4=No
1=AD400, 2=AD50000, 3=Composting, 4=No
0=To greywater, 1=To on-site treatment
0=To blackwater, 1=To greywater treatment
0=To grey or blackwater, 1=To rainwater treatment
0=To greywater, 1=To yellowwater treatment
* Note: The model will overrule and chose no treatment if the influent of the processing unit is zero. The options
“separate sewer” and “no transport” for black and greywater can also be overruled. In these cases the model will chose
“combined sewer” and “separate sewer”. Therefore, some choices are included indirectly.
Abbreviations: AD = Anaerobic Digestion for 5, 400, and 50,000 pe, AS = Activated Sludge for 5, 50, 400, 1,000, and
100,000 pe, CW = Constructed Wetland for 5, 200, and >1,000 pe, FB = Fixed Bed reactor for 5, 50, and 1,000 pe, TF =
Trickling Filter for 5, 50, 400, and 1,000 pe, MBR = Membrane BioReactor, RBC = Rotating Biological Contactors for
5,50, and 400 pe.
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6 Sustainable domestic water systems
Table 36: Systems for nutrient recycling selected with the global solver (gclSolve), using
the following weighting factors: fertiliser 100, soil conditioner 50, and waste –100.
Solution space & Data-set:
Title:
Description:
Solution space NR1 & data2003_nutrients
Good
Best
Cheap @
urine
urine
vacuum
separation, separating
toilet and
activated
compost
mixed
sludge
toilet
wastewater
Value of f = -objective
-8
2
94
99.68 to –124.80
Sustainability indicators:
(min-max)
Costs
(euro/cap/y)
1.1E3 3.8E3 –
9.9E5
5.4E2
4.6E3 – 3.3E6
2.9E3
5.3E3
Energy
(kWh/cap/y)
-2.4E1 –
4.8E1 –
5.4E3
4.7E5
4.3 – 4.7E5
4.7E5
4.6E5
Space
(m2/cap)
1.8E4
1.8E4
1.8E4
1.8E4
1.8E4 – 2.0E4
Water use
(m3/cap/y)
37
37
38
37
37 – 38
Acceptance #
1 - 3 (0-1)
1 – 2 (1)
1 (1)
1 (1)
0 – 8*
Sustainable behaviour #
1 (0)
1 (0)
1 (0)
1 (0)
0 – 8*
Fertiliser
(kg/cap/y) ? 2.2E4 ?
4.5E2
1.5E3 !
0
0 – 2.4E4
Soil conditioner
(kg/cap/y) ? 6.3E2 ?
8.3E2
0
0 – 1.2E3
? 1.2E3 ?
Waste
(kg/cap/y)
2.2E4
2.2E4
2.2E4
4.6E4
2.2E4 – 4.9E4 &
Most interesting domestic water systems included in this set ^:
explanation:
x7
Toilet
5= vacuum,
6,5
6
8
5
6= urine sep.
8= compost
x9
Blackwater transport
2= separate
1,2,3
4
3
1,2,3
3= vacuum
4= none
x11 Greywater transport
1= separate
{2}
{2}
{2}
{2}
2= none
x13 Blackwater treat. 1
4= none
4
4
4
4
x14 Blackwater treat. 2
3=
AS 400pe
6
3
14
14
6= AD 500000pe
14= none
x15 Blackwater treat. 3
3= wetland 1000pe
3
3
8
3
8= none
x17 Greywater treat. 1
1= MBR
9
9
~1~
9
9= none
x18 Greywater treat. 2
5=wetland 200pe
8
8
~5~
8
8= none
x21 Greywater splitter
0=to greywater,
1
1
0
1
1=to blackwater
x24 Yellowwater splitter
0=to greywater
0
1
1
0
1=to yw treat.
Non-sense
low flush
toilet and
mixed
wastewater
-83 to –82
@ Note: Cheapest system in first 6999 systems selected by the global solver.
? Too high values as the wastewater or sludge in these systems is mixed thus diluted and possibly polluted, see text.
! Higher fertiliser amount is in fact more diluted due to higher flush water volume used in low-flush compost toilet.
* Note: the low flush toilet is a urine separating toilet but the urine is mixed again.
^ Note that the x-values not shown are the same for all the systems, namely x1=0, x2=0, x3=0, x4=0, x5=3, x6=4, x8=6,
x10=3, x12=2, x16=7, x22=0, x23=1, the values of x22 and x23 indicate that all rainwater is directed to rainwater
treatment all others indicate no treatment or off, {0} indicate that lower and upper bounds fix x-value,
# Note: advantages and between brackets the disadvantages.
* Note: the maximum value 7 is based on the number of technologies chosen.
& Note: in normalisation the minimum value for waste was assumed to be 0, however minimum found is 2.2E4.
~ Only a membrane bioreactor or only a constructed wetland should be sufficient to treat the greywater.
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Sustainable wastewater treatment
6 Sustainable domestic water systems
The best system we found for nutrient recycling is based on low flush composting toilet that
separates the urine. The blackwater is treated in the toilet to produce compost which can be
classified as soil conditioner. While the separated urine, or yellowwater, is stored and can be
used as fertiliser. In addition the greywater is treated in a membrane bioreactor followed by a
constructed wetland, as this allows the decision support tool to classify the treated greywater
as suitable for infiltration. The choice of these two technologies in series is strange as only the
membrane bioreactor or only the wetland should be enough to treat the greywater. However,
according to the present settings of the tool, the constructed wetland, does not produce an
effluent good enough; as result more waste is produced (4.5E4 in stead of 2.2E4 (kg/cap/d)).
Therefore, one may argue that the restrictions are set too tight or that changes in data
concerning removal rates or concentrations in incoming streams should be made. Remarkable
is also the higher water use when selecting a compost toilet. However, the low flush
composting toilet uses more water for flushing than the urine separation toilet (Compost low
flush: 4 l/flush for faeces, 0.8 for urine, Urine separation toilet: 4 l/flush for faeces, 0.2 for
urine, Vacuum toilet: 1 for both urine and faces).
The disadvantage of the above described system maybe the use of the composting toilet that,
depending on the handling of compost and urine, may not be accepted by the end users as a
hygienic and comfortable sanitation option. Composting can be centralised, although the
freshness and quality of compost may then be less. For urine separation alternatives are to
separate urine after the toilet bowl or to use urinals for the collection of urine. Obvious the
amount of urine separated will be far less and depending on the way of collecting the urine
may get contaminated.
In Table 36 we can see that the above described systems was actually not the one selected as
best by the decision support tool. The tool selected a water system in which all wastewater is
mixed and treated anaerobically. Mathematically, with the current settings of the tool, this is
the best solutions, however we titled it “Non-Sense” as this system does not allow reuse of
nutrients and unable to treat the wastewater up to an acceptable level. For simular reasons, we
also valued the second best solution selected by the tool, which consists of urine separation in
combination with activated sludge treatment for the mixture of black and greywater, as less
good than the above described systems based on a urine separating composting toilet. Then
why are these solution selected? Well, the amounts and quality of the fertiliser and soil
conditioner produced by these is domestic water systems are overestimated by the decision
support tool. For instance in system with anaerobic digestion of the mixed wastewater the
treated wastewater can mathematically be classified as fertiliser, while the digested sludge can
mathematically be classified as soil conditioner, while only the run-off is classified as waste.
This is due to:
(1) the fact that decision support tool uses the total weight of streams that can be
classified as fertiliser to calculate the objective. Meaning that the maximum
dilution, that still satisfies the constraints, is valued highest. Alternatively one could
use the weight of P contained in the streams to calculate the objective. In addition,
one may wonder whether the value of the restriction, more than 15 mg Ptot per kg
total weight, is set right. Is the amount of P that will be available for reuse
contained in fertiliser high enough? The restriction uses Ptotal, while not all P
contained in Ptotal will be available for reuse. Furthermore additional constraints
may have to be set for other nutrients such as for instance potassium.
(2) the mixed wastewater is probably too diluted for anaerobic digestion, but the tool is
not aware of this as it has no influent constraint for anaerobic digestion.
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Sustainable wastewater treatment
6 Sustainable domestic water systems
(3) high values of P in the contaminants contained in the blackwater and greywater.
Phosphate is contained in the contaminants added to blackwater, while in greywater
the wastewater from washing activities contains quite a lot of phosphate. Although
based on literature, these values may be too high as alternative phosphate free
cleaning agents are available (Ptot added in washing: 1.5E5 g/d, added in toilet
cleaning: 6.6E4 g/d, for a population of 200,000 people, while the urine contains
1.8E5 g/d, and faeces 1.1E5 g/d for the same population).
(4) the assumption that the incoming wastewater consists only of household
wastewater, which is relatively clean with respect to metals and industrial
chemicals. If the latter contaminants are present, the saver route for producing
fertiliser and soil conditioner uses a urine-separating compost toilet.
This indicates that some improvements have to be made to the model and that the model is
not an expert system. We should be aware
that users of the decision support tool
have to understand the model and be
aware of the restrictions and data used
such that they can properly judge the
outcomes of the decision support tool.
6.1.3
Visualising results
In Figure 20 the objective value is shown
for the first 6999 evaluations in both the
unsorted and the sorted solution set. The
plot of the unsorted set makes one wonder
if the optimisation is working at all. A
closer analysis though shows that indeed
the solver does find the best solutions
within the first 6999 evaluations. We
constructed an optimum set of solutions
from the total set of results (25920
evaluations) that is saved in 4 solution
sets by the global solver. For this optimal
set we selected from the 4 solution sets:
(1) 4076 evaluations (objective: –83 to
45), (2) 1702 evaluations (objective: 13
to 48), (3) 120 evaluations (objective:
36), and (4) not a single evaluation out of
the last 4921 evaluations. This optimum
set showes little changes in the second
subplot of Figure 20 and slightly
increased the costs for the lowest costs
column in Table 36, but none of the other
Figure 20: Changing objective value over first 6999
results changed in comparison with the
evaluations, in the upper plot for the unsorted set of
results form the first 6999 evaluations.
solutions and in the lower plot for the sorted set of
For other plots that can be made with the
decision support tool see 6.2.3.
solutions (data2003_nutrients, solution space NR1).
Note that the optimisation is minimisation, thus the
lower the value the better.
105
Sustainable wastewater treatment
6 Sustainable domestic water systems
Table 37: Exploring the sensitivity to weighting factors.
Solver - data - solution space:
Title:
genetic algorithm - data2003_nutrients – solution space NR1
Fertiliser Soil
Costs
Acceptance
Waste
conditioner
584@
920@
501@
373@
-82 to -83
-176
-83
-82
190
87
26
134
Number of generation:
min(f)=-1*objective
No. of evaluations with min(f)
Weighting factors:
Acceptance
0
0
Costs
0
0
Fertiliser
100
100
Soil Conditioner
50
220
Waste
-100
-100
Sustainability indicators for best set of solutions*:
Acceptance #
1-3 (0-1)
1-2 (1)
Costs (euro/cap/y)
1.1E3 6.3E2 –
2.9E3
1.1E3
Fertiliser (kg/cap/y)
2.3E4
0
Soil Conditioner (kg/cap/y)
6.3E2
1.2E3
Waste (kg/cap/y)
2.2E4
2.2E4
The best domestic water systems*:
x7
Toilet
5, 6
6
x9
Blackwater trans.
1,2,3
1,2,3
x11 Greywater trans.
{2}
{2}
x13 Blackwater treat. 1
4
4
x14 Blackwater treat. 2
6
2
x15 Blackwater treat 3
3
3
x16 Blackwater treat. 4
{7}
{7}
x17 Greywater treat. 1
9
9
x18 Greywater treat. 2
8
8
x21 Greywater splitter
1
1
x24 Yellowwater splitter
0,1
0
0
-100
100
50
-100
-100#
0
100
50
-100
2–3 (0-1)
1.1E3 –
2.9E3
2.2E4
6.3E2
2.2E4
2–3 (0)
1.0E3 –
2.9E3
2.2E4
6.3E2
2.2E4
5,6
3
{2}
4
6
3
{7}
9
8
1
0,1
5
1,2,3
{2}
4
6
3
{7}
9
8
1
0,1
(min-max)
4.6E3! –3.3E6
0-2.2E4
0-1.2E3
2.2E4-4.9E4
solution space:
5,6,7
1,2,3,4
1,2
1,2,3,4
2,4,5,6,13,14
3,7,8
7
1,2,4,8,9
5,8
0,1
0,1
* Note that the x-values not shown are the same for all the systems, namely x1=0, x2=0, x3=0, x4=0, x5=3, x6=4, x8=6,
x10=3, x12=2, x16=7, x19=3, x20=3, x22=0, x23=1, the values of x22 and x23 indicate that all rainwater is directed to
rainwater treatment all others indicate no treatment or no use of this water source.
@Note: the genetic algorithm solvers stopped after 20 generations without improvement, {0} the brackets indicate that the
lower and upper boundaries fix the x on this value,
# Note: for acceptance the advantages and disadvantages between brackets – weighted negatively on the disadvantage of
acceptance, thereby ruling out technologies like urine separation toilet that may cause difficulties in acceptance for the
end user.
? Note: cheapest option with data used is the vacuum sewer, thereafter the mixed sewer that is used if no transport is
chosen in combination with large-scale treatment system.
! Note: the normalisation factor for minimal costs is incorrect as lower values have been found, this is a risk of using the
genetic algorithm--solver to set the normalisation factors, this makes the normalisation a bit unfair.
6.1.4
Sensitivity
We define 2 types of sensitivities: (1) sensitivity to weighting factors and (2) to input data.
Both are investigated by perturbing the weighting factors or data, where after a new selection
of optimal systems is made. We used different runs of the genetic algorithm solver to indicate
the sensitivity to changing weighting factors and made new data set to explore the sensitivity
to changing input data.
In Table 37, the results of the optimisation with the different weighting factors are shown.
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If a sustainability indicator can be increased without compromising on another sustainability
indicator any positive value of it’s weighting factor will make that it’s value is maximised as
the inverse of the objective is minimised (f = - objective). However, when there is a trade-off
with another sustainability indicator, the weighting factor should be higher than the “loss” in
the objective by compromising on the other sustainability factor. For instance, there is a tradeoff between fertiliser and soil conditioner if their maximums are both based on the use
blackwater. However, if the weighting factor for soil conditioner is higher than 214.5, the
optimisation will maximise the soil conditioner at the expense of fertiliser (see “Soil
conditioner” column in Table 37). The mathematical explanation is given below:
f = −∑
weight * sustainability indicator
 weightF * F weightSC * SC weightW * W 
= −
+
+

norm
normSC
normW 
 normF
100 * 2.2 *10 4 50 * 6.2 *102 − 100 * 2.2 *104
weightSC *1.2 *103 − 100 * 4.3 *104
+
+
=
+
+
0
2.2 *10 4
1.2 *103
4.9 *10 4
1.2 *103
4.9 *10 4
⇒ weightSC > 214.5 to realise SC = 1.2 *103
in which :
F = RoW (3,1) = fertiliser
SC = RoW (2,1) = soil conditioner W = RoW (8,1) = waste
Of course we used knowledge of the normalisation and results of several other optimisation
runs, but if one suspects that a sustainability has a trade-off and one wants to maximise this
indicator than the weighting factor should be set higher than the maximum values, normalised
and weighted, of the indicators that may be compromised by the optimisation of the chosen
sustainability indicator. If normalised, as we do, on the maximum value, the weighting factor
should be set higher than the sum of the weighting factors of the compromised sustainability
indicators.
The range in costs, in the “Costs” column in Table 37, is due to the fact that the 2 best levels
differ so little in objective value (82.7 and 82.6) that these are put together, but the best level
had indeed the lowest costs. However, for “Acceptance” this is different, the range shown in
Table 37 occurs at the best level. But we weighted on the disadvantages of acceptance,
meaning that we want to avoid technologies that may give problems with respect to socialcultural values of the end user, for instance the use of composting and urine separation toilets.
As one can see in Table 37, the disadvantage, shown between brackets, is selected to be zero
instead of 1, by choosing the vacuum toilet.
Remarkable in the “Costs” column of Table 37, is that the lowest costs case chooses a
separate sewer or a vacuum sewer. The cost for different sewer types are estimated as follows:
for mixed sewers the capital costs are 183 Euro/m and maintenance 3.9 Euro/m/y for separate
sewers this is 376 and 4.6, and for vacuum sewers 8.5 and 3.9. When the decision support tool
chooses “no blackwater transport” this option is overruled when a large-scale technology is
chosen and consequently a mixed sewer is used for the calculations instead. Thus the cheapest
system, according to the used data, is the vacuum sewer. This raises the question whether the
data are realistic because the perception is that vacuum sewers are very expensive in
maintenance. Therefore, we have to note that the data for sewer systems have to be rechecked.
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To explore the sensitivity of the solutions to changes in the data set we chose to change all
removal rates, since we feel that these are important while at the same time the accuracy of
these data is hard to estimate. As we assume that for some of the removal rates, for instance
those on solid removal, the data may change within a range of 10% we constructed a dataset
in which all removal rates increase by 5%. For faecal coliform removal an increase of 5% is
unrealistic, therefore we decreased the distance between the former value and 100% by 5%.
Table 38: Exploring the sensitivity to changes in input data by increasing the removal rates
for all technologies with 5% (except for faecal coliform removal).
Solver:
Solution space:
Data-set:
min(f)=-1*objective
number ofevaluations with min(f)
global solver
solution space NR1
nutrients
nutrients_RR
105
-83 to –82
-56 to –55
360
360
Sustainability indicators for best set of solutions:
Acceptance
1-3 (0-1)
1–3 (0-1)
Costs
(euro/cap/y)
1.1E3 –
1.1E3 –
2.9E5
2.9E5
Fertiliser
(kg/cap/y)
2.2E4
2.2E4
Soil Conditioner
(kg/cap/y)
0
0
Waste
(kg/cap/y)
2.2E4
2.2E4
Best set of domestic water systems defined by x-variables*:
x7
Toilet
5,6
5,6
x9
Blackwater transport
x11
x13
x14
x15
x16
x17
x18
x19
x20
x21
x24
Greywater transport
Blackwater treatment 1
Blackwater treatment 2
Blackwater treatment 3
Blackwater treatment 4
Greywater treatment 1
Greywater treatment 2
Rainwater treatment
Sludge treatment
Greywater splitter
Yellowwater splitter
1,2,3
1,2,3
{2}
4
6
3
{7}
9
8
{4}
{4}
1
0,1
{2}
4
6
3
{7}
9
8
{4}
{4}
1
0
nutrients
(min – max)
0 – 8#
4.6E3! – 3.3E6
nutrients_RR105
(min - max)
0 – 8#
8.0E1 – 3.1E6
0 – 2.2E4
0 – 2.2E4
0 – 1.2E3
0 – 7.6E1
2.2E4 – 4.9E4
2.2E4 – 5.0E4
explanation:
5= vacuum toilet,
6= urine separation toilet,
1= combined sewer,
2= separate sewer,
3= vacuum sewer,
none
none
6= anaerobic digestion 50,000 pe
3= constructed wetland 1,000 pe
7= none
9= none
8= none
4= none
4= none
1= to on-site treatment
0= to greywater
1= to yellowwater treatment
* Note that the x-values not shown are the same for all the systems, namely x1=0, x2=0, x3=0, x4=0, x5=3, x6=4, x8=6,
x10=3, x12=2, x16=7, x22=0, x23=1, the values of x22 and x23 indicate that all rainwater is directed to rainwater
treatment all others indicate no treatment or no use of this water source.
# Note that the normalisation factor for acceptance is given here, the maximum is based on the number of technologies
that are chosen but it may not be possible to score 1 for all 7 technologies.
! Note: the normalisation factor for minimal costs is incorrect as lower values have been found, this is a risk of using the
genetic-algorithm-solver to set the normalisation factors, this makes the normalisation a bit unfair.
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With this new dataset we ran an optimisation with the global solver, for 6999 evaluations. The
results of this optimisation, called nutrients-RR105, are compared with the results of previous
calculations that are called nutrients in Table 38. There is only 1 difference in the results of
the 2 optimisations: the nutrients-RR105 optimisation did not find the value 1 for the
yellowwater splitter (x24). The value of this particular variable is not significant. The decision
support tool simply does not acknowledge that the decision on where to direct yellowwater
(x24=SplitYW2) is empty because no urine is separated when selecting a vacuum toilet.
Another difference is the normalisation factor for costs, which was set incorrectly for the
nutrients case, as lower values than the minimum used for normalisation were found later.
This is a risk one takes when using the genetic algorithm for setting the normalisation factors.
For the nutrients-RR105 data the minimum normalisation factor is lower and probably
correct, but this does not affect the results for the best range, as costs were not weighted in the
objective.
6.2 Creating life support systems
Besides systems for nutrient removal, we also want to select systems that focus on water
conservation and reuse. When combining both nutrient recycling and water recycling one
approaches the so-called life-support systems, which are small-scale systems that maintain an
ecosystem supporting human life.
6.2.1
Solution space
The solution spaces defined for life support systems includes the most relevant choices with
respect to recycling and small-scale treatment and is shown in Table 35 on page 102. The data
set used in the optimisation for this scenario is data2003_water. In this data set we lowered
the concentrations of heavy metals in drinking water, rainwater, and in the added
contaminants (Cu and Zn concentrations are set zero). We did this to enable the use of treated
rainwater for consumption and to allow domestic reuse of treated wastewater. Low enough
concentrations can be realised be realised by using appropriate materials for water pipes and
roofing.
6.2.2
Selected systems
For the selection of life support systems we ran 6999 evaluations with the global solver, the
results are shown in Table 39. In this case we sorted the results with regard to the objective
and on the sustainability indicators costs and fertiliser (see the different columns in Table 39).
Note that the objective for optimisation is the weighted sum of drinking water use, domestic
reuse, and waste. Weighting factors were; –100 for drinking water use and for waste and 100
for domestic reuse. Furthermore, the rainwater data used are for the Netherlands, therefore,
rainwater is quite plentiful and available all year round.
The best solutions found when sorting on the objective are shown in the column "Water use &
reuse" of Table 39. These solutions minimise drinking water use by using a large rainwater
system and a disinfection unit to treat rainwater and treated wastewater water before use and
by the use of a dry composting toilet. Domestic reuse is realised by treating the greywater,
which is mixed with some of the rainwater, with a membrane bioreactor.
The high costs estimated for these solutions are due to two expensive components, namely the
disinfection unit (1.8E5 euro/cap/y for UV) and the membrane bioreactor (2.4E5 euro/cap/y).
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Table 39: Systems selected in 6999 evaluations by the global solver, using the following weighting factors:
domestic reuse +100, drinking water use -100, and waste -100.
Solution space LS1 - data2003_water
Sorted on:
Water use
Costs
Domestic Fertiliser
& reuse@
reuse
No.of systems:
1152
92
108
1696
Sustainability indicators for set of best solutions:
(min-max)
Total water use
(m3/cap/y)
2.4E1
2.4E1
2.7E1
2.6E1
2.4E1 – 2.9E1
Rainwater use
(m3/cap/y)
1.9E1
1.9E1
9.3
1.9E1
0 –1.9E1
Costs
(euro/cap/y)
2.6E5 –
1.6E4
8.9E52.9E4 –
4.6E3! –2.9E6
4.2E5
1.1E6
5.5E5
Energy
(kWh/cap/y)
5.1E4
4.1E4
5.5E3 3.8E4 –
-1.4E1 to 4.9E4
6.3E3
1.8E4
Acceptance #
3 (1)
3 (1)
2 (1)
3 – 4 (1)
2 - 4 (1) #
Sustainable behaviour #
3 (0)
3 (0)
3 (0)
3 (0)
3 (0)
Fertiliser
(kg/cap/y)
3.5E2
3.5E2
0
6.5E2
0 – 3.5E2 or diluted:
6.5E2
Soil conditioner
(kg/cap/y)
6.9E1
6.9E1
8.3E2
0 – 1.1E2
0 – 2.7E3
Domestic reuse
(kg/cap/y)
6.1E3
0
2.2E4
0 - 6.1E3
0 – 2.2E4
Waste
(kg/cap/y)
3.0E3
9.2E3
4.5E3
3.3E3 –
3.0E3 – 2.1E4 &
1.0E4
The best domestic water systems*:
Explanation:
x5
rw system
2
2
1
2
1= basic, 2= large
x6
Disinfection
1,3
1
1,3
1,3
1= RO, 3= UV
x7
Toilet
9
9
8
6
6= u.sep. toilet,
8= compost lf
9= compost dry
x8
On-site treatment
6
6
6
1,2,6
1= AS 5pe, 2= AD 5pe,
6= none
x9
bw transport
4
4
4
1,2,4
1= sewer, 2= separate,
4= none
x11 gw transport
2
2
2
2
2= none
x14 bw treat. 2
14
14
14
1,5,11,14
1= AS 50pe, 5=AD
400pe, 11= RBC 5pe,
14= none
x15 bw treat. 3
8
8
8
1,4,8
1= wetland 5pe, 4=TF
5pe, 8= none
x17 Greywater treatment 1
1
9
1
1,9
1= MBR
9= none
x18 Greywater treatment 2
4,8
8
8
4,7,8
4= wetland 5pe
7= UV,8= none
@ Sorted on objective, weighted to minimised drinking water use and waste and maximise domestic reuse.
* The variables that are fixed by their upper and lower bounds are not shown in the table, as these are the same for all
cases. Their values are: x1=0 (no household water), x2=1 and x3=1 (rainwater use throughout the household if
disinfection unit is available), x4=1 (water conservation), x10=3 and x11=2 and x12=2 (no transport of greywater,
yellowwater, and blacksludge), x13=4 (no treatment in bw1), x16=4 (no treatment in bw4), x19=3 (composting of
sludge), x20=4 (no rainwater treatment), x21=0 (greywater to on-site treatment), x22=1 and x23=0 (rainwater to
blackwater or greywater), x24=1 (yellowwater to yellowwater treatment).
# Note: advantages and between brackets the disadvantages. The normalisation factors for acceptance and sustainable
behaviour are maximal 7 based on the number of technologies that can be chosen, however the maximum found in the
first 6999 evaluations are 4 and 3.
& Note: minimum waste was assumed zero, however for this dataset a minimum of 3.0E3 is found.
! Note: the normalisation factor is not correct as we found a lower value in the results for Costs (risk of ga-solver).
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The rainwater system is responsible for most of the energy use (3.8E3 kWh/cap/y), the rest is
used in the membrane bioreactor for greywater treatment (1.3E3 kWh/cap/y). The urine is
separated and classified as fertiliser; this means that the maximum amount of fertiliser is
produced undiluted as a dry toilet is used. The waste consists of rainwater and run-off that
cannot be classified as suitable for domestic reuse because the restriction for total suspended
solids is not met (restriction: TSS < 15 mg/l, concentration calculated: 109 mg TSS /l). This
means that with additional treatment, all waste could be eliminated and the domestic reuse
could be increased to 2.5E4 kg/cap/y.
The best system selected from the set of solutions, when sorting on costs, relies solely on a
composting toilet. This means that greywater and rainwater and run-off are classified as
waste. The compost toilet produces compost that can be classified as soil conditioner and
separates the urine that can be classified as fertiliser.
Note that the cheaper solar dry toilet is not chosen because of the climate constraint, as this
toilet works only in hot and dry climates. When the temperature is changed to 30 ºC in the
data file and an optimisation is run with the genetic algorithm the best system consists of a
solar dry toilet, a large rainwater system and UV-disinfection for water used in the kitchen,
and a membrane bioreactor to make greywater suitable for reuse (data2003_water with
Temperature=30, solution space LS1). This configuration scores well on total water use
(24m3/cap/y), on domestic reuse (6.1E3 kg/cap/y, all originating from greywater), and on
waste (3.0E3 kg/cap/y originating from runoff). Also the amount of potential fertiliser is high
(3.5E2 kg/cap/d) originating from separated urine, and the soil conditioner (6.9E1 kg/cap/d) is
produced from the dried faeces and bio-wastes. The costs are high, 4.2E5 euro/cap/y, due to
the choice of a membrane bioreactor for greywater treatment. Note that the tight restrictions
on domestic reuse are causing these high costs. Based on the fact that water reused in the
household will be treated again before use the restriction can be lowered, such that cheaper
technologies, for instance a wetland, maybe used to produce water for domestic reuse,.
When sorting on domestic reuse, the maximum reuse, 6.1E6 kg/cap/y, based on greywater
recycling is realised. However, the fact that more greywater is produced than for instance
indicated in the column "Water use & reuse" of Table 39 is the result of the larger inflow of
rainwater as less rainwater is used in the household due to choosing a small-scale rainwater
system instead of a large-scale rainwater system. Therefore, this is not a smart solution and
the question rises whether the choice of directing all rainwater to greywater, as defined in the
solution space, is the right choice as all this water is expensively treated in the membrane
bioreactor. Remarkable in the "Domestic reuse column" of Table 39 is furthermore the zero
for fertiliser while using a urine-separating, low-flush composting toilet. The urine separated
is not classified as fertiliser since the concentration of Ptot is too low, namely 14 mg/kg total
weight while the restriction is 15 mg/kg. This is due to the dilution in the low flush
composting toilet.
When sorting on fertiliser we seem to find a higher amount of fertiliser, however due to the
choice of toilet using flush water the same amount of fertiliser is collected but now diluted.
Since the classification of fertiliser only applies to the yellowwater, the urine separation toilet
is the determining unit, whilst the blackwater and greywater treatment do not influence the
fertiliser production. Therefore, the column "Fertiliser" in Table 39 shows all the alternatives
for these treatment lines that are contained in the solution space. Only for on-site treatment
the options membrane and membrane bioreactor (options 3 and 4) are missing, but apparently,
since "no treatment" (option 6) is included, the model overruled these options probably based
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6 Sustainable domestic water systems
on influent restrictions. Note that the high energy use is again caused by the use of the largescale rainwater system.
6.2.3
Visualising results
In the following figures an overview of the solutions found is given. In Figure 21 the
objective value for the first 6999 evaluations is given. In Figure 22 values of the most
interesting decision variables at the different values of the objective are given. For instance it
is clearly visible that at the first breakpoint, were the objective f changes from 51 to 74 after
1152 evaluations, the choices for the toilet and the technology for the second step in
blackwater treatment change. The toilet (x7) changes from a dry composting toilet (x7=9) to a
urine separation toilet (x7=6). The second step in blackwater treatment (x14) changes from
"no treatment" (x14=14) to rotating biological contactors for 5 pe (x14=11).
Figure 21: Changing objective for 6999 evaluations (data2003_water, solution
space LS1, Note: minimisation of the objective function, thus the lower the better).
Figure 22: Decision variables at different levels of the objective (data2003_water,
solution space LS1). Clearly visible are the breakpoints of the objective’s value (see
also Figure 24 above). Breakpoints: f changes from 51 to 74 at 1152 evaluations,
74 to 87 at 1782, 117 to 131 at 5727, 132 to 137 at 5835, 142 to 190 at 6555, 190 to
231 at 6591, 231 to 260 at 6627, and 269 to 281 at 6987 evaluations.
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6 Sustainable domestic water systems
The Figures 23 and 24 give the details around this breakpoint for the decision variables and
for a selection of sustainability indicators. In Figure 24, the second subplot shows the
sustainability indicators that make up the objective. This figure shows that the main change in
the objective value is due to the increased drinking water consumption. When returning to
Figure 23 or 25 we can conclude that this must be due to the change in toilet choice, as the dry
composting toilets is replaced by a urine separation toilet that uses flush water.
Figure 23: Changing objective and decision variables for
the range 1130 to 1170 evaluations (first breakpoint in
results, data2003_water, Solution space LS1).
Figure 24: Selection of sustainability indicators for the
range 1130 to 1170 evaluations (first breakpoint in results,
data2003_water, solution space LS1).
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6.3
6.3.1
6 Sustainable domestic water systems
Conclusions
Conclusions
Sustainable systems
The decision support tool finds 2 routes for nutrient recycling, namely: (1) uses urine
separation, and (2) the production of clean sludge. This last route is questionable, as the
amount and quality of the nutrients that can be recycled this way are overestimated by the
decision support tool. For the fertiliser production based on urine separation, the decision
support tool indicated that the urine separation in a composting toilet is a good option since
this gives a good score on soil conditioner as well. The handling of urine and compost will be
crucial for the acceptance by the end user. Centralised composting and urine separation after
the toilet bowl or with urinals will lower the quantity and possibly the quality of nutrients that
can be recycled, but may be crucial for successful implementation of this domestic water
system.
To minimise drinking water use and to maximise water reuse the decision support tool
suggests using a large-scale rainwater system, a small-scale disinfection unit, a dry toilet, and
a membrane bioreactor for greywater reuse. This can be combined with urine separation to
produce fertiliser. When opting for a cheap solution the solar dry toilet is a good alternative in
hot climates. The domestic reuse is based on the expensive membrane bioreactor. Further,
research is needed to indicate whether cheaper options based on rainwater and/or greywater
recycling using cheaper technologies such as wetlands can produce water with a quality
suitable for domestic reuse. With respect to the rainwater system it goes without saying that
their use depends on the rainfall and that the large-scale system using pumps requiring quite
some energy.
Based on the technology choices made by the decision support tool we can conclude that
technologies not commonly used today, such as urine separation, membrane bioreactors, and
rainwater systems, may become important in future domestic water systems aiming at the
reuse of nutrients and water. These findings are in line with the literature (see Chapter 2).
Whether all the selected systems are as good as indicated by the optimisation in real life
depends on how realistic the simple models, the data and the restrictions are. Therefore,
another evaluation of these inputs and new optimisation runs is necessary for fine-tuning the
decision support tool.
6.3.2
Evaluating the decision support tool
We found that the decision support tool is very handy for research purposes; some practical
aspects of using the tool are described in the next 3 paragraphs.
6.3.2.1 Different solvers
For the nutrient recycling case we did optimisations with all three solvers, namely a mixed
integer solver, a genetic algorithm, and a global solver. We found that the mixed integer
solver is the least efficient due to the random selection of starting points. However, there is a
reasonably good change to find the global optimum within 1500 to 2000 runs. The genetic
algorithm is quite efficient as the almost complete set of systems forming the global optimum
is often found within 400 to 500 generations, with an average calculation time of about 10
minutes on a regular 662MHz pc. However, one can never be sure that the complete set of
“best” systems is identified. The global solver has a reasonable good search strategy.
Although, one will have to check the total solution set when constructing an optimal set of
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6 Sustainable domestic water systems
solutions, Figure 20 on page 105 illustrates this. Still, we found that the most interesting
solutions were indeed included in the first 6999 evaluations. The main advantage of this
solver is that the whole set of best solutions can be found. However, even for the relatively
small solution spaces we used the number of evaluations for the total number of possible
solutions was about 26,000 meaning that the calculation time is quite extensive (1 to 2 s per
evaluation on a regular 662MHz pc, thus about 10 hours).
Based on these finding we stopped using the mixed integer solver. We used the geneticalgorithm for a quick screening, to find promising systems, the different levels in the
objective value, and to determine estimates for the normalisation factors. Some of these
estimates were too low, which had to be correct for the important sustainability indicators.
Therefore, one might consider using different runs of the genetic-algorithm solver or
alternatively increasing the number of generations to increase the chance of finding the
correct values. We used the global solver for more thorough analysis, for instance by making
subsets and re-sort the solutions for different variables than the ones included in the objective.
Such an analysis we often started with the best 6999 evaluations. For more details on a
comparative runs with the different solvers see the overview in Appendix 4 on results.
6.3.2.2 Solution space
We chose to work with limited solution spaces containing options interesting for nutrient
recycling and life support. The size of our solution spaces was set to a maximum of 26,000
possible combinations of decision variables solely for the purpose of limiting the calculation
time. However, since the search strategies of the genetic-algorithm and the global solver work
reasonably well even with our discretely changing objective one can consider to use larger
solution spaces and limit the number of evaluations for the global solver or use only the much
faster genetic algorithm.
6.3.2.3 Categorisation of out going streams as “Fertiliser”
While evaluating the results from the nutrient recycling case we found that the restrictions
used to classify outgoing streams as potential fertiliser are too limited. Two changes are
required; (1) a change in the procedure of calculating as now the total weight of streams is
used such that the maximum dilution that still satisfies the constraints is valued highest, and
(2) the additional of restrictions to take in account that not all P contained in Ptotal will be
available for reuse and to take in account the availability of other nutrienst as for instance
potassium. We did not find the data on the availability of P and other nutrients in literature,
therefore we were unable to fine tune the restrictions for “Fertiliser”.
6.3.2.4 Categorisation of out going streams as “Waste”
In our approach we chose to categorise the outgoing streams in the following 10 categories:
Combined sewer overflow, Discharge, Domestic reuse, Fertiliser, Gas, Infiltration, Irrigation,
Landfill, Soil conditioner, and Waste. All outgoing streams that cannot be categorised
otherwise are categorised as waste. This means that one has to check the restrictions that
determine the categorisation, the incoming streams, and the performance of the technologies
to make sure one agrees on the categorisation. For instance we found that the tight restrictions
on copper, zinc, and solids in combination with high concentrations in incoming streams
resulted in large amounts of waste produced. We chose to loosen some of the restrictions and
to lower the copper and zinc concentration in incoming streams (see Table 13 on page 56).
Still run-off maybe categorised as waste due to a slightly higher concentration of solids than
allowed. Therefore, it is always good to check what the decision tool considers as “waste” and
whether one wants to fine-tune the weighting factors, add technologies to solution space, or
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6 Sustainable domestic water systems
change restrictions, concentrations, or performance of technologies. The limited options for
sludge treatment may also be a reason for larger volumes of waste produced.
6.3.2.5 Improving the decision support tool
Improvements that can be made to the tool are:
(1) Some corrections are made after optimisation (in dws2003out.m and SortResults.m),
this is done because some restrictions are for instance based on influent characteristics,
which are invisible to the solver. However, some correction that are made after
optimisation can be seen by the solver, for instance directing yellowwater that does
not exist (no urine sorting toilet makes SplitYW2 irrelevant), choosing blackwater
treatment, while using a dry toilet, which means that there is no blackwater stream, or
choosing greywater treatment while the greywater is directed to blackwater
(x21=SplitGW1=0). Similar for rainwater and yellowwater directions. By giving the
solvers the information on these restrictions, that are visible to the solver, one can
reduce the number of evaluations and therefore the calculation time. This can be done
by inserting simple rules in the linear constraint matrix, the so-called A-matrix, of the
solver.
(2) Reconsider routes in the model of the decision support tool. For instance, a route that
is presently not included is the separation of different types of greywater. One may
consider to direct wastewater from the kitchen and washing to blackwater while all
other greywater streams are directed to greywater treatment. This may make sense if
pollutants such as phosphorus from cleaning agents or faecal coliforms for instance
from washing diapers are present in the wastewater from washing and the kitchen. A
route that one may want to forbid is for instance; returning separated urine to
blackwater by directing it to greywater and directing the greywater to blackwater.
Such U-turns do not make sense in everyday life and could therefore be eliminated
from the model through logical constraints. Although one should realise that if
choosing a urine separation toilet the urine is always separated, thus than the only
route to blackwater is the one described above.
(3) Data and restrictions have to be checked by experts to make sure that all are realistic.
The presently used data rule out the categories Discharge, Landfill and Infiltration, for
outgoing streams as result there is always Waste production.
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7 Conclusions & Discussion
7 Conclusions & Discussion
The objectives for this research are defined as: (1) to develop a methodology to assess the
sustainability of domestic water systems and (2) to implement and (3) use this methodology to
select promising water systems that may help solving water problem. This chapter
summarises and discusses the answers to these objectives and the conclusions found.
7.1 Sustainable water management
A concrete challenge for this new millennium is to meet the UN target that aims at providing
safe drinking water and hygienic sanitation to all people on earth by the year 2025. The
problems are pressing, as it is estimated that polluted water causes the death of 15 million
young children annually. Although experts already predicted that it is impossible to meet the
challenge, the associated range of problems is even broader, as many of the existing water
systems do not provide an integral solution, such that we now face problems like
eutrophication, heavy metals in sludge, water shortages. Even more complex problems are
emerging, such as the loss of fertility due to traces of medicine and chemicals in the water,
and scarcity of nutrients for food production due to the disruption of the nitrogen and
phosphorus cycle.
How can we try to meet those challenges? A major aspect in finding solutions maybe the fact
that problems have become so complex that there is no single optimal solution. The solutions
we are seeking have to be compatible with, or readily adaptable to the local natural,
economic, technical, and social-cultural environment. In addition, the solution should offer
possibilities for further development. The solutions must be formulated with a long-term
global view in mind, such that these do not trigger new problems. Or in other words: we are
looking for sustainable solutions, carefully balancing the use of different resources such as
environmental, economical, and social-cultural resources in a way that the contribution to
local and global problems is minimised or are at least known and accounted for.
7.2 Assessing sustainability
To analyse the full scope of process sustainability we can use commonly available tools such
as cost benefit analysis, life cycle assessment, actor analysis, etc. To reach a final decision the
different indicators have to be normalised and weighted to integrate the indicators into a
single final objective, which makes the search for a sustainable solution a multi-objective
optimisation problem. Important objectives in selecting sustainable domestic water systems
are: (1) minimise costs, (2) the use of resources such as water, energy and space, (3) maximise
the production of clean water, nutrients, and biomass for reuse, (4) minimise harmful waste
products, (5) maximise social-cultural embedding through acceptance, participation, and
stimulation of sustainable behaviour.
Due to the complexity and the dynamic understanding of today’s problems there is a risk of
introducing new problems when implementing technical solutions. To ensure that solutions
have a positive overall effect, one needs a clear goal and a broadened scope, for instance by
implementing a product chain analysis or a life cycle assessment. We cannot define guidelines
for the goal and scope definition, but we would advise to try and include as many solutions as
possible. This can be done through extension of the super structure but sometimes much
simpler for instance by adding a new category to the classification of outgoing streams. We
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7 Conclusions & Discussion
experienced the need to extend our scope 3 times: (1) water use was added to include water
conservation, reuse, and the possibility to keep wastewater streams separate; (2) different
water sources such as rainwater, household water (water with bathing water quality possibly
obtained from recycling), and drinking water were added; (3) organic waste was added as its
treatment may be combined with wastewater treatment and is important in nutrient recycling.
In principle our sustainability assessment is a combination of existing tools, as mentioned
above. The main features that make the combination more than a sum of the parts are: (1) the
broad scope, (2) the set of sustainability indicators, (3) the process design oriented approach
for modelling through the use of a superstructure, and (4) selection of optimal structures by
multi-objective integer optimisation. We implemented this sustainability assessment in a
model-based decision support tool for the selection of sustainable domestic water systems.
7.3 The decision support tool
7.3.1
Data
Although, we did a quite extensive literature review to collect the data, we find that both
accuracy of the data and the completeness can be improved. Even though, we managed to
extract a reasonable set of data by taking information from multiple sources. Only a few data,
for instance space occupation for drinking water supply and the costs of small UVdisinfection and the solar dry toilet, were estimated based on a single source, which makes
these data less reliable due to the lack of comparison. For the data taken from multiple sources
other questions arise. For instance: on how well these data are comparable. For some data
there are more general problems arising for example: BOD-removal change with season and
the influent. Therefore the accuracy of the data remains an uncertainty that must be kept in
mind when interpreting the results.
The data were carefully selected and found suitable for use in our research project. We
constructed 37 models of 13 different technologies that quantify 27 sustainability indicators.
We encourage others to use the decision support tool and provide therefore a description and
full access to the data. There are no data hidden in the model, all data used are stored in a
spreadsheet and well documented (see Chapter 4 and 5 and Appendix 1 and 3).
7.3.2
Indicators
Based on the different dimensions of sustainability we defined a set of indicators including
economic, environmental, and social-cultural aspects. In addition we included functional
indicators that are used to account for characteristics of technologies such as robustness,
adaptability, and maintenance (see Figure 25).
Some of the indicators are not quantified but provide a qualitative measure, for instance social
acceptance and robustness. These are used to indicate a potential advantage or disadvantage
of a certain technology. Similarly, as for the quantified indicators, theses indicators are
weighted to determine their impact on the objective function. Qualitative indicators can be
used in the objective to make “go” or “no go” decisions based on qualitative indicators.
Another approach is to use this indicators not in the decision but in the implementation, for
instance by giving extra attention to institutional embedding when a disadvantage comes up in
the chosen solution for institutional requirements. We found it important to include qualitative
indicators because it makes the decision making process more transparent.
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7 Conclusions & Discussion
Although, we included 27 sustainability indicators, some others could be considered in
addition, for instance indicators for emissions to air and labour intensity, which here were left
out due to lack of data. The analysis provided by the decision support tool could be further
strengthened by extending the use of the indicators on reuse to other parts of the model and
not only to the classification of outgoing streams. In particular we think about the economical
and social-cultural effect of reuse.
Figure 25: Sustainability indicators categorised as technical, economic,
environmental, and social-cultural indicators.
When writing the model we at first included reuse of blackwater and greywater in the
household as an internal recycle, however, this required an initial guess and made the
calculations slow. Therefore, we included domestic reuse as a classification of the outgoing
stream. Furthermore, to include reuse in agriculture or industry requires quite a large
extension of the model and consequently also of the data. On the other hand, subsidization of
artificial fertiliser as well as social-cultural aspects of the reuse of human excreta in
agriculture are of crucial important in nutrient recycling.
7.3.3
Models
The quantification of the sustainability indicators is based on a model that represents the mass
balances of the domestic water system. This model is constructed as a superstructure by
superimposing a large number of known options for domestic water use, conservation and
reuse, as well as waste treatment. In the model structure, 37 simple static models of 13
different technologies are contained in the processing units. The optimisation selects a
combination of technologies resulting in a complete model of a domestic water system.
All calculations are performed with this Matlab-Simulink model of the domestic water
system, quantifying all outgoing streams, energy use, costs, land area requirements, and the
social-cultural aspects of the chosen configuration. Some of the model’s results are directly
used as sustainability indicator, while others are first converted or categorised. For instance,
to indicate the reuse potential of the different streams depends on the concentration of
different compounds and the restrictions for safe reuse
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7.3.4
7 Conclusions & Discussion
Optimisation
We defined the selection of optimal systems as a multi-objective integer optimisation
problem. To solve the problem we experimented with different solvers, namely a global
solver, a mixed-integer-solver, and a genetic algorithm. However, we found that due to the
large number of possible combinations36 and the discrete changes in sustainability it is
difficult to find global optima within a reasonable calculation time. To be sure that the global
optimum is found we used extensive search practised by the global-solver to calculate the
value of the sustainability indicators for all the possible domestic water systems within the
solution space. Therefore, we choose to reduce the problem size by defining a smaller
solution space, which obviously results in shorter computation times. The calculations show
that the genetic algorithm can usually indicate quickly what is probably the best score on the
objective and identify a number of domestic water systems that belong to this set of best
systems. Therefore we used the genetic algorithm to estimate the highest and lowest score on
the different sustainability indicators for normalisation and to explore the sensitivity with
respect to weighting factors.
7.3.5
Using the decision support tool
We used the tool to gain insight in the sustainability of different water systems. For this
purpose, we defined 2 scenarios, (1) nutrient recycling, and (2) life support systems that
combine nutrient and water reuse on relative small-scale. To select domestic water systems
for these scenarios we defined solutions spaces and set data and weighting factors according
to the aims of these scenarios, not considering a defined case where one has to incorporate
local conditions and the interests of the involved actors and reach a final decision. To use the
tool for decisions support requires another approach, as the discussions of all involved actors
on data, technologies, weighting factors, and the outcomes of the decision support tool will be
a main aspect in reaching a decision. Different actor may want to use different weighting
factors or even different data such that discussions and different iteration steps will be
necessary to reach a consensus on what system to install in the particular situation.
The role of weighting factors in selecting sustainable solution is crucial. One of the ideas
behind the decision support tool is to challenge decision makers to play with the weighting
factors and gain insight into the sustainability of the different systems and the trade-offs
made. There are two ways to use the weighting factors: (1) to define a ‘go no go’ decision or
(2) to value an indicator in a negotiation allowing for trade-offs with other indicators. We
experienced that decision makers may probably use both options starting first with a limited
number of weighting factors (1 to 5) that define a subset in the solution space. Thus the ‘go no
go’ indicators are given a high positive or negative weight to distinguish a subset of solutions
that satisfies the ‘go no go’ set by the decision maker. In a second step one ‘negotiates’
alternative solutions based on other sustainable indicators with in this subset.
Because of its use for research, the decision support tool remained more or less in the design
phase, meaning that it has been and can be adapted to incorporate different options. But it is
certainly possible to work with the current version though it does require some expertise in
the use of Matlab and the user must be willing to invest some time in exploring the set-up of
the tool and modifies it to ones need. The files that make up the tool are self documented and
additional information on how to use the tool is included in this thesis (see Chapter 3 and 6
36
The estimated number of domestic water systems contained in the total solution space is 7*1012 and to
quantify the sustainability indicators for a single system a regular 662MHz personal computer takes 1 to 2
seconds this means that the total calculation time becomes 3.6*105 years.
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7 Conclusions & Discussion
and Appendix 2). When using the tool one should be aware that it is not an expert system, for
instance some solutions found by the tool may make sense only mathematically. Therefore,
the experts will have to play with the tool using their expertise and the outcome of the
calculations to select the best systems for their projects. The tool offers possibility for
adaptations and even for inserting new data and new models. To make the tool into a readyto-use decision support tool would require the development of a user-friendly interface and
fine-tuning of the models and mostly logical restrictions to eliminate certain meaningless
choices. Furthermore, sludge treatment options included in the tool are limited. The use of
sludge as bio-fuel as practised in the Dutch cement industry is for instance not included.
7.4 Sustainable domestic water systems
7.4.1
Sustainable systems
Based on the technology choices made by the decision support tool we can conclude that
technologies not commonly used today, such as urine separation, membrane bioreactors, and
rainwater systems, may become important in future domestic water systems aiming at the
reuse of nutrients and water. These findings are in line with the literature as the urine
separation toilet is describe as best option for nutrient recycling for instance by Bengtsonn,
Hellström, and Kärrman, (see Chapter 2). Chen and Beck described wetlands, micro-filtration
and reverse osmosis as essential to success (see Chapter 2). Our findings do support their
conclusion as our decision support system selected the membrane bioreactor for water reuse,
and in some cases reverse osmosis for disinfection and a constructed wetland for wastewater
treatment.
We did not find the more conventional solutions as for instance described by Emmerson and
Mels (see Chapter 2), this is due to the fact that we aim at recycling of nutrients and water
while they aim at comparing and optimising existing treatment systems. Furthermore, the
sludge treatment included in the decision support tool is very limited. If clean sludge can be
produced and/or reused in different ways then the treatment configurations based on
conventional treatment may be promising too. Still, based on the selections made by the
decision support tool and its present settings we conclude that, if future domestic water
systems aim at nutrient and water reuse, it is likely that the conventional systems will have to
be replaced by new systems that allow separation of wastewater streams at the source, a
conclusion that is very much in line with what is reported in the recent literature. The reuse of
water will probably trigger decentralised treatment of rainwater and greywater and may
introduce disinfection on household scale. For nutrient recycling decentralisation is not
necessary although the separation and reuse of urine and compost may trigger this as well.
7.4.2
Trade-offs
We found a trade-off between domestic reuse and costs, based on the choice of the membrane
bioreactor for the treatment of the mixture of greywater and rainwater. Possibly cheaper
alternative may become applicable if rainwater is treated separately or if the performance of
cheaper technologies such as wetlands is improved or restrictions for reuse are lowered.
Another trade-off we found was between rainwater use and energy use, as the larger rainwater
system is not gravity based, energy used for pumping adds up. Alternatives maybe found in
larger rainwater systems based on gravity or the use of green energy sources such as solar
energy.
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7 Conclusions & Discussion
Furthermore, a small trade-off between soil conditioner and fertiliser is found. However, as
fertiliser is perceived being a product with higher quality this is of no importance. Similar
there is a trade-off between rainwater use and domestic reuse, as domestic reuse will be based
on greywater and rainwater, while rainwater used goes partly to blackwater. But since it is
more efficient to use rainwater right away than to mix and treat it with greywater first this is a
trade-off of no importance.
Since no trade-off between domestic reuse and fertiliser was found, it is very well possible to
combine the goals of our two scenarios and construct domestic water systems that recycle
both nutrients and water thereby approaching life support systems.
7.4.3
Decisive variables
When looking for systems that recycle nutrients classification of the outgoing streams is of
major importance. Therefore, restrictions set and the incoming concentrations of compounds
restricted are very important. In the case of nutrient recycling the production of fertiliser and
soil conditioner are essential, and therefore the restriction and concentrations in the incoming
streams for Ptot (mg/kg total weight), Cu and Zn (in fertiliser as mg/kg Ptot and in soil
conditioner mg/kg TS), and for soil conditioner in addition the total solid content.
Furthermore, when fertiliser production is based on urine separation, the decision variables
that determine the toilet choice (x7) and the yellowwater splitter (if x24=SplitYW2) are
playing a central role in the decision process (if x7 > 7 than a toilet with urine separation is
chosen, and when x24=1 the separated urine is directed to yellowwater treatment).
In systems aiming at minimizing drinking water use decisive variables are the choice for a
rainwater system (x5), the choice for a disinfection unit (x6) to allow rainwater use in the
kitchen and for personal hygiene, and the choice for a dry toilet (x7). If one wants to
maximise domestic reuse decisive variables are the choice for greywater treatment (x17 and
x18) and the splitter that directs greywater to greywater treatment (x21=SplitGW1). We found
that the choice for a membrane bioreactor was decisive in reaching the quality needed for
domestic reuse, however, this depends on the restrictions set and the quality of incoming
streams. Rainwater is also a good source for domestic reuse but can of course better be used
directly.
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Sustainable Wastewater Treatment
Glossary: Acronyms & symbols and terminology
Glossary
Acronyms & Symbols:
á
A
AD
AS
BOD
b_L
b_U
cap
COD
CSO
CW
d
ds
DO
F
F/M ratio
FB
FC
KNMI
l
MBR
MF
Million
MINLP
MIP
MLSS
ç
n̂
NF
NIPO
PAH
PCB
pe
PFU
RBC
RIVM
RO
RoW
SC
SI
SS
TF
ThOD
TKN
TOC
tr
TS
tsi
TSS
= Wweihjting factor
= In optimisation used as A-matrix, the constants in the linear constraints
= Anaerobic digestion
= Activated sludge
= Biological Oxygen Demand (see terminology)
= In optimisation used as lower boudns for the A-matrix, the linear constraints
= In optimisation used as upper bounds for the A-matrix, the lienear constraints
= Capita
= Chemical Oxygen Demand (see terminology)
= Combined Sewer Overflow
= Constructed wetland
= Day
= Dry solids
= Dissolved Oxygen concentration
= Fertiliser or basis for fertiliser production
= Food-Microorganism ratio
= Fixed bed reactor
= Faecal Coliforms
= Koninklijk Nederlands Meteorologisch instituut (Royal Dutch Meteorological Institute)
= Litre
= Membrane bioreactor
= Micro filtration, membranes with pore size of 0.02 to 5 ìm, able to remove microbial cells,
large colloids and small particles.
= 106
= Mixed Integer Non-Linear Programming
= Mixed Integer Programming
= Mixed-Liquor Suspended Solids
= Normalisation factor
= Mass flow (gram/day)
= Nano filtration, membranes with pore size between 2 and 5 nm.
= Nederlands Instituut voor Perceptie onderzoek (Dutch Institute for Perception Research)
= Polycyclic Aromatic Hydrocarbon compounds
= PolyChorinated Biphenyl
= Population equivalent
= Plaque Forming Units
= Rotation Biological Contactors
= Rijks Instituut voor Volksgezondheid en Milieu (Dutch Governmental Institute for Public
Health and the Environment)
= Reverse Osmosis, semi-permeable membrane without pores, allowing transport through
diffusion.
= Reuse or Waste matrix, giving the classification of outgoing streams
= Soil condiotner
= Sustainability Indicator
= Suspended Solids
= Trickling filter
= Theoretical Oxygen Demand (see terminology)
= Total Kjeldahl nitrogen
= Total Organic Carbon (see terminology)
= Matrix of removal rates for the different technologies
= Total Solids
= Matrix of sustainability indicators for different technologies
= Total Suspended Solids
Sustainable Wastewater Treatment
UF
UV
VROM
W
WHO
WOB
WRI
x
x_L
x_U
y
Glossary: Acronyms & symbols and terminology
= Ultra Filtration, membranes with pore size between 0.001 and 0.02 ìm, used to separate
macromolecules, to concentrate proteins, removing bacteria, viruses, cellular fragments,
colour, and humic substances.
= Ultra-violet used either as small scale disinfection unit to treat rainwater or treated
wastewater before use, or as large scale system to disinfect effluent from wastewater treatment.
= Ministerie voor Volkshuisvesting Ruimtelijke Ordening en Milieu (Dutch Ministry of
Housing, Spatial Planning and the Environment)
= Waste
= World Health Organisation
= Waterleidingmaatschappij Oost-Brabant (Drinking Water company East Brabant, The
Netherlands) that changed its name to Brabant Water.
= World Resource Institute
= Decision variables
= Lower bounds decision variables
= Upper bounds decision variables
= Year
Terminology:
Aerobic = Aerobic treatment is the removal of organic substances from wastewater by micro-organisms in the
presents of oxygen. The naturally occurring micro-organisms convert soluble and colloidal material into a
dense microbial mass that can be readily separated from the purified liquid using conventional
sedimentation. The breakdown of organic matter relies on two processes, (1) oxidation or respiration
forming a mineral end product, and (2) synthesis producing new biomass. If food supply becomes limiting
microbial cell tissue will be endogeneously respired (auto-oxidation) to obtain energy for cell maintenance.
Anaerobic = In anaerobic treatment wastewater or wastewater sludge is digested in the absence of molecular
oxygen.
Anoxic = Treatment with out oxygen – both molecular and organic.
Autotrophic organisms = Organisms that derive cell carbon from carbon dioxide.
Biological oxygen demand (BOD) = indicates the amount of organic matter in wastewater and is determined by
measuring the dissolved oxygen used by micro-organisms in the biochemical oxidation of organic matter
during a period of 5 days at 20 °C (BOD5 or for a seven days test BOD7). During this biochemical
oxidation inorganic compounds such as ammonia can also be oxidised. The reproductively of nitrifying
bacteria is however slow and it normally takes 6 a 10 days to exert a measurable nitrogenous biochemical
oxygen demand (NBOD). If these bacteria are present initially the sample can be inhibited to measure the
carbonaceous biochemical oxygen demand (CBOD) (Metcalf 1991, p 70-83). Limitations of the BOD test
are: (1) samples need to be seeded with bacteria, (2) pre-treatment maybe necessary (toxic wastes,
ammonia), (3) only biodegradable organics are measures, (4) no stoichiometric validity, (5) long period of
time is required (Metcalf 1991, p 70-83).
Bio-waste = organically biodegradable waste such as garden waste, food waste, etc.
Blackwater = wastewater from the toilet including urine, faeces, toilet paper, flush water etc. Separated urine is
called yellowwater, while separated faeces may be referred to as brown water.
Brownwater = faeces diluted with flush water.
Chemical Oxygen Demand (COD) = COD is measures as the oxygen equivalent of the organic matter that can be
oxidised by using a strong chemical oxidising agent (potassium dichromate) in an acidic medium, at
elevated temperature in presents of a catalyst (silver sulphate). COD is a more accurate measure than BOD.
Typically BOD/COD ratio varies form 0.4 to 0.8 (Metcalf 1991, p 70-83).
Organic matter + Cr2 O7− 2 + H +
catalyst
→ Cr
3+
+ CO2 + H 2 O
heat
Coliforms = see Total Coliforms and Faecal Coliforms.
Combiners / stream combiners = In our static model, most mixers in the superstructure do not physically exist
but indicate a choice for a water source or a sequence of different sources used over time. Assumed is that
all incoming streams are ideally mixed to form one homogeneous output stream. No reaction, storage or
energy transformation takes place inside mixers.
Constraints = Several technologies have constraints on the influent, for instance composting requires an input
with a certain solid-liquid-ratio, it the ratio in the input is to high or too low composting cannot be applied
Sustainable Wastewater Treatment
Glossary: Acronyms & symbols and terminology
and the decision variable will switch to no treatment for this unit sending a flag with the output to indicate
that the model overruled the input by the optimisation. In other cases, for instance wetlands, or tricking
filters the model will use another type of wetland of trickling filter. The solar dry toilet for instance has a
constraint in the average temperature and switches to a dry composting toilet if the temperature is too low.
All constraints can be found on the technological data sheets and in the data spreadsheet. The restrictions
used to classify the outgoing streams are in a way also constraints for the optimisation thought in the thesis
and model these are called restrictions (see concluding table in Chapter 4 and data spreadsheet in Appendix
1).
Conventional treatment = In our western society large scale centralised activated sludge plants are the usual
wastewater treatment system –also referred to as end-of-pipe.
Denitrification = Denitrification, the removal of nitrogen by conversion to nitrogen gas, is accomplished under
anoxic conditions. For this conversion a carbon source is required, and as nitrified wastewater is often low
in carbonaceous matter a external carbon source or a recycle stream has to be provided. Due to the required
anoxic conditions denitrification is accomplished in another reactor or another zone in an oxidation ditch
than nitrification. The process of nitrification and denitrification is meant to meet the effluent requirements,
nitrogen is removed biological and emitted as N2 gas and therefore not available for reuse.
Domestic water systems = we defined domestic water system as water supply, excluding large scale drinking
water production, but including rainwater collection and disinfection of rainwater or household water,
water use, wastewater transport, wastewater treatment, sludge treatment, and rainwater infiltration. We
excluded: surface and groundwater management, agriculture, and industry. Although we included in the
indication of reuse among others potential fertiliser, soil conditioner, and irrigation.
Faecal Coliforms (FC) = are those coliforms that can ferment lactose at 44.5°C. The presence of faecal
coliforms indicates the presence of faecal material from warm-blooded animals (incl. Homo Sapiens). Also
referred to as thermotolerant coliforms (Bitton 1999, p.122). See also Total Coliforms.
Greywater = greywater is not always precisely defined in literature, mostly is referred to domestic wastewater
excluding wastewater from the toilet (= blackwater), however in some cases also water from the kitchen is
excluded as this also carries a higher load of polutants.
Heterotrophs (organotrops) = This is the most common nutritional group among micro-organisms and includes
mostly bacteria, fungi, and protozoa. These micro-organisms that obtain their energy via oxidation of
organic compounds (Bitton 1999, p.51). Heterptrops use organic carbon for the formation of cell tissue.
Household water = Water of a lesser quality than drinking water safe for household water use except drinking.
Often the European Bathing water standard is used as quality guideline for this type of water. Household
water sometimes called B-water, drinking water is than A-water, and recycled effluent E-water.
Hydrolysis = Complex waste components including fats, proteins, polysaccharides, and nucleic acids are
liquefied under the influence of enzymes into their component subunits (fatty acids, amino acids,
monosaccharides, and purines and pyrimidines). This is accomplished by a heterogeneous group of
facultative and anaerobic bacteria.
Kitchen = a water use category used by the model of the domestic water system. Based on average Dutch water
use figures about 7.8 l/cap/d (NIPO 1999) is in the kitchen used for food preparation, drinking, and dish
washing. Other water use categories are: Outdoor, Personal hygiene, Toilet, and Washing.
Log 5.7 pathogens reduction = 99.9998 % reduction, namely (1-10-5.7)*100=99.9998.
Mesophyl = Middle temperature range around 35-38 °C.
Nitrification = Nitrification is the oxidation of ammonia to nitrite by micro-organisms and further to nitrate. This
process requires oxygen (approximately 4.3 mg O2/mg NH3-N) and consumes alkalinity (approximately
8.64 mg HCO3- per NH3-N)(Tchobanoglous 1991, p.413). In this process nitrogen is not removed but the
oxygen demand is lowered. Nitrification can be achieved in the same reactor as carbonaceous organic
matter degradation, although low soluble carbonaceous BOD loadings are necessary to promote growth of
nitrifying cultures.
Orthophosphates = the complexes PO4-3, HPO4-2, H2PO4-, H3PO4, HPO4-2 that are directly available for
biological metabolisms (Crites 1999, p.52).
Outdoor = a water use category used by the model of the domestic water system. Based on average Dutch water
use figures about 8.2 l/cap/d (NIPO 1999) is used for other purposes than dish washing, drinking &
cocking, personal hygiene, and washing clothes. This is the rest category and includes for instance
cleaning, watering the garden, and car washing. Other water use categories used by the model are: Kitchen,
Personal hygiene, Toilet, and Washing.
Sustainable Wastewater Treatment
Glossary: Acronyms & symbols and terminology
Personal hygiene = a water use category used by the model of the domestic water system. Based on average
Dutch water use figures about 51.1 l/cap/d (NIPO 1999) is used for showering, bathing, brushing teeth and
washing hands. Other water use categories used by the model are: Kitchen, Outdoor, Toilet, and Washing.
Polyphosphate = molecules with 2 or more phosphorus atoms, oxygen atoms, and sometimes hydrogen atoms,
combined in a complex molecule; P2O7-4, HP2O7-3, P3O10-5, H3P3O10-2, etc. Polyphosphates undergo
hydrolysis in aqueous solutions and revert to orthophosphates, however, this is a slow process (Crites 1999,
p.52).
Processing unit = In processing unit blocks, separation and/or material transformation take place. These blocks
are therefore defined by state information (temperature, components, etc.) and by information on the nature
of the block (chemical reactions, physical separation, etc.). The processing units contain simple static black
box models of unit operations in parallel, including a blank option as well. One of the unit operations or the
blank option is selected and use in the specific domestic water system of which the sustainability indicators
are to be calculated.
Psychrophyl = Lower temperature range <25°C.
Rainwater = literary only water in the form of rain but often includes all water from of precipitation (see also
storm water and runoff).
Restrictions = The model uses restrictions to classify the outgoing streams see the concluding table in Chapter 4
and the data spreadsheet in Appendix 1.
Runoff = rainwater that has been collected on a hardened surface and therefore may have been polluted as dust,
leaves, bird faeces, etc, that were on this surface are now in the water.
Splitters / Stream splitters = Alike the mixers, splitters indicate different choices, in this case realising different
directions of flows. A splitter can exist as a water supply pipe splitting into pipes with different directions.
For instance directing rainwater to toilet flushing and the washing machine (two options of splitter r2).
However, it can also mean that some of the water evaporates during cooking while the rest maybe poured
into the sink (splitter k2). We assume that stream splitters are ideally mixed and that no storages or
reactions occur. As such outgoing streams of stream splitters can only differ in quantity not in composition.
The composition of the outgoing streams is therefore the same as the composition of the incoming stream.
Storm water = the rainwater of heavy rains, ‘superfluous’ in the sense that it is not taken up by the surface and in
many cases the sewers will not be able to deal with all this water directly.
Superstructure = a structure incorporating a large number of alternative systems, showing the possible mass
streams connecting different units. Two types of units are defined, namely mixers & stream splitters
(triangles) and processing units (squares). Inserting specific models for the processing units generates a
specific system. Selecting a subset of streams and operational units and leaving out the rest of the stream
and units, yields the representation of a particular domestic water system.
Thermophyl = Higher temperature range around 40 - 60°C.
Theoretical Oxygen Demand (ThOD) = this measure for organic matter in wastewater is determined from the
chemical formulas as theoretical oxygen demand (mol O2/mol organic compound) (Metcalf 1991, p 70-83).
Toilet = a water use category used by the model of the domestic water system, based on average Dutch water use
figures about 39 l/cap/d (NIPO 1999) is used fortoilet flushing. However, in the model the water use for
toilet flushing depends on the chosen toilet. The flush water demand of the toilet ranges between 0 and 9
litres/flush and the estimated use is 6 times/cap/d. Other water use categories used by the model are:
Kitchen, Outdoor, Personal hygiene, and Washing.
Total Coliforms (TC) = rod shaped bacteria, aerobic and facultative anaerobic, gram-negative, non-sporeforming, that ferment lactose with gas production within 48 hours at 35°C (Bitton 1999, p.122). Discharges
in high numbers in human and animal faeces, but not all of them originate from faeces.
Total Organic Carbon (TOC) = is another measure for the organic matter in wastewater. Especially applicable to
small concentrations of organic matter and is measured with an infrared analyser as the CO2 equivalent
after oxidation at high temperatures. Typically, the BOD/TOC varies between 1.0 and 1.6 (Metcalf 1991, p
70-83).
Total Solids (TS) = Residue that remains after evaporation of the water at 103 to 105 °C.
Total Suspended Solids (TSS) = Solids that settle at the bottom of an Imhoff cone in a 60 minute period.
Urban water system = we chosen to use this term for system larger than the domestic water system as it may
include surface water management and industrial water use as well.
Washing = a water use category used by the model of the domestic water system. Based on average Dutch water
use figures about 27.6 l/cap/d (NIPO 1999) is used for washing clothes. Other water use categories used by
the model are: Kitchen, Outdoor, Personal hygiene, and Toilet.
Yellowwater = urine either diluted with flush water or not.
Sustainable Wastewater Treatment
Appendix 1:Spreadssheet Data2003.xls
Appendix 1: Spreadsheet “Data2003”
All data used in de model are stored in the spreadsheet “DataDWS.xls”. The fist sheet in the
spreadsheet is a title page with a table of contents (included in this appendix). The data is
presented in 8 sheets, namely:
(1) General data & incoming streams: in this sheet some general information on the
population and climate as well more specific information on the amount and
composition of incoming streams is presented. This data is described in Chapter 4 of
this thesis and included on the following pages.
(2) Household: here all data quantifying processing units in the household is presented,
including the different types of toilets, the disinfection unit and the rainwater systems.
In principal this data is the same as the data on the technology data sheets (see
Appendix 3), however, one can change the data in the spreadsheet and use the adapted
data as input for the calculations.
(3) Transport: this sheet gives the quantification of the different sewerage systems,
transport by truck and different infiltration systems (see also the technological data
sheets in Appendix 3).
(4) Treatment: all data needed to quantify the treatment technologies is summarised in
this sheet (see also the technological data sheets in Appendix 3).
(5) Classification of outgoing streams: the classification of outgoing streams as
described in Chapter 4 is quantified in this sheet (see also the concluding table at the
end of Chapter 4).
(6) Normalisation and Weighting; the data from normalisation table as calculated with
the optimisation (see Chapter 3) are presented in this sheet. Furthermore, one can
insert weighting factors in this sheet.
(7) Macro: in this the macro sending or importing data to or from Matlab is written in
this sheet and can be adapted here. The macro uses the file “excllink.xla” as an
interface between Matlab and Excel (see http://www.mathworks.com).
Note: In this research another spreadsheet is used and sometimes referred to. Namely a spreadsheet
summarising all interesting water and wastewater data from literature gathered during the research
(approximately 200 reports/articles). This spreadsheet is based on a spreadsheet I got from Martin
Oldenburg and Ralf Otterpohl and is used to calculate average values of for instance the composition of
certain streams such as urine, faeces, greywater, etc.
A
1
2
3
4
5
6
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8
9
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83
84
85
86
B
C
D
1 General Data & Incomming streams
E
F
G
1.1 Scale:
People
Households
Population
Maximum no people
matrix
(people/househo
2
(no. of househo 100000
(people*househ 200000
(people/building
50
1.2 Climate
Rainfall
Roof area
Correction factor
Temperature
(mm/(m2*d))
(m2/household)
(0…1)
(°C)
2.4
50.00
0.85
15
RinAmount
1.3 Energy & waste
Price electricity
Price electricity
Price wastewater
Price wastewater
(Euro/kWh)
(Euro/household
(Euro/m3)
(Euro/household
0.0751
48.12
0
121
EnergyCosts, ve(NRE 2001)
1.4 Water supply:
I
J
K
L
M
N
O
Purple is used for the constraints and white zeros are to fill the matrix and are not used in the model.
People
Households
Population
maxpphh
(g/l)
1
998
(g/l)
(g/l)
(g/l)
(g/l)
(g/l)
(g/l)
(g/l)
(no./l)
(g/l)
1
2
3
4
5
6
7
8
9
1.5
0
0
0.00132
0.00027
0.002
0.0062
0
996.5
(KNMI)
Roof
RinCorrect
Temperature
(average outdoor temperature)
(NRE 2001)
WWCosts, vecto(Eindhoven Municipality 2001)
(Eindhoven Municipality 2001)
Drinking water Household water
Din
Density:
Composition:
TS
TSS
BOD
TN
TP
Cu
Zn
FC
H2O
H
Note: Figures in blue calculated, in red rough estimate, in black from literature.
Letter codes in grey are the variable name in Matlab-Simulink.
Rain water
Hin
Rin
3
0.005
0
0.00264
0.00054
0.002
0.0124
20000
-6
6
0.01
0
0.00048
0.00001
0.000041
0.002125
10000
-6
DensityH2O
DinComp, HinComp, RinComp, vectors (9,1)
(Rin=2*Hin)
(Hin=2*Din)
(Hin=2*Din)
(Din Standard 98/83/EEC and Hin proposed by Versteegh 1997 and Rin Boschloo 1999 p20)
(Hin=2* Din)
(Hin EEG bathing water norm (standard 76/160/EEC), for Rin 50% this norm )
(Density - TS)
Note: for scenario's with reuse Cu and Zn can be limiting therefore one may want to assume lower values in water assuming plastic piping and roofing materials.
Values measured by our dinking water company ate the tap are much lower: Cu < 1*10^3 and Zn <1*10^2 (WOB 1998)
Price
(Euro/m3)
Price fixed rate
(Euro/hous
Energy water supply (kWh/m3)
Space water suppply(m2/m3)
Qualitative indicators:
Acceptance
Adaptability
Expertise
Quality of occupied space
Institutional requirements
Maintanance
Participation
Reliability
Robustness
Stimulating sustainable behavi
1.5 Water use:
1
2
1
1
1.17
32
0.22
482
0.88
0
0.13
482
DinCosts, HinCosts, vectors(2,1 (WOB 2001)
(WOB 2001)
DinEnergy, HinEnergy
(VROM 1992)
DinSpace, HinSpace
(VROM 1992)
Note: 1 indicates a potential advantage, 0 neutral with respect to this indicator, and -1 a potential disadvantage.
1
2
3
4
5
6
7
8
9
10
1
1
0
0
0
0
0
0
1
0
1
1
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
0
0
1
DinIndicate, HinIndicate, RinIndicate, vectors (10,1)
(NIPO 1995)
(l/use)
Toilet
Shower
Bath
Wash basin
Dishes by hand
Dishwasher
Hand wash
Washmachine
Drinking & cooking
Others
6.72
56.89
108.69
3.97
7.66
25.21
42
96.88
2
8.2
(uses/(cap*d)) (fraction of cap)
5.8
0.68
0.18
1.08
0.64
0.21
0.05
0.28
1
1
(l/(cap/d))
1
0.99
0.46
0.98
1
0.17
1
0.94
1
1
38.98
38.30
9.00
4.20
4.90
0.90
2.10
25.50
2.00
8.20
total:
Total
Water use hygiene:
Water use kitchen:
Water use washing:
Water use outdoor:
(l/d)
(l/d)
(l/d)
(l/d)
1
1
1
1
Note: in the model toilet data as given below are used,
these figures are only to complete this table.
134.08 (l/(cap*d))
total*Population
######## (l/d)
Evaporated, consumed, infiltrated
(l/cap/d)
(fraction)
51.50
7.80
27.60
8.20
0.2
0.2
0.1
1
PH1Amount, SplitPH2
Note: for water use in toilets see Household worksheet
K1Amount, SplitK2
W1Amount, SplitW2
O1Amount, SplitO3
1.6 Water conservation (for conservation in toilets or by using rainwater see Household worksheet)
ConsShower, vector(3,1)
Note: based on assumption that normal shower uses 10 l/min (56.89 l/use), conservation shower 6 l/min (34.13 l/min), 0.6
Water conservation by sho(l/cap/d)
1
15
Energy conservation by sh(kWh/cap/y 2
Note: conserves 10 m3/household/y and 45 m3 gas/household/y according to SEV/NOVEM 1999, Vadumecum Water ISB
0
Extra costs shower
(Euro/hous 3
0
Water conservation by wa (l/capita/d) 1
ConsWashM, vector(3,1)
Note: based on assumption that normal washing machine uses 96.88 l/use, conserving one 39 l/use, 0.28 uses/d, penetra
15
Energy conservation by w (kWh/capita 2
Note: conserves 10 m3/household/y and 0.5 kWh/household/y (approximately 250 washes) according to SEV/NOVEM 19
0.2
Extra costs washing mach (Euro/hous 3
0
A
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
B
C
D
E
F
G
(l/(cap*d))
(g/l)
(g/cap/d)
1
1
1
Faeces
Fin
0.19
1090
207
Urine
Uin
1.19
1016
1209
Biowaste
Bin
0.36
610
220
(g/l)
(g/l)
(g/l)
(g/l)
(g/l)
(g/l)
(g/l)
(no./l)
(g/l)
1
2
3
4
5
6
7
8
9
257.53
206
69.14
10.68
2.83
0.01237
0.056702
1.00E+09
832.47
45.48
0.6
5.06
9.8
0.89
0.000055
0.000258
0.00E+00
970.52
214.3
85.72
53.575
9.01
1.87
0.02627
0.07758
0.00E+00
395.7
(g/(cap*d))
(g/(cap*d))
(g/(cap*d))
(g/(cap*d))
(g/(cap*d))
(g/(cap*d))
(g/(cap*d))
(no./(cap*d
(g/(cap*d))
1
2
3
4
5
6
7
8
9
toilet
C1
38.71
7.77
7.56
0.23
0.33
0
0
1.39E+05
0
kitchen
C2
46.68
9.37
9.12
0.27
0.4
0.0126
0.0006
1.68E+05
0
hygiene
C3
45.56
9.14
8.9
0.27
0.39
0
0
4.58E+03
0
H
I
J
K
L
M
N
1.7 Wastes:
Amount:
Density:
Load
Composition:
TS
TSS
BOD
TN
TP
Cu
Zn
FC
H2O
FinAmount, UinAmount, BinAmount
FinDensity, UinDensity, BinDensity
FinLoad, UinLoad, BinLoad
FinComp, UinComp, BinComp, vectors (9,1)
1.8 Contaminants:
TS
TSS
BOD
TN
TP
Cu
Zn
FC
H2O
wash
C4
91.12
18.29
17.8
0.53
0.77
0
0
5.05E+03
0
outdoor
C5
4.41
0.89
0.86
0.03
0.04
0
0
0.00E+00
0
roof
C6
0
2.09
0.01
0.14
0.02
0.0158
0.1315
0.00E+00
0
total
Cin
226.48
47.55
44.25
1.47
1.95
0.0284
0.1321
3.17E+05
0
CLoad, matrix (9,6), CinLoad, vector(9,1)
Note: for scenario's with reuse Cu and Zn can be limiting therefore one may want to assume that these are low in added contaminants.
O
A
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
B
2 Household
C
D
E
F
G
H
I
J
K
L
M
N
O
P
Note: Figures in blue calculated, in red rough estimate, in black from literature.
Letter codes in grey are the variable name in Matlab-Simulink.
Purple is used for the constraints and white zeros are to fill the matrix and are not used in the model.
2.1 Toilets
No of toilets per households
No. flushes faeces per d(no/(cap*d))
No. flushesurine per day(no/(cap*d))
1
1
5
Toilet type:
Old toilet
(Note: for references see Technological data sheet sin Appendix 3 of the PhD thesis)
nToilet
nFlushF
nFlushU
New toilet
Flush InterrupLow Flush
Vacuum toil Separation toilet
Compost toilet
Solar dry
urine separation possible urine separation possible urine sepation possible
(TDS 4)
(TDS 4)
(TDS 4)
(TDS 4)
(TDS 9)
Toilet (TDS 8) Urinal (TDS 8) ow flush (TDS 6 Dry (TDS 5)
(TDS 7)
Constraint:
Tmin (°C)
1
0
0
0
0
0
0
0
0
0
20
tConstraint, maxtrix(3,10)
Scale:
Processing unit:
(pe)
(name)
1
5
t
5
t
5
t
5
t
5
t
5
5
t
5
t
10
t
tScale, matrix(1,10)
t
5
t
Removal rates:
TS
TTS
BOD
TN
TP
Cu
Zn
FC
H2O
matri
1
2
3
4
5
6
7
8
9
1
2
3
4
7
8
9
10
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
5
0
0
0
0
0
0
0
0
0
6
(fraction)
(fraction)
(fraction)
(fraction)
(fraction)
(fraction)
(fraction)
(fraction)
(fraction)
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.18
0.15
0.9
0.4
0
0
0
0.95
0.6
0.18
0.1
0.8
0.4
0
0
0
0.9
0.6
0
0
0
0
0
0
0
0.95
0.9
tRemoval, matrix(9,10)
Costs investment
Costs O & M
Lifetime
(Euro/system)
(Euro/system/y)
(y)
1
2
1
100
0
10
100
0
10
100
0
10
280
0
10
800
0
10
820
0
10
200
0
10
3500
21
10
3500
0
10
600
0
10
tCosts, matrix(2,10)
(g/d)
(g/d)
(g/d)
1
2
3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
(kWh/y/system)
(fraction)
(m2)
(l/flush)
(l/flush)
1
1
1
1
2
0
0
1
9
9
0
0
1
6
6
0
0
1
9
3
0
0
1
1.5
0.8
100
0
2
1
1
0
0.8
1
4
0.2
0
0.4
2
9
2
CO2
CH4
NH3
Energy
Urine separation
Space
Flush water faeces
Flush water urine
(1.25*NH3)
(1.25*NH3)
(0.486*Ntot,in)
(0.486*Ntot,in)
0
0
0
1400
0.8
6
4
0.8
50
0.8
6
0
0
0
0.8
4
0
0
-1
1
1
1
1
1
1
1
1
1
-1
0
0
0
-1
-1
1
1
0
1
-1
0
0
0
-1
0
1
1
0
1
3.67*(TSin*0.18-CH43.67*(TSin*0.18-CH4
tLifetime, matrix(1,10)
tEmissions, matrix(3,10)
tEnergy, matrix(1,10)
tUrineSep, matrix(1,10)
tSpace, matrix(1,10)
tFlush, matrix(2,10)
Note: 1 indicates a potential advantage, 0 neutral with respect to this indicator, and -1 a potential disadvantage.
Acceptance
Adaptability
Expertise
Flexibility
Institutional requirements
Maintanance
Participation
Reliability
Robustness
Stimulating sustainable behaviour
1
2
3
4
5
6
7
8
9
10
1
0
0
0
0
0
0
1
0
0
1
0
0
0
0
0
1
1
0
1
1
1
0
0
0
0
1
1
0
1
1
0
0
0
0
0
1
1
0
1
1
0
-1
0
0
0
0
0
0
1
-1
0
0
0
0
0
1
1
0
1
-1
0
0
0
0
0
1
1
0
1
tIndicate(10,10)
A
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
B
C
D
E
F
G
H
2.2 Disinfection unit
I
J
K
L
M
Scale:
Processing unit:
therefore it does not matter if cDis is 1,2 or 3, all characteristics are set zero.
RO-unit (TDS Sun-UV (TDUV-unit (TD No System
duConstraint, matrix(3,4)
TSmax (g/l)
1
2
0
2
0
TSSmax (mg/l)
2
0
30
0
0
Temperature (ºC)
0
20
0
0
(l/system/d)
duScale, matrix(1,4)
1
265
10
10
0
du
du
du
du
Removal rates:
TS
TTS
BOD
TN
TP
Cu
Zn
FC
H2O
(fraction)
(fraction)
(fraction)
(fraction)
(fraction)
(fraction)
(fraction)
(fraction)
(fraction)
1
2
3
4
5
6
7
8
9
0.96
0.96
0.96
0.9
0.99
0.98
0.98
0.99
0.1
0
0
0
0
0
0
0
0.995
0
0
0
0
0
0
0
0
0.999
0
0
0
0
0
0
0
0
0
0
duRemoval, matrix(9,4)
Investment costs
O & M costs
Life time
Energy
Space
(Euro/l)
(Euro/system/yea
(y)
(kWh/m3)
(m2/system)
1
2
1
1
1
2.6
0.3
2
0.15
0.25
0
0
10
0
1
120
50
10
0.008
0
0
0
0
0
0
duCosts, matrix(2,4)
Constraint:
N
duLifetime, matrix(1,4)
duEnergy, matrix(1,4)
duSpace, matrix(1,4)
Note: 1 indicates a potential advantage, 0 neutral with respect to this indicator, and -1 a potential disadvantage.
Acceptance
Adaptability
Expertise
Quality of occupied space
Institutional requirements
Maintanance
Participation
Reliability
Robustness
Stimulating sustainable behaviour
2.3 Rainwater systems
System:
Constraint:
O
note: if no rainwater or householdwater is usedin the kitechn or in personal hygiene cDisa=4 overruling the choice
1
2
3
4
5
6
7
8
9
10
1
0
0
0
-1
0
1
-1
1
1
0
0
0
0
-1
0
1
0
-1
0
1
0
0
0
-1
0
1
1
0
1
0
0
0
0
0
0
0
0
0
0
duIndicate, matrix(10,4)
Scale:
Processing unit:
vanced (TDS No system
Basic (TDS 10
rwsConstraint, matrix(2,3)
Regular rainfall
1
0
0
0
Roof (m2/househ 2
50
0
0
(per household)
rwsScale, matrix(1,3)
1
1
1
0
rws
rws
rws
Removal rates:
TS
TTS
BOD
TN
TP
Cu
Zn
FC
H2O
(fraction)
(fraction)
(fraction)
(fraction)
(fraction)
(fraction)
(fraction)
(fraction)
(fraction)
1
2
3
4
5
6
7
8
9
0.5
0
0.5
0
0
0
0
0
0
0.1
0
0.1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
rwsRemoval, matrix(9,3)
Investment costs
O & M costs
Life time
Energy
Space
Rainwater utilisation
(Euro/system)
(Euro/system/y)
(y)
(kWh/m3)
(m2/system)
(fraction)
1
2
1
1
1
1
250
0
10
0
2
0.5
2600
36
10
7.5
3
1
0
0
0
0
0
0
rwsCosts, matrix(2,3)
note: if no rainwater is used cRWSa=3 overruling the choice
therefore it does not matter if cRWS is 1 or 2, all characteristics are set zero.
rwsLifetime, matrix(1,3)
rwsSpace, matrix(1,3)
rwsUtilisation, matrix(1,3)
Note: 1 indicates a potential advantage, 0 neutral with respect to this indicator, and -1 a potential disadvantage.
Acceptance
Adaptability
Expertise
Quality of occupied space
Institutional requirements
Maintanance
Participation
Reliability
Robustness
Stimulating sustainable behaviour
1
2
3
4
5
6
7
8
9
10
0
0
0
0
-1
0
1
1
-1
1
1
0
0
0
0
0
1
0
1
1
0
0
0
0
0
0
0
0
0
0
rwsIndicate(10,3)
P
A
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
B
C
D
3 Transport & Infiltration
E
F
G
H
I
J
K
L
M
N
O
Note: Figures in blue calculated, in red rough estimate, in black from literature.
Letter codes in grey are the variable name in Matlab-Simulink.
Purple is used for the constraints and white zeros are to fill the matrix and are not used in the model.
>>>>>>
3.1 Sewerage (TDS 12)
(3.5 transport distances see below)
Removal rates:
TS
(fraction)
TTS
(fraction)
BOD
(fraction)
TN
(fraction)
TP
(fraction)
Cu
(fraction)
Zn
(fraction)
FC
(fraction)
H2O
(fraction)
Investment (Euro/m)
Investment (Euro/syste
O & M Cos (Euro/m/y)
O & M Cos (Euro/kg)
O & M Cos (Euro/syste
Life time (y)
Energy
(kWh/m3)
Energy slud(kWh/kg/km
Energy
(kWh/syste
Space
(m2/m3)
Space
(m2/kg)
Space
(m2/system
CSO
(fraction)
Emissions: CO2 (g/km)
3.2 Truck ( 3.3 Infiltration (TDS 14)
3.4 No transport
Mixed
Separated
Vacuum
0
0
0
0.5
0
bwt
0
0
0
0
0
bwt, gwt, yw
0
0
0
0
0
bwt
matrix
1
2
3
4
5
6
7
8
9
1
0
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
0
0
3
0
0
0
0
0
0
0
0
0
4
0
0
0
0
0
0
0
0
0
5
0.9
0.9
0.95
0.2
0.8
0.8
0.9
0.99
1
6
0.9
0.9
0.95
0.2
0.8
0.8
0.9
0.99
1
7
0.9
0.9
0.95
0.2
0.8
0.8
0.9
0.99
1
8
0
0
0
0
0
0
0
0
0
1
2
3
4
5
1
1
2
3
1
2
3
1
1
183
0
3.9
0
0
50
0.4
0
0
0
0
0
0.03
0
188
0
2.3
0
0
50
0.35
0
0
0
0
0
0
0
85
0
3.9
0
0
50
17
0
0
0
0
0
0
0
0
0
0
0.06
0
0
0
0.0008
0
0
0
0
0
0
0
136
0
0
0
10
0
0
0
0
0
0.5
0
0
0
42
0
0
0
20
0
0
0
0
0
17
0
0
0
42
0
0
0
20
0
0
0
0
0
17
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Constraint: BODmax (mg/l)
TSSmax (mg/l)
FCmax (no/100ml)
Treshhold CSO (% of r
(/household)
Scale:
Processing unit:
Soak away wales & ditchenfiltration fields
tpConstraint, matrix(4,8)
0
20
20
20
0
0
30
30
30
0
0
2000
2000
2000
0
Note if more than the given
0
0
0
0
0
tpScale, matrix(1,8), Note: a
0
1
1
1
0
bst, ywt w4, gw2, rwbw4, gw2, rw1
bw4, gw2, rw1bwt, bst, gwt, ywt
tpRemoval, matrix(9,8)
tpCosts, matrix(5,8)
tpLifetime, matrix(1,8)
tpEnergy, matrix (3,8)
tpSpace, matrix(3,8)
tpCSO, matrix(1,8)
tpEmissions, matrix(1,8)
Emisisons for transport are still missing daNote: 1 indicates a potential advantage, 0 neutral with respect to this indicator, and -1 a potential disadvantage.
Acceptance
Adaptability
Expertise
Quality of occupied sp
Institutional requireme
Maintanance
Participation
Reliability
Robustness
Stimulating sustainab
3.5 Transport distances
systems <= 100 pe
systems <= 1,000 pe
systems <= 10,000 pe
systems > 10,000 pe
Sludge transport
Urine transport
1
2
3
4
5
6
7
8
9
10
1
2
3
4
1
1
1
0
0
0
0
0
0
0
-1
0
1
0
0
0
0
0
0
0
0
0
1
0
-1
0
0
0
0
-1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
1
0
0
0
0
0
1
0
0
0
1
0
0
0
0
0
1
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
tpIndicate, matrix(10,8)
Distance (m/pe)
0
tpDistance(1,4), Note for black and greywater transport the highest scale of treatment systems chosen determines t
25
50
150
Distance (km)
25
tpDistanceBS
50
tpDistanceYW
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
A
1
1
2
3
CH4 production
(g/pe/d)
Emissions: >>>>>
H
I
J
L
70
0
0
0
10
75
0
0.2
0
0
0
500
2
0.75
0.75
0.90
0.70
0.40
0.50
0.40
0.90
0.35
0
0
0
0
0
50
bw2
400
0
0
0
40
0
0
0
20
40
0
0.1
0
0
0.80
0.80
0.95
0.80
0.60
0.50
0.50
0.95
0.20
3
0
0
0
0
0
400
bw2
200
0
0
0
30
0
0
0
20
40
0
0.1
0
0
4
0.80
0.80
0.95
0.80
0.60
0.50
0.50
0.95
0.20
0
0
0
0
0
1000
bw2
20
0
0
0
7
0
0
0
40
15
0
0.02
0
0
5
0.95
0.95
0.95
0.80
0.80
0.60
0.60
0.99
0.10
0
0
0
0
0
100000
bw2
0
1
0
-1
-1
-1
0
0
0
-1
0
0
1
0
-1
-1
-1
0
0
0
0
0
0
1
0
-1
-1
0
0
0
0
0
0
0
1
0
-1
-1
0
0
0
0
0
0
0
1
0
-1
-1
1
0
0
0
0
0
M
O
P
Q
R
S
T
(Note: for references see Technological data sheet sin Appendix 3 of the PhD thesis)
N
U
V
460
0
0
0
25
0
0
0
10
-15
0
0.4
0
0
6
0.40
0.60
0.60
0.10
0.20
0.40
0.40
0.95
0.20
400
0
0
0
25
0
0
0
20
-20
0
0.1
0
0
7
0.45
0.70
0.70
0.20
0.40
0.80
0.80
1.00
0.10
280
0
0
0
60
0
0
0
20
-36
0
0.04
0
0
8
0.55
0.97
0.95
0.40
0.50
0.95
0.95
1.00
0.10
6
1
0
-1
-1
-1
0
1
-1
-1
1
8
1
0
-1
-1
0
0
1
-1
0
1
0
1
0
0
-1
0
0
0
1
0
0
0
1
0
0
1
-1
0
0
1
0
1
200
0
0
0
17
0
0
0
10
6
0
2
0
0
10
0.75
0.75
0.70
0.40
0.30
0.80
0.80
0.98
0.10
CO2 (g/d) 3.67*(TSin*0.18-CH4)
CH4 (g/d) (1.25*NH3)
NH3 (g/d) (0.486*Ntot,in)
13
1
0
-1
-1
1
0
0
-1
0
0
0
0.052
0
0
0
0.003
0
0
20
0.02
0
0
0
0.01
9
0.18
0.15
0.95
0.04
0.00
0.00
0.00
1.00
0.80
0
1
0
0
1
-1
0
0
1
0
1
850
0
0
0
17
0
0
0
10
6
0
6
0
0
11
0.75
0.75
0.70
0.40
0.30
0.80
0.80
0.98
0.10
0
1
0
0
1
0
0
0
1
1
1
225
0
0
0
5
0
0
0
20
1.5
0
1
0
0
12
0.90
0.90
0.90
0.50
0.40
0.90
0.90
0.99
0.10
0
1
0
0
1
0
0
0
1
1
1
370
0
0
0
5
0
0
0
20
1.5
0
3
0
0
13
0.90
0.90
0.90
0.50
0.40
0.90
0.90
0.99
0.10
0
1
0
0
1
0
0
0
1
1
0
1.5
0
0
0
0.00004
0
0
0
20
0.15
0
0.5
0
0
14
0.99
0.99
0.98
0.80
0.60
0.95
0.99
1.00
0.10
0
1
0
0
1
0
0
0
1
1
0
14
0
0
0
0.00004
0
0
0
20
0.15
0
1
0
0
15
0.99
0.99
0.98
0.80
0.60
0.95
0.99
1.00
0.10
0
1
0
0
0
-1
0
0
0
0
0
1245
0
0
0
70
0
0
0
10
110
0
0.4
0
0
16
0.90
0.90
0.90
0.80
0.37
0.50
0.50
0.90
0.00
0
1
0
0
0
-1
0
0
0
0
0
420
0
0
0
23
0
0
0
10
51
0
0.16
0
0
17
0.95
0.95
0.98
0.90
0.70
0.60
0.60
0.95
0.00
0
1
1
0
-1
0
0
0
-1
1
0
135
0
0
0
0.3
0
0
0
4.5
0
0.15
0.02
0
0
18
0.95
0.90
0.83
0.30
0.22
0.50
0.50
1.00
0.15
4.2 Anaerobic Digestion (TDS 16) 4.3 Composting
4.4 Constructed
(TDS 17) wetland (TDS 18)
4.5 Fixed bed (TDS 19)4.6 Membrane (TDS 20)
Greywater Blackwater Greywater Blackwater Greywater Blackwater
0
0
0
100
0
0
0
0
1000
1000
0
0
0
0
0
0
0
360
0
360
0
360
0
360
360
360
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.4
0
0
0
0
0
0
0
0
0
0
0
0
0.8
0
0
0
0
0
0
0
0
0
5
400
50000
0
5
5
200
200
1000
1000
5
50
0
ost
bw2, bs1 bw2, bs1
bs1
gw2
bw3
gw2
bw3
gw2
bw3
bw2
bw2 ost,bw1, bw4
Purple is used for the constraints and white zeros are to fill the matrix and are not used in the model.
Letter codes in grey are the variable name in Matlab-Simulink.
K
Note: 1 indicates a potential advantage, 0 neutral with respect to this indicator, and -1 a potential disadvantage.
Acceptance
1
Adaptability
2
Expertise
3
Quality of occupied space 4
Institutional requirements 5
Maintanance
6
Participation
7
Reliability
8
Robustness
9
Stimulating sustainable behaviour
10
1
2
3
4
5
6
7
8
1
1
2
1
2
3
800
0
0
0
100
0
0
0
10
75
0
0.4
0
0
1
0.70
0.70
0.80
0.60
0.30
0.30
0.30
0.80
0.30
Investment (Euro/pe)
costs
(Euro/kg)
(Euro/m3)
(Euro/(m3/d))
O & M costs(Euro/pe/y)
(Euro/kg/y)
(Euro/m3/y)
(Euro/(m3/d)/y)
Life time (y)
Energy
(kWh/pe/y)
(kWh/m3/y)
Space
(m2/pe)
(m2/system)
(m2/kg)
G
4.1 Activated sludge (TDS 15)
Removal rates:
TS
(fraction)
TSS
(fraction)
BOD
(fraction)
TN
(fraction)
TP
(fraction)
Cu
(fraction)
Zn
(fraction)
FC
(fraction)
H2O
(fraction)
Matrix:
1
2
3
4
5
6
7
8
9
F
Note: Figures in blue calculated, in red rough estimate, in black from literature.
E
0
0
0
0
0
5
ost
D
Constraint: (min pe)
TS max (mg/l)
TSS max (mg/l)
TS/(TS +H2O) min
TS/(TS+H2O) max
Scale:
(pe)
Processing unit:
>>>>>>
B
C
4 Wastewater treatment
W
X
Y
Z
AA
AB
AC
AD
AE
AF
AG
AH
1
Note: Figures in blue calculated, in red rough estimate, in black from literature.
Letter codes in grey are the variable name in Matlab-Simulink.
2
Purple is used for the constraints and white zeros are to fill the matrix and are not used in the model.
3
4
5 4.7 MBR (TDS 21)
4.8 RBC (TDS 22)
4.9 Sedimentation (TDS 23)
4. 10 Septic4.11
tankTrickling
(TDS 24)filter (TDS 25)
Pre-Sedimentation
Post-Sedimentation
Precipitation
Low rate High rate Low rate
6
Mixed Greywater
0
0
0
0
0
5
5
5
0
0
0
0
7
360
360
0
0
0
0
0
0
0
0
360
0
8
0
0
0
0
0
0
0
0
0
0
0
0
9
0
0
0
0
0
0
0
0
0
0
0
0
10
0
0
0
0
0
0
0
0
0
0
0
0
11
0
0
5
50
400
0
0
0
5
5
5
50
12
gw1
bw2,gw1 bw2, gw1 bw2, gw1
bw1
bw4
bw1
ost
bw3, gw2 bw3, gw2 bw3, gw2
13 ost, bw2
14
19
20
21
22
23
24
25
26
27
28
29
30
15
0.99
0.99
0.75
0.80
0.90
0.57
0.57
0.75
0.70
0.45
0.75
0.45
16
1.00
1.00
0.80
0.85
0.95
0.55
0.55
0.77
0.70
0.50
0.80
0.50
17
0.98
0.99
0.90
0.93
0.96
0.30
0.30
0.44
0.60
0.55
0.85
0.55
18
0.90
0.85
0.40
0.50
0.60
0.10
0.10
0.25
0.35
0.30
0.50
0.30
19
0.70
0.72
0.20
0.20
0.24
0.10
0.10
0.78
0.30
0.30
0.50
0.30
20
0.50
0.50
0.50
0.50
0.50
0.40
0.40
0.80
0.50
0.30
0.50
0.30
21
0.50
0.50
0.50
0.50
0.50
0.30
0.30
0.50
0.50
0.30
0.50
0.30
22
1.00
1.00
0.80
0.85
0.90
0.60
0.60
0.80
1.00
0.90
0.95
0.90
23
0.15
0.05
0.00
0.00
0.00
0.10
0.10
0.10
0.00
0.05
0.05
0.05
24
25
26
0
0
0
0
0
0.9
0.45
3.15
600
1100
1100
425
27
0
0
0
0
0
0
0
0
0
0
0
0
28
140
110
1000
415
300
0
0
0
0
0
0
0
29
0
0
0
0
0
0
0
0
0
0
0
0
30
0
0
0
0
0
0.006
0.003
0.03
30
4.6
4.6
0.72
31
0
0
0
0
0
0
0
0
0
0
0
0
32
0.6
0.5
18.2
4.5
0.03
0
0
0
0
0
0
0
33
0
0
0
0
0
0
0
0
0
0
0
0
34
2
3
10
10
20
20
20
20
10
10
10
10
35
0
0
70
26.5
10
1.9
1
1.9
0
75
75
40
36
1.5
0.2
0
0
0
0
0
0
0
0
0
0
37
0.04
0.002
0.7
0.3
0.1
0.017
0.007
0.014
1.2
0.8
0.8
0.08
38
0
0
0
0
0
0
0
0
0
0
0
0
39
0
0
0
0
0
0
0
0
0
0
0
0
40
41
42
1
1
1
1
1
1
1
1
1
0
0
0
43
1
1
0
0
0
-1
-1
-1
0
0
0
0
44
0
0
0
0
0
0
0
0
0
0
0
0
45
-1
-1
-1
-1
-1
0
0
0
0
0
0
0
46
0
0
0
0
0
0
0
0
-1
0
0
0
47
0
0
0
0
0
0
0
0
0
1
1
1
48
0
0
0
0
0
0
0
0
0
0
0
0
49
-1
-1
0
0
0
1
1
1
1
1
1
1
50
1
1
0
0
0
1
1
1
0
0
0
0
51
0
0
0
0
0
0
0
0
0
0
0
0
52
53
0
0
0
0
0
0
0
0
0
0
0
0
54
55
56
57
58
59
32
0.45
0.50
0.55
0.40
0.40
0.40
0.40
0.90
0.05
300
0
0
0
0.03
0
0
0
20
10
0
0.01
0
0
0
0
0
0
0
1
0
1
0
0
0
31
0.75
0.80
0.85
0.50
0.50
0.50
0.50
0.95
0.05
425
0
0
0
0.72
0
0
0
10
40
0
0.08
0
0
0
0
0
0
0
1
0
1
0
0
0
400
bw3, gw2
Low rate
0
0
0
0
0
High rate
0
360
0
0
0
50
bw3, gw2
AJ
AI
0
0
0
0
0
0
1
0
1
0
0
300
0
0
0
0.03
0
0
0
20
10
0
0.01
0
0
33
0.75
0.80
0.85
0.60
0.60
0.60
0.60
0.95
0.05
bw3, gw2
High rate
0
360
0
0
0
400
AK
0
0
0
0
0
0
1
0
1
0
0
150
0
0
0
0.07
0
0
0
30
3.75
0
0.005
0
0
34
0.45
0.50
0.55
0.40
0.40
0.40
0.40
0.90
0.05
1000
bw3, gw2
Low rate
0
0
0
0
0
AL
0
0
0
0
0
0
1
0
1
0
0
150
0
0
0
0.07
0
0
0
30
3.75
0
0.005
0
0
35
0.75
0.80
0.85
0.60
0.60
0.60
0.60
0.95
0.05
bw3, gw2
High rate
0
360
0
0
0
1000
AM
AO
AP
AQ
AR
0
1
0
0
0
0
0
0
-1
0
0
0
0
0
0.14
0
0
0
0.004
10
0
1600
0
5
0
36
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
1.00
0
bw4, gw2
100
0
30
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
100
0
0
0
0
0
20
0
0
0.36
0
0
37
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.90
0.00
0
yw1
0
0
0
0
0
wwtConstraint, matrix(5,38)
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
38
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
wwtEmissions, vector(1;3)
wwtGas, matrix(1,38)
wwtIndicate, matrix(10,38)
wwtSpace, matrix(3,38)
wwtEnergy, matrix(2,38)
wwtLifetime, matrix(1,38)
wwtCosts, matrix(8,38)
wwtRemoval, matrix(9,38)
0
wwtScale, matrix(1,38), Note a zero here means any scale
all except yw1
0
0
0
0
0
4.12 UV (TDS
4.13
26)
Yellowater
4.14 storage
No treatment
(TDS (blanco
27)
option)
AN
A
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
B
C
D
5 Classification of outgoing streams
E
F
G
H
I
J
K
L
M
Note: Figures in blue calculated, in red rough estimate, in black from literature.
Letter codes in grey are the variable name in Matlab-Simulink.
Purple is used for the constraints and white zeros are to fill the matrix and are not used in the model.
Restrictions:
>>>>>>
TS
TSS
BOD
N-total
P-total
Cu
Zn
FC
5.1 Domestic re5.2 Irrigation
(mg/l)
(mg/l)
(no/l)
matrix:
1
2
3
4
5
6
7
8
5.3 Fertiliser valu5.4 Infiltration
(mg/kg P)
(mg/l)
5.5 Discharge
(mg/l)
5.6 Compost
(mg/kg TS)
5.7 Landfill
(mg/kg TS)
1
2
3
4
5
6
7
0
15
0
0
0
2
0
20,000
0
20
10
0
0
0.2
2
10000
0
0
0
0
15
70
500
0
0
0.5
0
0
0.4
0.015
0.065
0
0
35
25
10
1
0
0
0
500
0
0
0
0
110
350
0
100
0
0
0
0
110
350
0
(Note: for references see concluding table in Chapter 4 of the PhD thesis)
Restrict, matrix(8,7)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
C
D
E
F
G
H
I
1
2
3
4
5
6
7
8
9
10
188
9000
(Euro/(cap*d))
(Euro/cap/yr)
(kg/cap/y)
(kg/cap/y)
(kg/cap/y)
(kg/cap/y)
(kg/cap/y)
(kg/cap/y)
(kg/cap/y)
(kg/cap/y)
(kg/cap/y)
38
0.2
(m2/cap)
(kg/cap/y)
53
(kWh/(cap*y))
47
NormCostsL(2
NormRoWL(1
NormSpaceL(
NormEnergyL
max:
1.70E+05
1
min:
0.65
0.43
8.83E+01
1.49E+05
1.49E+05
1.49E+05
1.49E+05
1.49E+05
5.03E+03
0.01
0
0
0
0
0
0
0
0
0
0
1.20E+02
0
max:
min:
max:
1900000
5.00E+04
-12
7.00E+03
149
149
19
149
0
0
0
24
min:
max:
min:
K
0
0
0
0
0
0
1
2
3
4
5
6
7
8
9
10
disadvantage, neutral, or
advatage. Two sums are
calculated, sum of disadvantages and sums of advantages.
These sums are vectors,
and normalised by dividing
by the number of choices
made, obtaining a vector
with values between 0 and 1.
0
0
0
0
0
0
0
0
0
0
Advantage
L
M
O
P
Q
and (2) based on minimum and maximum values calcualted on input data,
set of best solution for good scores on other indicators.
factors keeps the optimisation transparant afterward you can search the
will not influence the objective. Using a limited zet of non-zero weighting
(-1*objective). If you chose to set weighting factors to zero this indicator
on the objective. The optimisation minimises the inverse of the objective
Note that you have to use + and _ to have positive and negative influence
Procedure weighting:
through optimisation (see also Chapter 3 of the thesis).
therefore we used (3) the ga-solver to deteremine the normalisation factors
however for some indicator this is quiet difficult, for instance for costs,
as this scores good on indicators as costs, energy and space!
0
0
0
0
0
0
0
0
0
0
Disadvantage
WeightIndicate(10,2)
WeightCosts(2,1)
R
There are 3 possibilities for normalisation, (1) based on literature
Procedure normalisation:
N
Note: always weight waste largly negative - else your wastewater may not treated
WeightRoW(10,1)
WeightSpace(1,1)
WeightEnergy(1,1)
WeightWater(3,1)
6.2.5 Qualitative indicators
NormCosts(2,1)
6.2.4 Costs
6.2.3 Reuse or waste
NormRoW(10,
1
0
2
50
3
100
4
0
5
0
6
0
7
0
8
-100
9
0
10
0
6.2.2 Energy & Space
0
NormSpace(1,1)
0
NormEnergy(1,1)
NormWater(3,1), last column
6.2.1 Water
6.2 Weighting factors
J
quantified -1, 0, or -1, for
169880
1
range:
0.65
0.43
8.83E+01
1.49E+05
1.49E+05
1.49E+05
1.49E+05
1.49E+05
5.03E+03
0.01
range:
1900012.2
43000
range:
149
149
19
125
range:
Data based on model inputs:
Note: these indicators are
(toilet 30% of
(m3/cap/y)
(m3/cap/y)
NormWaterL(4
(toilet + washin
47
Literature data:
(m3/cap/y)
(m3/cap/y)
Acceptance
Adaptability
Expertise
Quality of occupied space
Institutional requirements
Maintanance
Participation
Reliability
Robustness
Stimulating sustainable behaviour
6.1.5 Qualitative indicators
6.1.4 Costs
Costs water services
Low income
6.1.3 Reuse or waste
1 Landfill
2 Soil Conditioner
3 Fertiliser
4 Irrigation
5 Domestic reuse
6 Discharge
7 Inflitration
8 Waste
9 CSO
10 Gas
6.1.2 Energy & Space
Energy
Space
6.1.1 Water
Drinking water
Household water
Rainwater
Total water demand
6.1 Normalisation
Note:different solution spaces have different normalisation factors these can be determined through optimisation (for more info see chapter 3 in the PhD-thesis).
In optimisation using dws2003.mdl the factors as shown below are used.
Note: in the model dwssmall_manual.mdl normalisation and weighting factors have to be inserted manual.
A
B
6. Normalisation & Weighting
S
Sustainable Wastewater Treatment
Appendix 2: Explaining m-files for optimisation
Appendix 2: Explaining the mm-files used for optimisation
This appendix guides you through the optimisation routines that are used to compute optimal domestic water
systems. The m-files I wrote for the optimisation and the ga-solver gaminI are available as freeware and can be
downloaded from the TU/e internet site (www.tue.nl/bib under Publications TU/e, Full-text Dissertations TU/e,
van der Vleuten-Balkema). For the other 2 solvers, mip-Solve and gclSolve look at www.tomlab.biz or
www.ima.mdh.se/tom and/or contact K. Holström at [email protected]. For more information see also
www.kiboko.info or www.chemeng.ntnu.no/~preisig or contact me through [email protected]. For each of the mfiles a brief description is given in the table below. But first a list of the most important names of variables and
solvers:
f
ftot
ga
gcl
mask
maskinfo
is the optimal point found.
contains all optima found during the optimisation routine.
refers to the genetic algorithm.
refers to the global solver – gclSolve.
When using as smaller set of decision variables by creating a mat-file through running
in2003.m new lower and upper bounds are set and masks are used to translate the x back to the
original x used by the model (see also masktxt and maskinfo – note only used by m-files with
small in their name). The mask is a matrix (1 row, 61 columns), when the mask is not active
its value is 0. For instance if the solution space is made smaller, for instance only allowing the
toilets 5,6 and 9 to be chosen, the value of mask(1,8)=7. The row number of the mask
indicates that cToilet=x(7)=5 is moved, namely to the place indicated by the value of the
mask, thus in this case 7. If so the system boundaries are set to xL3=7 and xU3=9 and both
Toilet 5 and 6 are moved respectively to 7 and 8. The solver used makes it choices in this
solution space and before calculating the objective value the x is translated back to for
instance 5 if the solver chose 7.
is a matrix (1 row, 61 columns) that contains text explaining the masks, containing the
following text:
'1 pRWS(1) moves to pRWS(2)'
'2 pDis(1) moves to pDis(2) or to pDis(3)'
'3 pDis(2) moves to pDis(3)'
'4 pToilet(1) moves to the right (2..9)'
'5 pToilet(2) moves to the right (3..9)'
'6 pToilet(3) moves to the right (4..9)'
'7 pToilet(4) moves to the right (5..9)'
'8 pToilet(5) moves to the right (6..9)'
'9 pToilet(6) moves to the right (7..9)'
'10 pToilet(7) moves to the right (8..9)'
'11 pToilet(8) moves to the right (9)'
'12 empty'
'13 pOST(1) moves to the right (2..5)'
'14 pOST(2) moves to the right (3..5)'
'15 pOST(3) moves to the right (4..5)'
'16 pOST(4) moves to the right (5)'
'17 pBWT(1) moves to the right (2..3)'
'18 pBWT(2) moves to the right (3)'
'19 pYWT(1) moves to the right (2)'
'20 pBW1(1) moves to the right (2..3)'
'21 pBW1(2) moves to the right (3)'
'22 pBW2(1) moves to the right (2..12)'
'23 pBW2(2) moves to the right (3..12)'
'24 pBW2(3) moves to the right (4..12)'
'25 pBW2(4) moves to the right (5..12)'
'26 pBW2(5) moves to the right (6..12)'
'27 pBW2(6) moves to the right (7..12)'
'28 pBW2(7) moves to the right (8..12)'
'29 pBW2(8) moves to the right (9..12)'
'30 pBW2(9) moves to the right (10..12)'
'31 pBW2(10) moves to the right (11..12)'
Sustainable Wastewater Treatment
masktxt
mip
vlag
vlagbest
Appendix 2: Explaining m-files for optimisation
'32 pBW2(11) moves to the right (12..13)'
'33 pBW2(12) moves to the right (13)'
'34 pBW3(1) moves to the right (2..7)'
'35 pBW3(2) moves to the right (3..7)'
'36 pBW3(3) moves to the right (4..7)'
'37 pBW3(4) moves to the right (5..7)'
'38 pBW3(5) moves to the right (6..7)'
'39 pBW3(6) moves to the right (7)'
'40 pBW4(1) moves to the right (2..6)'
'41 pBW4(2) moves to the right (3..6)'
'42 pBW4(3) moves to the right (4..6)'
'43 pBW4(4) moves to the right (5..6)'
'44 pBW4(5) moves to the right (6)'
'45 pGW1(1) moves to the right (2..8)'
'46 pGW1(2) moves to the right (3..8)'
'47 pGW1(3) moves to the right (4..8)'
'48 pGW1(4) moves to the right (5..8)'
'49 pGW1(5) moves to the right (6..8)'
'50 pGW1(6) moves to the right (7..8)'
'51 pGW1(7) moves to the right (8)'
'52 pGW2(1) moves to the right (2..7)'
'53 pGW2(2) moves to the right (3..7)'
'54 pGW2(3) moves to the right (4..7)'
'55 pGW2(4) moves to the right (5..7)'
'56 pGW2(5) moves to the right (6..7)'
'57 pGW2(6) moves to the right (7)'
'58 pBS1(1) moves to the right (2..3)'
'59 pBS1(2) moves to the right (3)'
'60 pRW1(1) moves to the right (2..3)'
'61 pRW1(2) moves to the right (3)'
is a matrix (1 row, 61 columns) containing strings of text that indicate which masks are active
(else the string of that row is empty) and for what variable the mask is active.
refers to the mixed integer solver.
A value of 1 indicates the model overruled choice made by the user or the optimisation
routine. This happens for instance when the influent is unsuitable for the treatment technology
(see the constraints in the spreadsheet and technology data files). Another reason for a model
to overrule a choice is when a choice is made to use a technology while the incoming stream is
zero. The optimisation routines as well as dws2003out.m have a section that corrects the x for
these vlags. There are 12 flags but vlag has usually 13 rows as the objective (=f) is save in
vlag(1,1) (see also vlagtxt.m). For the choice of the rainwater system (cRWS) and the
disinfection systems (cDis) and treatment in GW1 it is more complicated and not visible in
vlags. cRWS is overruled if cRWS is not 3 thus a rainwater system is chosen but there is no
rainwater available or the choice is made not to use rainwater. cDis is overruled if cDis not 4
thus a disinfection system us chosen but there is no water that needs disinfection, or when
cDis is 2 (solar disinfection) but the climate is not warm enough (temperture <
duconstraint(3,2)), in this case disinfection is done with a UV-lamp. For BW3 there is no flag,
but when the toilet chosen is a dry toilet the blackwater line is empty, this is detected by the
flags for BW1, BW2, and BW4. For BW3 I inserted the rule that if cToilet>7 then BW3=8,
meaning that when a dry toilet is chosen no treatment takes place in BW3. In GW1 the choice
is overruled if the inflow=0 this happens when SplitGW1=1. Furthermore, I found that the
model may direct grey- and yellowwater to the blackwater line even though in case of a dry
toilet this blackwater line receives no blackwater. Therefore, I inserted a rule that if cToilet>7
SplitGW1=0, meaning that in case of a dry toilet the greywater cannot be directed to the
blackwater line. For these 5 cases – cRWS, cDis, cBW3, cGW1 and cSplitGW1 – there is no
flag but the correction is made using a flag in combination with x or the variables Temperature
and duConstraint. So the correction is made but the visibility is not provided in the form of a
flag. It would be more efficient to set some of these rules in the A-matrix of the gclSolver but
since I made these rules one at the time in process of obtaining results I never came to make
this into an efficient procedure.
the vector of flags for x0 that is the uncorrected x-vector that is the input for the model to
Sustainable Wastewater Treatment
vlagtot
vlagtxt
x
xtot
x0
xtot0
Appendix 2: Explaining m-files for optimisation
obtain the optimum f.
all flags coming with ftot and xtot.
is a string vector to explain the flags containing the text:
'1 objective'
'2 vRW (no rainwater available)'
'3 vHK (no disinfection unit)'
'4 vToilet (low temp., solar dry --> compost toilet)'
'5 vOST (wwtconstraint(2,18) or (2,19))'
'6 vBWT (scale > minpphh no tranport not allowed)'
'7 vGWT (scale > minpphh no tranport not allowed)'
'8 BW1 (wwtconstraint(2,18) or (1,24) or (1,26))'
'9 vBW2 (wwtconstraint(2,16),(2,17),(2,18), or (2,19))_'
'10 vBW4 (tp or wwtconstraints)'
'11 vGW2(tp or wwtconstraints)'
'12 vRW1 (tpconstraints)'
'13 vBS1 (wwwtconstraint(1,24))'
x-vector resulting in the optimum f – corrected for flags – there are 24 x only mipsolve uses 25
but this extra x is set to1 and does not go through the model.
matrix containing all x-vectors that resulted in f as saved in ftot – corrected for flags.
x0 is the x-vector uncorrected for flags that results in f when running through the model.
is the matrix of all x0 vectors that result in the ftot.
The direction structure used is:
Running optimisation can be done by calling the solvers ga2003small, gcl2003small or mip2003small. The datafile they use is data2003.mat, the solution space is solspace, and the model is dws2003.mdl. Solvers settings can
be changed in the solvers’ m-files. These solvers run with the 24 decision variables and all technology options
available (see Figure 20 page 105 or the spreadsheet data2003.xls). The idea is to optimise over a smaller
solution space that can be created with in2003.m, when including all options one can use the full solution space
however the calculation time may then be outrages. When running the global solver (gclSolve.m) there is a risk
of running out of memory during optimisation. Therefore, gcl-solver has the possibility to restart with input of
data from a previous run saved in gcl_restart by gclsmallr_restart.m when saved by gcl2003smallrestart.m.
However, I did not use this option as I adapted gcl_small to avoid memory problems by saving the data of every
7000 evaluations in a new results file. To make a full run of the optimisations by mip2003small.m,
Sustainable Wastewater Treatment
Appendix 2: Explaining m-files for optimisation
ga2003small.m, and gcl2003small.m, after each other run optot.m. Mat-files that contain the results of the
optimisation are saved automatically at the end but also during optimisation. The name of this mat-file is the
name of the solver followed by _best or _interim depending on the time of saving. To generate a complete
overview of the output run dws2003out.m, for sorting and plotting of the results use SortResults.m. More details
are given in the table below.
Table explaining all m-files, mdl-files, mat-files, and xls-files used:
file-name:
CalcTime.m
directory:
\AAWater\2003
function of file and settings to be changed:
This m-file estimates the calculation time for a smaller set of
decision variables.
ChangeRR
\AAWater\2003
With this m-file you can change the removal rates of all
technologies.
ChangeWeight.m
\AAWater\2003
With this m-file you can change the weights and make a new
ordering of the results to find the new optimum. Input needed
is the mat-file you want to use – this must be one of the
interim.mat files saved during optimisation or the
changeweight_best.mat file that is automatically saved by a
previous run of this m-file. Note: only when using an
gclsmall_interim.mat file saved at the end of the run makes
ensures you that you have all possible systems – thus the new
optimum is the optimum of the solution space you defined. If
using mipsmall_interim or gasmall_interim you can say
something about the sensitivity of the optimum for weighting
factors but you may find an other optimum that when making
a new run with the solver and the new weighting factors.
data2003.mat
data2003_nutrients.mat
data2003_nutrients_RR1
05
data2003_water.mat
\AAWater\2003
\x-matfiles
Date-file containing all data needed for the model calculation
except the decision variables. This file is generated by
transferring the date from the Excel spreadsheet
(data2003.xls) to Matlab. Change the filename of your data
file into this name such that you do not have to make changes
in the different m-files of the solvers.
I used
data2003_nutrients (with weighting factors aiming at fertiliser
and soil conditioner production), data2003_nutrients_RR105
(with removal rates * 1.05) and data2003_water (with
different weighting factors set to promote water reuse and
lowered copper and zinc in the rainwater and drinking water
assuming plastic pipes to allow domestic reuse).
data2003.xls
\AAWater\2003
This is a data file in form of a Excel spread sheet that gives an
overview of all input data used by the model. The spreadsheet
contains a macro to send the data to Matlab if the link is
established by the add-in excllink.xla.
dws2003.mdl
\AAWater\2003
This is the model of the domestic water system in Simulink,
which is called by the optimisation routines to calculate the
objective (=f).
dws2003in.mat
This data-file is automatically save by in2003.m and defines
the solution space with selected choices and technologies
contains the variables: xL3 xU3 mask masktxt pHW pRWa
pRWb pCONS pRWS pDis pToilet pOST pBWT pYWT
pGWT pBST pBW1 pBW2 pBW3 pBW4 pGW1 pGW2 pBS1
pRW1 pSplitGW1 pSplitR pSplitR7 pSplitR8 pSplitYW2
dws2003in_refresh.mat
This data-file is automatically save by in2003refresh.m it is an
Sustainable Wastewater Treatment
Appendix 2: Explaining m-files for optimisation
update dws2003in.mat with the same variables.
dws2003_manual.mdl
\AAWater\2003
This is a manual version of the model similar to dws2003.mdl
but in this file the decision variables and the normalisation and
weighting factors can be set by hand
dws2003out.m
\AAWater\2003
This m-file gives a 2-page overview of the output of the
optimisation. It shows the x-vector with explanation added,
the objective, the 5 best solutions found and from the best the
sustainability indicators for the different processing units, the
normalisation and weighting factors, the mass balance, and the
solution space used. Running dws2003out includes running a
simulation with the model, thus to see all details open the
model (dws2003.mdl) prior to running dws2003out, one can
place additional displays in the model to see streams of
variables.
filedefaultfields.m
\AAWater\2003
\ga
\AAWater\2003
\ga
Defines settings for the solver gaminI.m
ga_CWeightsF.m
\AAWater\2003
\ga
This m-file the ga-solver several times each time with
different weighting factors (same solspace and dataset).
gadafaultoptions.m
\AAWater\2003
\ga
\AAWater\2003
\ga
Defines settings for the solver gaminI.m
\AAWater\2003
\ga
Solver Genetic Algorithm adapted to handle integer problems
see appendix 6
GA2003small.m
GAF2003small.m
gaminI.m
gaminI_interim.mat
Defines the optimisation problem – the following settings can
be changed in this file, the values shown are what I used for
my research and [default]:
optu.GenerationSize=20; % [20] must be even integer
optu.CrossoverRate=-1; % [-1] If -1, use Booker's VCO, must
be in (0,1).
optu.MutationRate=0.2; % [0.02] If -1 adaptive mutation rate,
must be in (0,1).
optu.DisplayIterations=1; % [0] Display status once every
DisplayIterations
optu.MaximumIterations=2000; % [20000]
optu.Epsilon=1; %[1e-4] Smallest gain worth recognizing.
optu.StopIterations=20; % [2000] Min. number of gains <
Epsilon before stop.
optu.ReplaceBest=10; % [50] re-insert best into population
after ? generations
optu.VectorizedFunction='off’'; % [0] Logical indicator set
to 1 if the function can simultaneously evaluate many
parameter vectors.
optu.ReproductionSelection='tournament'; % [ roulette (gives
error AB2003) | {tournament} | elitist ];
This file calls the solver gaminI.m and loads the solutionspace
(solspace.m) and the dataset (data2003.m). Results are saved
in gasmall_best.mat and gasmall_interim.mat and by the
solver in gaminI_interim.mat.
Defines function of x that must be minimised, which is the
model dws2003.mdl. In this m-file, the masks are used to
translate the x-value chosen by the solver back to the original
x-value used by the model (see also mask and in2003.m).
Intermediate
results
of
a
run
with
ga2003small.m,
Sustainable Wastewater Treatment
Appendix 2: Explaining m-files for optimisation
automatically saved by gaminI.m. The variables saved are:
beta, iter, funstr , and options.
ga_Normalisation.m
\AAWater\2003
\ga
gasmall_interim.mat
This file runs the ga-solver for all sustainability indicators
setting all weighting factors to zero expect the one of the
sustainability indicator chosen to determine the highest or
lowest values this indicator can have given the solution space
and dataset defined. These values are used as normalisation
factors and saves in NewNorm.mat and in a ready to use data
set for instance Data2003_nr1_NN.mat.
Intermediate results automatically saved by gaF2003small.m,
containing the variables: vlagtot xtot ftot suOSTtot suBW1tot
suBW2tot suBW3tot suBW4tot suGW1tot suGW2tot
suBS1tot suRW1tot CostsTot EnergyTot SpaceTot AdvTot
DisTot RoWTot WaterTot. This file is cleaned at very new
run of ga2003small.m
gcl2003small.m
\AAWater\2003
\gcl
Defines the optimisation problem for gclSolve. Setting that
can be changed here are:
% Number of evaluations (default = 200)
GLOBAL.MaxEval=25921;
% Global/local weight parameter (default =1e-4)
GLOBAL.epsilon=1e-4;
% Error tolerance (default=0.01)
GLOBAL.tolerance=10;
% Printing Level % PriLev >= 0 warnings, PriLev > 0 each
iteration info
PriLev=1;
To avoid running out of memory the data-files are split up –
see
gclF2003small.m
gclsmall_interim.mat,
and
gclsmall_teller.mat
gcl2003smallrestart.m
\AAWater\2003
\gcl
To avoid running out of memory during optimisation keep de
number of evaluations low and restart optimisation later. Note
that it is not possible to make a restart after the error message
out of memory.
gcl2003small_teller.mat
\AAWater\2003
\gcl
The number of evaluations is indicated by the variable teller
that is saved in gclsmall_teller.mat. This variable is reset to 1
at every new run of gcl2003small.m
gclF2003small.m
\AAWater\2003
\gcl
Defines the function of x that must be minimised by
gcl2003.m – this means running the simulation with
dws2003.mdl. Including translating the x-vector back to the
original x-values for the simulation and saving info for
gcl2003small. To avoid running out of memory this file saves
the result of every 7000 evaluations in another mat-file:
gclsmall_interim, gclsmall_interim_2, gclsmall_interim_3,
gclsmall_interim_4 and gclsmall_interim_5. Furthermore, the
number of evaluations stored in the variable teller is saved to
gclsmall_teller.mat.
gclF2003smallrestart.m
\AAWater\2003
\gcl
Similar to gclF2003small.m but and saving info for
gcl2003smallrestart variable saved is GLOBAL.
gclSolve.m
\AAWater\2003
\gcl
Stand-alone solver for global optimisation, see appendix 2.
gclsmall_interim.mat
Intermediate results automatically saved by gclF2003small.m,
Sustainable Wastewater Treatment
Appendix 2: Explaining m-files for optimisation
gclsmall_interim_2.mat
gclsmall_interim_3.mat
gclsmall_interim_4.mat
gclsmall_interim_5.mat
containing the variables: vlagtot xtot ftot suOSTtot CostsTot
EnergyTot SpaceTot AdvTot DisTot RoWTot WaterTot. If
large number of evaluatiomns are done one risks running out
of memory, to avoid this very 7000 evaluations a new mat-file
is initiated to store the results. These results are glued back
together by gclsmall.m, above 21000 evaluations gclsmall.m
makes to result files and gives2 outputs. The number of
evaluations is counted by gclsmall_teller.mat. These files are
cleaned at very new run of gcl2003small.m
gclsmallr_interim.mat
Intermediate
results
automatically
saved
by
gclF2003smallrestart.m, containing the variables: vlagtot xtot
ftot suOSTtot CostsTot EnergyTot SpaceTot AdvTot DisTot
RoWTot WaterTot. This file is cleaned at very new run of
gcl2003smallrestart.m
gclsmallr_restart.mat
Saved by gcl2003smallrestart.m at the end of the run to enable
restart with results from previous run – variable saved is
GLOBAL.
in2003.m
\AAWater\2003
This m-file is used to create a small set of decision variables
such that the optimisation is limited to a smaller solution
space requiring less calculation time. The result of the m-file
is a new set of lower and upper boundaries and masks to
translate the x back to the original x for the simulation. These
variables xL3, xU3 and the vector mask are automatically
saved in dws2003in.mat.
in2003refresh.m
\AAWater\2003
This file is related to in2003.m. It is used to give an overview
of the new solution space after having this created with
in2003.m or having made some changes by hand to the
definition of the solution space.
makeset.m
\AAWater\2003
This m-file makes a new solution set with given ranges of
results from the gcl-interim-mat-files. In this way one can
select all best ranges and glue them together in one results file.
MIP2003small.m
\AAWater\2003
\mip
Definig the problem for mipSolve.m, note that mipSolve only
runs under Tomlab.
Settings that can be changed here are:
maxoptim=1500; % maximal number of searches starting
with new random selected staring point
% Printing Level
% Output every iteration >2 Output each step in simplex
alg
Prob2.optParam.PriLevOpt=0;
%PriLev Print level in mipSolve PriLev=0;
% Number of iteratios
Max.Iter=500;
Note that mip2003small uses an x-vector with 25 rows but the
last x is set to 1 and only for the purpose to not leave the amatrix empty, this x plays no role in the calculation of the
objective.
MIPF2003small.m
\AAWater\2003
\mip
Defining the function of x that has to be minimised – this
means running the simulation with dws2003.mdl. Including
translating x-values back with masks as a smaller solution
space is defined in solspace.mat.
Sustainable Wastewater Treatment
mipsmall_interim.mat
Appendix 2: Explaining m-files for optimisation
Intermediate
results
automatically
saved
by
MIPF2003small.m contains the variablesvlagtot xtot ftot
suOSTtot suBW1tot suBW2tot suBW3tot suBW4tot CostsTot
EnergyTot SpaceTot AdvTot DisTot RoWTot WaterTot. This
file is cleaned at very new run of mip2003small.m
moreplots
\AAWater\2003
This m-file was used to make the plots for Chapter 6.
optot.m
\AAWater\2003
\ga_gcl_mip
This m-files runs the 3 solvers mip3002small, ga2003small,
and gcl2003small after each other. The solver setting can be
changed in this m-file. This m-file uses data2003.mat,
solspace.mat, and mipF2003small.m, gaF2003small.m and
gclF2003small.m. All results are saved in the regular ..._best
and ..._interim.mat files.
solspace.mat
solspace_ls1.mat
solspace_nr1.mat
\AAWater\2003
\x-matfiles
This mat-file contains the upper and lower bounds (xL3, xU3)
as well as the masks (mask, masktxt, maskinfo) that define a
limited solution space. This file is needed to run optimisations
with ga2003small.m, gcl2003small.m, and mip2003small.m.
I defined solspace_nr1 including technologies interesting for
nutrient recycling and solspace_ls1 including technologies
interesting for water and nutrient reuse (ls stands for life
support). Solution spaces nr2 and ls2 were not used research.
SortDiff.m
\AAWater\2003
This file sorts the results on different variables than f the
objective value, for instance on Costs, Water use, Fertiliser,
Acceptance, Toilet type, etc.
SortResults.m
\AAWater\2003
This file sorts and plots the results. One can chose to plot the f
and x-values for all data points or a range or one can plot f
versus x. The sorted results are automatically saved in
Sorted.mat.
SortQuick.m
\AAWater\2003
Sorts results of different runs of gclSolve after each other
without plotting.
Sortx.m
\AAWater|2003
Selects from results these solutions that contain a given value
of specified decision variable (is a row in the xtot-vector).
Sustainable Wastewater Treatment
Appendix 3: Technology data sheets
Appendix 3: Technology data sheets
No:
Configurations:
1
2
3
RO disinfection unit
Sunlight for disinfection
Small-scale UV-disinfection unit
4
Water flush toilet
5
6
15
Compost toilet (dry)
Compost toilet (low flush, urine
separation)
Solar dry toilet (urine separating)
Two compartments
Urine separating toilet
Toilet + urinal
Vacuum toilet
Small gravity
Rainwater system (basic)
system
> 3m3
Rainwater system (advanced)
Mixed
Sewerage system
Separated
Vacuum
Transport by truck
Soak away
Infiltration
Swales & ditches
Infiltration fields
Activated sludge
16
Anaerobic digestion
17
18
Composting
Constructed wetlands
19
20
21
Fixed bed reactor
Membrane
Membrane bioreactor
22
Rotating biological contactor
23
Sedimentation
24
25
Septic tank
Trickling filter
26
27
UV-disinfection wastewater
Yellowwater storage
7
8
9
10
11
12
13
14
37
Technology:
Scale:
min. 1/hh37
Processing
units:
ds
ds
ds, bw4,
gw2
t
<= 5pe
<= 5 pe
t
t
<=10 pe
1/hh1
t
t
1/hh1
1/hh1
t
rws
1/hh1
/km
/km
/km
/kg
1/hh1
1/hh1
1/hh1
5, 50, 400,
1,000, and
100,000 pe
5, 400, and
50,000 pe
>= 100 pe
5, 200, and >
1,000 pe
5 and 50 pe
/m3
/m3
/m3
5, 50, and 400
pe
> 5 pe
> 5 pe
5 pe
5, 50, 400, and
1,000 pe
> 100 pe
/m3
rws
bwt, gwt,
ywt
265 (l/d)
10 (l/d)
10 (l/d)
Old
New
Flush interrupter
Low flush
Mixed wastewater
Greywater
Sedimentation
Precipitation
1/hh means that we assume 1 system per household, pe is population equivalent.
bst, ywt
bw4, gw2,
rw1
ost, bw2
ost, bw2,
bs1
bs1
ost, bw3,
gw2
ost, bw2
bw1, bw4
bw2, gw1
bw2, gw1
bw1, bw4
bw1
ost
ost, bw3,
gw1
bw4, gw2
yw1
Sustainable Wastewater Treatment
Appendix 3: Technology data sheets
Technology Data Sheet 1: Small-scale reverse osmosis (RO) unit for disinfection.
Description:
Scale:
Constraints:
Processing unit
Incoming streams:
Outgoing streams:
Assumptions:
Pre-filtration and reverse osmosis for drinking water production in side the household.
Maximal 265 litres/day/system.
Influent low in solids to prevent clogging of the pre-filters (TDS max = 2 g/l, in model TS <= 2
g/l).
Disinfection unit (du).
Household water (H3) and rainwater (R2), non-toxic and reliable water sources..
Drinking water to kitchen (DU1) and personal hygiene (DU2).
(1) effectively removes bacteria and viruses, and protozoa,
(2) replacement pre-filters every 6 months, membrane and post filter every 3 years,
(3) investment approximately:500- 700 Euro/system, Maintenance: 90 Euro/y,
(4) info on: http://www.ro.co.za/reverse.htm ,
(5) info on: http://www.ext.nodak.edu/extpubs/h2oqual/watsys/ae1074w..htm ,
(6) concentrate and others wastes neglected.
Removal rate (%):
TS
TSS
BOD
Ntot-N
Ptot-P
Cu
Zn
FC
H2O
average
96
96
96
90
99
98
98
99
10
min
95
max
99
83
5
96
99
99
99
99
10
General info:
Temperature
Retention time
average
-
min
max
Costs:
- investment (euro/l)
-O&M
(euro/system/y)
- life time (y)
average
2.6
0.3
min
1.1
max
11
Emissions:
CO2
CH4
NH3
Resources:
- energy (kWh/m3)
- nutrients
- space
- water
average
0
0
0
average
0.15
0.25
-
Qualitative indicators:
- acceptance
- adaptability
- expertise
- quality of space
- institutional
requirements
- maintenance
- participation
- reliability
- robustness
- sustainable
behaviour
97
98
2
1
0
0
0
-1
0
1
-1
1
1
Remarks:
see http://www.ro.co.za/reverse.htm
93 –96 % according to (4), 83 –92%, see (5)
Estimate based on Rautenbach 2000, can be much lower
see (4)
Remarks:
Remarks:
(3) and (4) , (5) says 300-3000US$ per system
see assumption (3), (4)
Rautenbach 2000, p.240 depends on use and pressure
min
max
Remarks:
Indirect emissions neglected
min
0.1
max
0.2
Remarks:
Broens 2001, p.14 dead end, cross flow several kWh/m3
Rough estimate
see (4) – recover only 5 to 15% of influent
Remarks:
Relatively easy to operate and high tech imago.
Some expertise is required – but can be a self controlling unit.
Decentralised approach may be difficult for institutions to control and inspect.
Some maintenance, cleaning and replacing bulbs and filter.
In house water purification, and maintenance by owner.
Depends on energy and water supply as well as regular changing of filters.
Water quality good in dependent of source, however rejection rate can be high.
Does not stimulate to treat all the water therefore people maybe conservative with
drinking water, this may also be a limitation.
Sustainable Wastewater Treatment
Appendix 3: Technology data sheets
Technology Data Sheet 2: Sunlight for disinfection.
Description:
Scale:
Constraints:
Processing unit:
Incoming streams:
Outgoing streams:
Assumptions:
Direct sunlight is applied to disinfect water through heating and UV-radiation.
10 litres/system.
Influent low in transparency (TSS < 30 mg/l).
Disinfection unit (du).
Household water (H3) or rainwater (R2), or other reliable non-toxic water source.
Water to kitchen (DU1) and personal hygiene (DU2)
(7) solar radiation > 600 W/m2, exposure time > 5 h, thus temperature rises above 75°C,
(8) effectively removes bacteria and viruses, and most protozoa cyst,
(9) no or low costs either using bottles (SODIS) or solar heaters (see Jørgensen et al. 1998).
Removal rate (%):
TS
TSS
BOD
Ntot-N
Ptot-P
Cu
Zn
FC
average
0
0
0
0
0
0
0
99.5
H2O
General info:
Temperature (°C)
Retention time (h)
0
average
-
Costs:
- investment (euro/l)
average
0
- O & M (euro/l/y)
- life time (y)
Emissions:
CO2
CH4
NH3
Resources:
- energy:
- nutrients
- space
- water
Qualitative indicators:
- acceptance
min
max
99
99.999
min
max
> 75
>5
Remarks:
min
0
Max
0.008
Remarks:
(euro/l) see Jørgensen et al. 1998 Danish prototype thus high
costs.
(euro/(household*y))
(y)
min
max
Remarks:
min
max
Remarks:
Solar energy only.
0
10
average
0
0
0
average
0
1
-
0
- adaptability
- expertise
- quality of space
- institutional
requirements
- maintenance
- participation
- reliability
- robustness
0
0
0
-1
- sustainable
behaviour
1
0
1
0
-1
Remarks:
Medema et al. 1996, 2 to 5-log reduction for UVdisinfection
very limited in case of bottles, more for solar water heaters.
Probably some lost water for cleaning – this is neglected.
Remarks:
Relatively easy to operate, but needs discipline, maybe seen as too low tech, works
only if enough sunlight is available, and how safe is storage of the treated water?
Some knowledge required
Decentralised approach may be difficult for institutions to control and inspect
In house water purification by owner
Low in required commodities – sunshine has to be abundant though.
End-users has only an indication that water is safe, influent has to be of relatively
high quality.
Does not stimulate to treat large amounts of water therefore people maybe
conservative with drinking water, this may also be a limitation
Sustainable Wastewater Treatment
Appendix 3: Technology data sheets
Technology Data Sheet 3: Small-scale UV-disinfection unit.
Description:
Scale:
Constraints:
Processing unit:
Incoming streams:
Outgoing streams:
Assumptions:
Activated carbon filter followed by UV-light (system described by Serpieri et al. 2000).
10 l/system/d, therefore small scale.
Influent relatively low in solids to prevent clogging of filters (TS <= 2 g/l).
Disinfection unit (du), black and greywater treatment (bw4, gw2).
Household (H3) and/or rainwater (R2), or treated wastewater (BW6, GW3).
From the disinfection unit (du) water is supplied to the kitchen (DU1) and personal hygiene
(DU2), treated wastewater (BWout, and GWout) maybe suitable fro reuse.
(10) influent relatively clean, therefore adsorption and filtration by filter neglected,
(11) effectively removes bacteria and viruses, protozoa cyst may form a problem,
(12) light bulb last s approximately 2.5 years (5000h, 5 hr/day for 10 l/day production),
(13) uses 2 bulbs of 8 Watt, 0.008 kWh/litre, price: 50-65 Euro/bulb,
(14) carbon filter is replaced every year, investment 20US$, replacement 5US$,
(15) some waste is produced but this is neglected (carbon filter, and light bulbs).
Removal rate (%):
TS
TSS
BOD
Ntot-N
Ptot-P
Cu
Zn
FC
average
0
0
0
0
0
0
0
99.9
H2O
General info:
Temperature
Retention time
0
average
-
Costs:
- investment (euro/l)
-o&m
(euro/system/y)
- life time (y)
average
120
50
Emissions:
CO2
CH4
NH3
Resources:
- energy:
- nutrients
- space
- water
average
0
0
0
average
0.008
0
0
0
Qualitative indicators:
- acceptance
- adaptability
- expertise
- quality of space
- institutional
requirements
- maintenance
- participation
- reliability
- robustness
- sustainable
behaviour
min
max
Remarks:
see assumption 1 +2
99
99.999
min
max
Remarks:
min
max
Remarks:
see Serpieri et al. 2000, p 19.
see assumption 3, 4 and 5
min
max
Remarks:
Indirect emissions neglected
min
max
Remarks:
(kWh/litre) see assumption 2
Serpieri et al. 2000 p.18: down to 1-2 CFU/100ml,
Medema et al. 1996: 2 to 5-log reduction for UVdisinfection, however Serpieri et al. indicate that carbon
filter adds bacteria to the water.
10
1
0
0
0
-1
0
1
1
0
1
limited (see Serpieri et al 2000)
Remarks:
Relatively easy to operate.
Some expertise is required – but can be a self controlling unit.
Decentralised approach may be difficult for institutions to control and inspect.
Little maintenance, cleaning and replacing bulbs and filter.
In house water purification, and maintenance by owner.
Depends on availability of bulb and electricity, measured light intensity and stops
if turbidity is too high.
Influent must not contain too much solids, but is treated for UV disinfection.
Does not stimulate to treat all the water therefore people maybe conservative with
drinking water, this may also be a limitation
Sustainable Wastewater Treatment
Appendix 3: Technology data sheets
Technology Data Sheet 4: Water flush toilets
Description:
Scale:
Constraints:
Processing unit:
Incoming streams:
Outgoing streams:
Assumptions:
Removal rate (%):
TS
TSS
BOD
Ntot-N
Ptot-P
Cu
Zn
FC
H2O
General info:
Temperature
Retention time
Costs:
- investment
(Euro/toilet)
- operation &
maintenance
- life time (y)
Emissions:
CO2
CH4
NH3
Resources:
- energy:
- nutrients
- space
- water: flush faeces
- water: flush urine
Qualitative
indicators:
- acceptance
- adaptability
- expertise
- quality of space
- institutional
requirements
- maintenance
- participation
- reliability
- robustness
- sustainable
behaviour
Toilet using water to flush urine and faeces.
1 per household.
Requires water supply.
Toilet (t).
Faeces (Fin), urine (YW1= Uin + FW3), and flush water (FW2), some contaminants are included
in de flush water (C1) others are neglected. Waste flushed may contain: toilet paper and possibly
some wastewater from cleaning and unwanted sanitary waste such as tampons panty liners,
condoms, etc.
Mixture of incoming streams (BW1, YW2 and SCout are 0).
(1) Waste only mixed, no reactions or storage.
Old
New
0
0
0
0
0
0
0
0
0
Flush
interrupter
0
0
0
0
0
0
0
0
0
Low
flush
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
-
-
-
-
Old
New
100 (70250)
-
100 (70250)
-
Flush
interrupter
100 (70250)*
-
Low
flush
280
10
10
10
10
Old
New
-
-
Flush
interrupter
-
Low
flush
-
0
1
9 –12
9- 12
0
1
6-9
6- 9
0
1
6-9
3
0
1
1.5
0.8
Old
New
1
0
0
0
0
1
0
0
0
0
Flush
interrupter
1
1
0
0
0
Low
flush
1
0
0
0
0
0
0
1
0
0
0
0
1
0
0
0
1
1
0
1
0
1
1
0
1
Remarks:
Remarks:
Remarks:
* Replacement costs is 45 Euro, not more
expensive in purchase than normal toilet.
-
Remarks:
Note: flush water volumes up to 16
litres/flush are reported (NSFC 1999 USA),
usually 6-12 litres.Data on number of flushes
in Butler 1991, Bulter 1993, NIPO 1999, and
Wijst 1998. Low flush toilets: Mini flush
Gustavsberg 1 litre/flush, approximately 765
Euro (Swedenviro 2001), Gustavsberg
Miniflush 650 DEM, 0.8 to 1.5 litre/flush,
extra costs for Gustaveberg system: booster
110 Euro, toilet 35 Euro, installation: 70 to
200 Euro, on estimated total extra costs: 280
Euro (SEV 1999, p.26-28).
Remarks:
Flush interrupter not always used
Flush water as user requires, no more no less
End user decides on amount for flushing.
Depend on water supply.
May clog when used to flush large items.
Sustainable behaviour is stimulated and
visible.
Sustainable Wastewater Treatment
Appendix 3: Technology data sheets
Technology Data Sheet 5: Compost toilet (dry).
Description:
Scale:
Constraints:
Processing unit:
Incoming streams:
Outgoing streams:
Assumptions:
A dry toilet with a composting chamber.
1 system per household, <= 5 pe.
Needs temperate or Mediterranean climate, not too cold and not too dry, an enthusiastic user,
land available for spreading of compost.
Toilet (t).
Faeces (Fin), urine (YW1=Uin+FW3, FW3=0), and kitchen waste and bulking agents (B1) (no
flush water, FW2=0).
Compost (SCout) and gaseous emissions (neglected)(BW1=0 and YW2=0).
(16) Bulking agent freely available, addition 0.1 kg woodchips/cap/d to maintain C:N ratio
of about 25 (Table 3.3, p.37), assuming only to introduce the necessary carbon source.
(17) Compost to be used on-site (no transport, land available).
(18) Maintenance by owner.
(19) Emissions to air are assumed to be mainly H2O, CO2, and NH3 (TS mainly as CO2).
Removal rate (%):
TS
TSS
BOD
Ntot-N
Ptot-P
Cu
Zn
FC
H2O
average
18
10
80
40
0
0
0
90
60
min
13
max
24
6
73
35
86
General info:
Temperature
Retention time
average
??
-
min
max
Costs:
- investment
(Euro/toilet)
- operation &
maintenance
- life time
average
3,500
min
1,000
max
7,000
Emissions:
CO2
CH4
average
min
max
To be calculated
To be calculated
NH3
NOx
Resources:
- energy:
- nutrients
- space (m2)
To be calculated
0
average
min
max
0
0
6
Remarks:
21% Haug 1993 p46, 13% Lasardi 2000 p220, 24% Trubetskaya
2001 p52
Rough estimate.
See below, 28%, >0.5 and << 15%, 13%, 54%, >20%, 6 to 73%.
Estimate,product: 0 to 35/g (MC 2000), max 200/g (NSFC 1998)
86% Haug 1993 p46, 35% Lasardi 2000 p220, 52% Trubetskaya
2001 p52
Remarks:
Cold slows process down, heat may dry the compost too much.
Depending on temperature and mixture, approximate 1 a 2 years.
0
Remarks:
see below, 1,000-1,500, 1,200-6,000, 1,250-3,000, 3,000-7,000,
3, 000
minimal
10
(y)
- water
0
Qualitative indicators:
- acceptance
-1
- adaptability
- expertise
- quality of space
- institutional req.
- maintenance
- participation
- reliability
- robustness
- sustainable behav.
0
0
0
-1
-1
1
1
0
1
Remarks:
CO2=(TSin*0.18-CH4)/ 12*(12+2*16), see assumption 4
CH4=(NH3/(14+3))/3*4*(12+4), see assumption 4 and data CM
2000
NH3=Ntot-N,in*0.4/14*(14+3), see assumption 4, CM 2000
Neglected
Remarks:
this type is unheated – needs temperate / Mediterranean climate
Recycled
Quite large due to long retention time, although volume
reduction
Water conservation – no flush water
Remarks:
Low-tech imago, there have been problems with flies and smell. In some cultures,
the visibility of excreta is unacceptable. Only acceptable for enthusiastic user.
Expertise is required to control the process (too wet, C/N-ratio, too cold, worms)
Requires more space than normal toilet, but can be in basement.
Decentralised approach may be difficult for institutions to control and inspect
Adding bulking agents, and removing and use of compost
Changing of toilet habits, and control and maintenance by owner
No dependency on water or energy supply.
Sensitive to temperature and moisture content, mixing and bulking agent addition.
No flush water used and compost is recycled probably on-site
Sustainable Wastewater Treatment
Appendix 3: Technology data sheets
Estimating the removal of Water, Carbon and Nitrogen:
From the mass balance in Haug 1993 (see Figure 3.2) we find removal of water of 86% and a reduction of total
solids of 21%. Based on the measurements of Lasardi 2000 (see Table 3.3) we can calculate the total solids
removal based on the assumption that the P is inert (P-total unchanged), this gives us a s decrease of solids of
13% while an increase of 23% is measured (%ww). If we assume this difference due to water evaporation, the
water removal is 35%. Similar calculations can be done based on the measurements of Trubetskaya 2001 (see
also Table 3.3), giving a TS removal of 24% and a water removal of about 52%. In the vent gas carbon dioxide
was measured to be 0.2%, humidity was 95%, hydrogen sulphide was 0.5 ppm, ammonia 3 ppm, and methane 4
ppm (not detectable were sulphur dioxide, carbon monoxide, and methyl mercaptan) (CM 2000). From these
measurements, it becomes plausible that the main loss in TS is due to carbon losses. In the ORWARE model it is
assumed that 50 to 70% of the carbon is transformed into CO2, to trace amounts of methane (0.35% of CO2) and
to humus (Björklund et al. 2000, p.47).
Assuming, as done in the ORWARE model, that the major part of nitrogen in compost is bound to humus, while
the remainder is mineralised as NO3- and NH4+. Gaseous losses are estimated according the following
equation; N loss = 0.55903 − 0.01108 * (C / N ) in which Nloss is the lost fraction of the incoming nitrogen, C
is the incoming carbon, and N is the incoming nitrogen. The nitrogen loss is mainly NH3 (96% while 2% is N2O,
and 2% is N2) (Kärrman 2000, p.88, and Sonesson 1998, p.28). According to the formula, the nitrogen loss at a
C:N ratio of 25 is about 28%. According to Gotaas (in Jenkins 1994, on p.29) this would be much lower, namely
14.8% for a C:N ratio of 22 and 0.5% at a C:N ratio of 30. According to the measurements of Lasardi the Total
N as % of the dry weight is the same in the sludge straw mixture and final compost, however, as TS the TS
contents increases, the nitrogen loss can be estimated at 13%. Similar for the Trubetskaya measurements an
estimate of 54% N-total reduction can be estimated. Bengtsson et al (1996, p.44) estimates a nitrogen loss for
kitchen waste composting of 20%, however, in kitchen waste the nitrogen contents is significantly lower than in
mixed faeces, urine and biowaste. Based on the Clivus Multrum measurements of 2.7 to 9.4 g N-total per litre
compost (CM 2000) and an estimated incoming N-total of 10 g/l, the N-total reduction rate is about 6 to 73%
(Note: 10g/l = (1.19*9.8+0.2*10.7+0.36*9)/(1.19+0.2+0.36), see spreadsheet).
Pathogen reduction:
Clivus Multrum is NSF approved, the measured faecal coliform counts for this system range between undetected
and 35 per gram (NSF’s standard is < 200 /g).
Costs:
Mountain Lion Trading (Oct. 2000) 1,000 to 1,500 US$, EPA (2000) factsheet 1,200 to 6,000 US$, Fittschen
etal. (1997) 11,250 to 26,470 SEK (approx. 1,250 – 3,000 Euro), Lange (1997, p.115) 6,000 to 13,000 DEM
(approx. 3,000 to 7,000 Euro), at www.environment-agency.gov.uk listed that the Clivus unheated composting
toilet costs 1800UKpounds (approx.3000 Euro) and that operating costs are minimal.
Internet-sites of manufactures of composting toilets:
Clivus Multrum: http://www.clivusmultrum.com email: [email protected], composting toilet system
for use with dry or low flush toilets.
Cotuit Dry Toilets: http://www.cape.com/cdt/
DryLoo: http://www.dryloo.com/index.htm
Nature Loo: http://www.nature-loo.com.au/ email: [email protected], liquid drains through the compost
and excess is collected/infiltrated, one chamber when full replaced by empty compartment leaving the full
one stored to cure the compost, foot pump for spaying moisture on the compost, prices range from 595 to
2190 Australian Dollars excluding freight.
Sun-Mar: http://www.sun- mar.com email: [email protected] or [email protected], self-contained
toilets (price 949 to 1160 US$ June 1997) and central composting units (1100 to 2000 US$), both with
rotating drum system, excess liquid is evaporated. Average electricity use for fan and heating 150 Watt for
the Excel model).
Info and prices are also available trough Internet shops such as http://www.jademountain.com or
http://www.mtlion.com.
Sustainable Wastewater Treatment
Appendix 3: Technology data sheets
Technology Data Sheet 6: Compost toilet (low flush – urine separation).
Description:
Scale:
Constraints:
Processing unit:
Incoming streams:
Outgoing streams:
Assumptions:
Removal rate (%):
TS
Low flush toilet with a composting chamber and urine separation.
1 system per household, <= 5 pe.
Requires a limited supply of water and energy, an enthusiastic user, land available for spreading of
compost.
Toilet (t).
Faeces (Fin), urine (YW1=Uin+FW3), flush water (FW2), and kitchen and garden waste (B2).
Diluted urine (YW2), compost (SCout) and gaseous emissions (neglected) (BW1=0).
(1) Assumed is that enough carbon is available in garden waste to maintain C:N ratio of about 25
(as urine is separated the C:N ration is far more favourable, see Table 3.3, p.37).
(2) Compost to be used on-site (no transport, land available), 80% of urine separated for used.
(3) Emissions to air are assumed to be mainly H2O, CO2, and NH3 (TS removal mainly as CO2).
average
18
min
13
max
24
6
73
TSS
BOD
Ntot-N
Ptot-P
Cu
Zn
FC
15
90
40
0
0
0
95
H2O
60
35
86
General info:
Temperature
Retention time
average
??
-
min
max
Costs:
- investment (Euro)
average
3,500
min
1,000
max
7,000
- operation & m.
(Euro/y)
- life time (y)
Emissions:
CO2
CH4
NH3
Resources:
- energy: (kWh/y)
ventilation
heating
- nutrients:
urine separation (%)
- space (m2)
- water (l/flush)
flush faeces
flush urine
Rough estimate, final product: 3 MPN/g, max 200/g (NSFC
1998), <1 (Ecolet 2000).
86% Haug 1993 p46, 35% Lasardi 2000 p220, 52%
Trubetskaya 2001 p52
Remarks:
Cold slows process down, heat may dry the compost too much.
Depending on temperature and mixture, 1 to 2 years.
21
Remarks:
see below, 1000-1500, 1225, 1375, 1850, 1250-3000, 12006000, 3000-7000
Only 1 source mentions maintenance and operating costs.
10
(y)
To be calculated
To be calculated
To be calculated
average
min
max
180
1300
22
880
4
0.8
0
0
0
-1
-1
1
1
0
1
Remarks:
CO2=(TSin*0.18-CH4)/12*(12+2*16), see assumption 3
CH4=(NH3/(14+3))/3*4*(12+4), assumption 3 and CM 2000
NH3=Ntot-Nin*0.4/14*(14+3), assumption 3 and CM 2000
Remarks:
See below
330
1750
Hellström et al. 1999 (see also notes with TDS 8 urine
separation toilet)
Storage for compost and urine.
80
6
Qualitative indicators:
- acceptance
-1 / 0
- adaptability
- expertise
- quality of space
- institutional requir.
- maintenance
- participation
- reliability
- robustness
- sustainable behav.
Remarks:Removed from faeces – urine is separate stream!
21% Haug 1993 p46, 13% Lasardi 2000 p220, 24%
Trubetskaya 2001 p52
Rough estimate.
Rough estimate.
See below, 28%, >0.5 and << 15%, 13%, 54%, >20%, 6 - 73%.
1.5
0.1
6
1.5
(l/flush)
(l/flush)
Remarks:
Approaches a ‘normal’ toilet, and is probably acceptable, use of hand-shower is
possible. Acceptance depends on handling and reuse of compost and urine.
Occupied space is likely situated in basement, the lost space is of low value.
Decentralise approach may be difficult for institutions to control.
The users have to empty the toilet them selves yearly, urine can be used as fertiliser.
Users recycle the compost themselves.
Limited electricity and water supply are a requirement.
Process is quite robust due to heater, ventilator, and rotating compartment system.
Low flush + compost recycling stimulates sustainable behaviour.
Sustainable Wastewater Treatment
Appendix 3: Technology data sheets
Note:
In contract with the dry composting toilet, this low flush toilet consists of 2 parts, namely the toilet and urine
separation device and the composting chamber. Sometimes these combined in one system, however, as
flush water is used it is possible to install the composting chamber separate (not necessary right beneath the
toilet). Urine separation can be done in the toilet bowl or by using urinals, or just after the toilet, or
alternatively one can collect the leachate from the compost separately.
This fact sheet describes a combined system including both toilet and composting chamber. Hellstrom et al.
(1999), found that depending on the type of urine separation toilet about 50 to 80% of the urine can be
collected separate. With urinals 50% separation should be achievable (urine from male population).
Therefore, a proper urine separation toilet should achieve at least 70% separation to add functionality.
Estimating the removal of Water, Carbon and Nitrogen:
From the mass balance in Haug 1993 (see Figure 3.2) we find removal of water of 86% and a reduction of total
solids of 21%. Based on the measurements of Lasardi 2000 (see Table 3.3) we can calculate the total solids
removal based on the assumption that the P is inert (P-total unchanged), this gives us a s decrease of solids of
13% while an increase of 23% is measured (%ww). If we assume this difference due to water evaporation, the
water removal is 35%. Similar calculations can be done based on the measurements of Trubetskaya 2001 (see
also Table 3.3), giving a TS removal of 24% and a water removal of about 52%. In the vent gas carbon dioxide
was measured to be 0.2%, humidity was 95%, hydrogen sulphide was 0.5 ppm, ammonia 3 ppm, and methane 4
ppm (not detectable were sulphur dioxide, carbon monoxide, and methyl mercaptan) (CM 2000). From these
measurements, it becomes plausible that the main loss in TS is due to carbon losses.
Assuming, as done in the ORWARE model, that the major part of nitrogen in compost is bound to humus, while
the remainder is mineralised as NO3- and NH4+. Gaseous losses are estimated according the following
equation; N loss = 0.55903 − 0.01108 * (C / N ) in which Nloss is the lost fraction of the incoming nitrogen, C
is the incoming carbon, and N is the incoming nitrogen. The nitrogen loss is mainly NH3 (96% while 2% is N2O,
and 2% is N2) (Kärrman 2000, p.88, and Sonesson 1998, p.28). According to the formula, the nitrogen loss at a
C:N ratio of 25 is about 28%. According to Gotaas (in Jenkins 1994, on p.29) this would be much lower, namely
14.8% for a C:N ratio of 22 and 0.5% at a C:N ratio of 30. According to the measurements of Lasardi the Total
N as % of the dry weight is the same in the sludge straw mixture and final compost, however, as TS the TS
contents increases, the nitrogen loss can be estimated at 13%. Similar for the Trubetskaya measurements an
estimate of 54% N-total reduction can be estimated. Bengtsson et al (1996, p.44) estimates a nitrogen loss for
kitchen waste composting of 20%, however, in kitchen waste the nitrogen contents is significantly lower than in
mixed faeces, urine and biowaste. Based on the measurements by Ekolet (Ekolet 2000), Compost: C 27%, N 2.6%,
NH4-N 0.2%, NO3-N 0.7%, P 1.9%, K 1.8%, Mg 0.8%, pH 6.6%, dry matter content 30%. Leachate: pH 7.5, BOD7 + ATU
4 mg/l, CODcr 42 mg O2/l, Total N 4.9 mg/l, Total P 4.2 mg/l, Cd < 0.001 mg/l, Hg < 0.0002 mg/l, Faecal coliforms 0, the
Pathogen reduction:
In this process, faecal coliforms are reduced, according to measurements of NSFC 1998 to a count of 3 MPN/g
in compost with a moisture content of 7.8 % by weight. For the BioLet Deluxe composting system after 6 month of
use, this is a dry toilet, electric mixing device and fan (20 Watt) and evaporation of excess liquid by heating (225 Watt and 80
Watt). Test results Ekolet (Ekolet 2000): Compost: coliform bacteria (35°C) < 1 per gram, E. coli < 1 per gram, dry matter
content 30%, Leachate: Faecal coliforms 0.
Energy use for electric ventilation is about 22 to 330 kWh/y, and for heating about 880 to 1750 kWh/y, or even higher when
all excess liquid is evaporated. For instance: 2.6 Watt Naturum (1100kWh/y), 166 kWh/y for Separett Villa 6000, 21 to 37
Watt in Fittschen etal. 1997. For instance: 100 to 200 Watt (876 – 1752 kWh/y) in Fittschen 1997, 1000 kWh/y Lange 1997
(fan + heating), Sun-Mar Excel 150 Watt fan + heating.
Prices: Mountain Lion Trading (Oct. 2000) 1,000 to 1,500 US$, EPA (2000) factsheet 1,200 to 6,000 US$, Fittschen etal.
(1997) 11,250 to 26,470 SEK (approx. 1,250 – 3,000 Euro), Lange (1997, p.115) 6,000 to 13,000 DEM (approx. 3,000 to
7,000 Euro)., at www.environment-agency.gov.uk prices are listed for Barton heated composting toilet 735 UKP (1225 Euro)
investment, and 12.50UKP (21 Euro) for yearly maintenance, and for Biolet 826 (1,375 Euro)(most costly option) and
12.50UKP/y, Ecolet prices: Orivesi model (year round toilet) 1,850 Euro, and Holiday-home model 550 Euro (excl.
transport).
Sustainable Wastewater Treatment
Appendix 3: Technology data sheets
Internet-sites of manufactures of composting toilets:
Alascan: http://www.alascanofmn.com/ email: [email protected], system for upto 10 adults, electric mixing,
ventilation, liquid recycling. Approximately 3 years to produce finished compost, characteristics 10-10-10 (N-P-K), with
traces of Ca, S, and Mg. From all toilet and kitchen waste biologically converted to carbon dioxide and water vapour,
and 90 to 99 % of pathogens are eliminated. Approximately 8 to 10 gallons of valuable soil amendment is produced by a
family of four per year.
Aquatron: http://www.aquatron.se , email: [email protected] , selling a composting system for a normal toilet (3 to 6 litre
flush) with liquid-solid separator just before the composting chamber and a drain for leachate from the compost,
followed by a UV-disinfection unit after which the water is mixed with greywater.
Biolet: http://www.biolet.com , email: [email protected] , selling for instance a self contained composting toilet for 999 US$
(November 200), excess liquid is evaporated, a fan is optional.
Dubletten: http://www.dubbletten.nu email: [email protected] , urine separation toilet designed to comfortable
separate the urine at the source uncontaminated. The blackwater is treated by composting while the yellowwater is
stored separate for use as fertilizer.
Ecolet: http://www.ekolet.com/ekolet-eng/ email: [email protected] , rotating four compartment composting tank, electrical
ventilation, excess liquids to greywater sewer, can produce high quality of compost high in nutrients and low in
pathogens, according to test results on internet page.
Ecotech: http://www.ecological-engineering.com/ecotech.html email: [email protected], sells waterless
and double flush urine diverting toilet, the Carousel composting toilet systems, grey- and rainwater systems. The
composting toilet system can be used with low flush or dry toilets. The carousel system is divided into four
compartments, which are filled after each other. Emptying is done as soon as all four compartments are full
(approximately after 2 years every 6 months a compartment is emptied). The excess liquid from urine and flush water is
leaches through the compost and can be diluted with greywater (1:8) and be applied as fertiliser (preferably subsurface).
Prices range for a complete system range approximately from 2,100 to 4,550 US$ FOB (July 2000).
Nature Loo: http://www.nature-loo.com.au/ email: [email protected], liquid drains through the compost and excess is
collected/infiltrated, one chamber when full replaced by empty compartment leaving the full one stored to cure the
compost, foot pump for spaying moisture on the compost, prices range from 595 to 2190 Australian Dollars excluding
freight.
Naturum: http://www.naturum.fi/english/index_e.htm rotary drum composter toilet, self-contained, urine separation,
emptying about once a month when in use for four persons. electric fan (12 V, 2.6 Watt - estimated electricity use 1100
kWh/y), price 992 Euro incl. transport, excl. taxes.
Phoenix: http://www.compostingtoilet.com/ email: [email protected] , dry toilets, rotating device, moisture
control through spraying, excess moisture is collected in lower part of the composting chamber, clear instructions on
adding bulking material (two gallons every 100 uses).
Separett: http://www.separett.com/english/index.htm email: [email protected] , a range of dry toilets with urine separation
(collection tank, infiltration, or to sewerage), energy consumption for fan about 166 kWh/y for Separett Villa 6000.
Sun-Mar: http://www.sun- mar.com email: [email protected] or [email protected], self-contained toilets (price 949
to 1160 US$ June 1997) and central composting units (1100 to 2000 US$), both with rotating drum system, excess
liquid is evaporated. Average electricity use for fan and heating 150 Watt for the Excel model).
Info and prices are also available trough Internet shops such as http://www.jademountain.com or http://www.mtlion.com.
Sustainable Wastewater Treatment
Appendix 3: Technology data sheets
Technology Data Sheet 7: Solar dry toilet.
Description:
Scale:
Constraints:
Processing unit:
Incoming
streams:
Outgoing
streams:
Assumptions:
This dry toilet has urine separation and a solar heater above a drying chamber for faeces.
1 system per household, <= 10 pe.
Hot and dry climate.
Toilet (t).
Faeces and urine (Fin, FW2 =0, YW2=Uin+FW3, FW3=0).
Removal rate
(%):
TS
TSS
BOD
Ntot-N
Ptot-P
Cu
Zn
FC
average
H2O
General info:
Temperature
Retention time
90
average
-
Costs:
- investment
(Euro/system)
-O&M
(Euro/system/y)
- life time (y)
average
600
Emissions:
CO2
CH4
NH3
Resources:
- energy:
- nutrients
- space (m2)
- water
average
0
0
0
average
0
0
4
0
Dried faeces and urine (SCout, YW2, BW1=0).
(1) 80% of urine separated (separate stream),
(2) Faeces dried up to 90% H2O removed – otherwise no change in composition.
min
max
Remarks: Removal rates for faeces, urine is separate stream.
max
Rough estimate - If heated and dried properly the material
will be safe in use as soil conditioner.
See assumption 2 – water evaporates.
Remarks:
0
0
0
0
0
0
0
95
min
One month up to one year.
min
max
Remarks:
Estimated: twice the price for a VIP latrine (Loetscher 1999,
p.147)
Maintenance and emptying by user / farmer.
min
max
min
max
Remarks:
Neglected
Neglected
Neglected
Remarks:
0
10
Qualitative indicators:
- acceptance
0
- adaptability
0
- expertise
- quality of space
- institutional
requirements
- maintenance
- participation
- reliability
- robustness
- sustainable
behaviour
0
0
-1
0
1
1
0
1
Rough estimate
Remarks:
Accepted only if water is not seen as essential for hygiene or when water us extremely
scares.
Can be built in different forms for instance several toilets in one block, once built little
to adapt.
Design requires some expertise.
Decentralised and therefore more difficult to control performance of system / health
risks.
Emptying every several months or once a year.
User of toilet also maintainer and user of soil conditioner.
No supply of energy other than sun, and no water supply.
Depends on climate and design.
No flush water and reuse of nutrients.
Sustainable Wastewater Treatment
Appendix 3: Technology data sheets
Technology Data Sheet 8: Urine separating toilet.
Description:
Scale:
Constraints:
Processing unit:
Incoming
streams:
Outgoing
streams:
Assumptions:
Low flush toilet collecting urine separately, either a special toilet with two compartments or a urinal
and normal toilet.
1 system per household
Requires facility for urine collection.
Toilet (t).
Faeces (Fin), urine (YW1=Uin+FW3), and flush water (FW2).
Urine (YW2) and faeces (BW1), both streams diluted by flush water and toilet paper with the faeces
(SCout=0).
(1) 80% of the urine is separated, in case of urinal 50%.
(2) No change in composition other than mixing with flush water.
Removal rate
(%):
TS
TSS
BOD
Ntot-N
Ptot-P
Cu
Zn
FC
H2O
General info:
Temperature
Retention time
Urine
separating toilet
0
0
0
0
0
0
0
0
0
average
-
Normal toilet +
urinal
0
0
0
0
0
0
0
0
0
average
-
Remarks:
Costs:
Urine
separating toilet
820 (650-990)
Normal toilet +
urinal
200 (140-1150)
Remarks:
0
0 (0-76)
10
10
Urine
separating toilet
0
0
0
average
0
80
Normal toilet +
urinal
0
0
0
average
0
40
1
2
4 (0.6 –6)
0.2 (0-2)
9 (6-9)
2 (0-2)
Urine
separating toilet
-1
0
0
0
0
0
1
1
0
1
Normal toilet +
urinal
0
0
0
0
0
0
1
1
0
1
- investment
(Euro)
- operation & m.
(Euro/y)t.
- life time (y)
Emissions:
CO2
CH4
NH3
Resources:
- energy:
- nutrients
separation (%)
- space (m2)
- water (litres)
- flush faeces
- flush urine
Qualitative
indicators:
- acceptance
- adaptability
- expertise
- quality of space
- institut. requir.
- maintenance
- participation
- reliability
- robustness
- sustain. behav.
Remarks:
See below, waterless urinals, normal toilet + urinal 140 to 500
Euro.
See below – special chemicals required for some waterless
urinals.
Remarks:
Remarks:
Urine separation rate.
Some extra space is required for urine storage and urinal.
Remarks:
Separating toilet look different and then require explanation
Some extra cleaning maybe required – but depends on design.
Urine separation in toilet bowl visualised separation and reuse
Low in water demand, no energy supplied required.
Some blocking of urine pipe can occur in bad designs.
Low flush volume and nutrient recycling in behaviour pattern.
Sustainable Wastewater Treatment
Appendix 3: Technology data sheets
Notes:
Urine separation can be done in the toilet bowl or by using separate urinals, or just after the
toilet, or in composting toilets one can alternatively collect the leachate from the compost
separately. The rate of urine separation was found 60 - 80% for Dubbletten, and 50-65 for
DS-toilet (Hellström etal.1999). With urinals 40% separation should be achievable (urine
from male population is 50%, but not all urine will be collected separate, kids for instance
may use the ‘normal’ toilet). Therefore, a proper urine separation toilet should achieve at least
70% separation to add functionality. In the data sheets it is assumed that 80% of the urine is
collected without contamination.
Toilet types (see Swedenviro 2001):
(1) Wost Man Ecology:(www.wost-man-ecology.se) flush 0.6 litre/flush for urine 0 to 0.4 litres/flush, 990
Euro, WM-DS 0 to 0.7 for urine and 3.5 litres/flush for faeces, 650 Euro (Swedenviro 2001).
(2) BB Innovation & Co AB, Dubbletten, 4 litres or 1.5 to 2 dl/flush, 728 Euro, (Swedenviro 2001),
www.dubbletten.se.
(3) Gustavsberg, Model Nordic, 4 and 2 litres/flush, 720 Euro (Swedenviro 2001).
(4) Roediger Vakuum + Haustechnik, Roevac no mix, 0 for urine, 6 litres per flush for faeces, 750 Euro
(Swedeneviro 2001), www.roevac.de
Waterless urinals:
(1) http://www.ernstsystems.com approximately 410 Euro, 540 Euro investment and 76 Euro maintenance
per year (original figures: 322 UKP, and 15UKP every 120 days for maintenance, see
www.environment-agency.gov.uk/envinfo/nwdmc/being_w.../fact_13.htm)
(2) http://www.uridan.com about 600 - 900 Euro,
(3) http://www.waterless.com about 530 Euro (Swedenviro 2001)
Sustainable Wastewater Treatment
Appendix 3: Technology data sheets
Technology Data Sheet 9: Vacuum toilet.
Description:
Scale:
Constraints:
Processing unit:
Incoming streams:
Outgoing streams:
Assumptions:
Low flush toilet that uses vacuum to transport excreta.
1 system per household.
Requires vacuum sewer.
Toilet (t).
Faeces (Fin), urine (YW1=Uin+FW3), and flush water (FW2).
Mixture of faeces, urine, and flush water (BW1) (Note YW2 and SCout are 0).
(b) 1 litres per flush,
(c) Only mixing – no reactions or removal of substances.
Removal rate (%):
TS
TSS
BOD
Ntot-N
Ptot-P
Cu
Zn
FC
H2O
General info:
Temperature
Retention time
average
0
0
0
0
0
0
0
0
0
average
-
min
max
Remarks:
min
max
Remarks:
Costs:
- investment
(Euro/toilet)
- operation & m.
(Euro/y)
- life time (y)
average
800
min
376
max
1470
Remarks:
Reported costs: 376, 580, 640, 800, 876, 1470 – see below
Emissions:
CO2
CH4
NH3
Resources:
- energy:
(kWh/y/system)
- nutrients
- space (m2/system)
average
0
0
0
average
100
- water (litres)
flush water
Qualitative indicators:
- acceptance
- adaptability
- expertise
- quality of space
- institutional req.
- maintenance
- participation
- reliability
- robustness
- sustainable
behaviour
0
10
(y)
min
max
min
2
max
450
Remarks:
Neglected.
Neglected.
Neglected.
Remarks:
Reported: 2, 10, 50-60, 142, 450, see below
7.2
Estimated, 1 m2 for toilet and some extra room for pumps
etc.
Reported: 0-1.2, 0-0.5, 0.1-0.2, 0.2-1.5, 0.3-0.4, 0.5, 0.3-1.9,
0.5-0.9, 1, 1.2, 2, 0-7.2, see below
0
2
1
1
0
-1
0
0
0
0
0
0
1
0
Remarks:
High-tech imago, not very different in use than water closets, some noise though.
Expertise required for maintenance.
Requires more space than normal toilet, vacuum unit, but can be in basement.
Some expertise required
Requires energy and water supply and high-tech maintenance.
Can clog when flushing large items.
Low flush water required and not usable to flush sanitary waste etc.
Sustainable Wastewater Treatment
Appendix 3: Technology data sheets
Notes:
Vacuum toilet systems:
• Clearvac, on Internet: www.wost-man-ecology.se , flushes 0.3 – 0.4 litres.
• Evac, see www.evacgroup.com , 1.2 litres per flush, 800 Euro in AquaSpec2000, 580 Euro in
Swedenviro 2001, complete vacuum system 18,000 Euro.
• Jets, Hareid, Norway, 0.2 – 1.5 litres/flush, in Swedenviro 2001: two models with flush volumes 0- 1.2
litres, and 0- 7.2 litres, energy use approximately 10 kWh/y/household, toilet 640 Euro, complete
vacuum system 3,500 Euro (contact: [email protected]).
• KTH Arkitektur, LVS low vacuum system, 0.35 and 1.9 litres/flush, 250 kWh/y/2.8 persons (including
ventilation), contact: [email protected] (in Swedenenviro 2001).
• Microphor, Microflush, 2 litres per flush, www.microphor.com .
• Roediger Vakuum + Haustechnik, Roevac vacuum toilet, 1 litre/flush, 10 to 12 kWh/year/person, [email protected] (Swedenenviro 2001).
• Sanivac Vakumtechnik GmbH, Wedel, Germany, email: [email protected], 0.5 litre/flush
(www.environment-agency.gov.uk (12/03/01)).
• SeaLand, VacuFlush, trademark of Sealand Technology, see www.sealandtechnology.com , model
SeaLand 1 to 2 dl/flush, 376 Euro, Model Sealand Vacuflush 0.5 to 0.9 l/flush, 1,470 Euro (Swedenviro
2001).
• VakuTech internet site: www.vakutech.de , INFO: energy use: 142,56 kWh/y for 1 household with 4
people flushing 4 times a day, 330 days a year, installation costs 5 euro?
• Wost Man Ecology:(www.wost-man-ecology.se Model Clever-Vac, 0.5 or 0 to 0.4 litres/flush, 2
kWh/y, 876 Euro (Swedenviro 2001).
Sustainable Wastewater Treatment
Appendix 3: Technology data sheets
Technology Data Sheet10: Rainwater system (basic).
Description:
Scale:
Constraints:
Processing unit:
Incoming streams:
Outgoing streams:
Assumptions:
A basic system to collect rainwater for toilet flushing (350 litre storage tank, rainwater filter, no
pump).
1 system per household.
Requires regular rainfall and a roof area.
Rainwater system (rws).
Rainwater (Rin) and pollutants from the roof (C6).
Water to toilet flushing (R1), and overflow (R5).
(1)
no significant filtering by rainwater filter, only larger solids and organics are removed
by overflow to sewer,
(2) no significant pathogen growth in storage tank,
(3) based on simulation for the Dutch situation, we assume that for a 50 m2 roof area this
system can utilise approximate 45% (35 - 55%) of the total rainwater collected by the roof.
Removal rate (%):
TS
TSS
BOD
Ntot-N
Ptot-P
Cu
Zn
FC
H2O
General info:
Temperature
Retention time
average
5
0
5
0
0
0
0
0
0
average
min
max
Remarks:
see assumptions 1 and 2
min
max
Remarks:
outdoor water temperature
not relevant
Costs:
- investment
(euro/system)
-O&M
(euro/system/y)
- life time (y)
average
500
min
120
max
1000
Remarks:
Emissions:
CO2
CH4
NH3
Resources:
- energy:
- nutrients
- space (m2)
- water:
rainwater utilisation
average
0
0
0
average
0
0
2
min
max
Remarks:
min
max
Remarks:
45
35
0
10
Qualitative indicators:
- acceptance
0
- adaptability
- expertise
- quality of space
0
0
0
- institutional
requirements
- maintenance
- participation
- reliability
- robustness
-1
- sustainable
behaviour
1
0
1
1
-1
Depends on shape of tank.
55
(% of total rainwater collected by roof) see assumption 4
Remarks:
Generally people are very positive to rainwater systems, however this low end
rainwater system may only be acceptable for people with some expertise, as
required do-it-yourself.
The space occupied (0.35 m3) is often low quality space (attic, balcony, roof
garage)
Decentralised systems may be hard to control and inspect, however risks are
limited.
The users built and maintain their system themselves
Does not depend on energy or control equipment.
Depends on rainfall and water use, for Dutch situation with toilet flush interrupter
reliability quite high – empty only 3 to 4 times a year for a few days.
Feedback on water use, incentive to use water saving toilet
Sustainable Wastewater Treatment
Appendix 3: Technology data sheets
Technology Data Sheet11: Rainwater system (advanced).
Description:
Scale:
Constraints:
Processing unit:
Incoming
streams:
Outgoing
streams:
Assumptions:
Rainwater collection for toilet flushing, washing etc, (storage tank ( >3 m3 ) underground, rainwater
filter, pumps, and control devices.
1 system per household.
Requires roof area and rain.
Rainwater system (rws).
Rainwater from and pollutants roof (Rin + C6).
Water to domestic uses (R1, R2, R3, R4) and to overflow (R5), and some of the solids are filtered
out and rain in excess rainwater, the rest accumulates in the storage tank and may concentrate
pathogens, metals, and PAK if available in rainwater.
(d) Only larger solids are filtered out by the rainwater filter and flow with first rain to sewer,
(2) no significant pathogen growth in storage tank,
(3) tank volume not limiting for rainwater utilisation.
Removal rate
(%):
TS
TSS
BOD
Ntot-N
Ptot-P
Cu
Zn
FC
H2O
General info:
Temperature
Retention time
average
Costs:
- investment
(euro/system)
-O&M
(euro/system/y)
- life time (y)
Emissions:
CO2
CH4
NH3
Resources:
- energy:
(kWh/m3)
- nutrients
- space (m2)
- water
rainwater
utilisation
average
0
0
0
average
7.5
10
0
10
0
0
0
0
0
0
average
7
0
min
max
Remarks:
see assumptions 1 and 2
Removal due to natural die-off – neglected.
min
0
max
15
Remarks:
Rain & underground water temperature.
Not relevant.
average
2600
min
1200
max
4000
Remarks:
36
18
27
min
max
Remarks:
min
5
max
10
Remarks:
Ewijk et al. 1998, p.32.
0
6
10
0
3
100%
Qualitative indicators:
- acceptance
- adaptability
- expertise
- quality of space
- institutional req.
- maintenance
- participation
- reliability
- robustness
- sustainable
behav.
1
0
0
0
0
0
1
0
1
1
Tank placed underground.
(% of total rainwater collected by roof), see assumption 4
Remarks:
Generally people are very positive about rainwater use.
The space occupied (3 m3) can be underground/ in basement therefore not ‘lost’.
Decentralised system may be hard to control and inspect, although risks are limited.
The users maintain their system themselves.
Depends on electricity for pumping and control equipment.
Due to tank size almost always rainwater in storage for Dutch situation.
Feedback on water use, incentive to use water saving toilet.
Sustainable Wastewater Treatment
Appendix 3: Technology data sheets
Some suppliers of rainwater systems:
3P Technik – components for rainwater systems, see http://www.3ptechnik.de/producte/farohr.html
ASP UWO-Combimat, the rainwater system exists of a underground tank and uses a pump (700 to 850 W).
dBCOM – components for rainwater systems and consultancy on alternative energy and water, see
http://dbcom.nl/rege-fil.htm
Harry
Rode
Tiefbautechniek:
components
for
rainwater
harvesting,
see
http://www.harryrode.de/regenprofi/prod/filt/laubabscheider.htm
Heflex Water Systems – Sittard, The Netherlands.
HeWa – Oude Pekela, the Netherlands – also small scale gravity systems for 500 euro (excl. VAT and
installation), http://www.hewa.nl see also Haffmans 1995, p.32-34.
KBS Nederland bv – Hya-Rain system, Zwanenburg, the Netherlands.
Kilian Water – rainwater and greywater systems (constructed wetlands), www.kilianwater.nl
Kordes – international company, rainwater systems from 6 m3 to 540 m3 – see www.kordes.de
Rezo – rainwater use and infiltration systems, tank size 2000 to 3000 litres, costs without installation
approximately 1500 Euro – see www.rezo.nl
Wavin KLS – Azura system, Hardenberg, the Netherlands.
Sustainable Wastewater Treatment
Appendix 3: Technology data sheets
Technology Data Sheet 12: Sewerage systems.
Description:
Scale:
Constraints:
Processing
unit:
Incoming
streams:
Outgoing:
Assumptions:
Removal rate
(%):
TS
TSS
BOD
Ntot-N
Ptot-P
Cu
Zn
FC
H2O
General info:
Temperature
Retention time
Costs:
- investment
(Euro/m)
- O. & M.
(Euro/m/y)
- life time (y)
Emissions:
CO2
CH4
NH3
Resources:
- energy:
(kWh/m3)
- nutrients
- space
- water
Qualitative:
- acceptance
- adaptability
- expertise
- quality space
- institut. requir.
- maintenance
- participation
- reliability
- robustness
- sustain. behav.
38
Piped transport of wastewater.
Per km transport distance (in case of on-site treatment 0 km).
If all wastewater and rainwater to combined sewer than CSO of 338 %.
Blackwater, greywater, and yellowwater transport (bwt, gwt, ywt).
Depends on systems, mixed than domestic wastewater + rainwater, separate than domestic wastewater
and separate rainwater, vacuum than either mixed domestic wastewater (no rainwater) or blackwater.
Mixture of incoming streams (BW3, CSOout, GW2, YW5).
(1) Only mixing no reactions or separation.
(2) All sewerage systems underground therefore no occupation of high quality space.
Mixed
Separate
Vacuum
Remarks:
0
0
0
0
0
0
0
0
0
Mixed
0
0
0
0
0
0
0
0
0
Separate
0
0
0
0
0
0
0
0
0
Vacuum
Remarks:
Mixed
183 (104a
150-210b
270c)
3.9
(3.7-4.5b, 3.4c)
50
Separate (1 flow!)
188=0.5*377
(365-390c,d)
Vacuum
85
(60-110b)
2.3=0.5*4.7
(4.5-5c)
50
3.9
(3.3-4.5b)
50
Mixed
0
0
0
Mixed
0.4
(0.08e, 0.22f,
0.1g, 0.690.86h)
-
Separate
0
0
0
Separate
0.35=0.5*0.7
(mixed: 0.69h,
rain: 0.75h, grey:
0.86h, 0.07i)
-
Vacuum
0
0
0
Vacuum
17
(8i, 44j,
0.7jk)
Separate
1
0
0
0
0
0
0
0
0
0
Vacuum
1
0
-1
0
0
0
0
-1
0
0
Mixed
1
0
0
0
0
0
0
0
-1
0
-
Remarks:
average and in brackets reported data from
literature: (a) KIWA 1998, (b) RIONED 1998,
(c) RIONED1996, (d) Geldof 1997b, p.23-27
average and in brackets reported data from
literature
Remarks:
Remarks:
(e) Bengtsoon et al. 1997, p.13, (f) Dalemo 1996,
(g) calculation based on CBS 1998, (h) Ewijk et
al. 1998, (i) Otterpohl 2000, p.13, (j) QUA-VAC
2001, (k) Sydney Water 1997
See assumption 2
Remarks:
In all systems the convenience is high.
Vacuum systems can easily pump wastewater up.
Vacuum:more expertise and maintenance.
All system underground.
Depends on design of the system.
Depends on design of the system.
Vacuum: needs electricity + special spare parts.
Mixed systems result is cso’s.
Schijven et al 1995 report a 9% of the total wastewater volume that is discharged with out treatment (including CSO) in 1990. The wastewater discharged
untreated, in the case of CSO, will be diluted, rough estimate is that the pollution load is about 3% of the total yearly wastewater pollution. More data will
become available from a to be published report by CIW in 2001.
Sustainable Wastewater Treatment
Appendix 3: Technology data sheets
Technology Data Sheet 13: Transport by Truck
Description:
Scale:
Constraints:
Processing unit:
Incoming streams:
Outgoing streams:
Assumptions:
Transport of wastewater or sludge by truck.
Per km transport distance.
Sludge transport and yellowwater transport (bst, ywt).
Sludge or urine (BS1, BS2, BS3, BS4, or YW3).
The incoming stream (BS5, YW5).
(1) Only transport no changes in composition.
(2) Space for storage incorporated in toilet or treatment systems, space truck neglected.
Removal rate (%):
TS
TSS
BOD
Ntot-N
Ptot-P
Cu
Zn
FC
H2O
General info:
Temperature
Retention time
average
0
0
0
0
0
0
0
0
0
average
-
min
max
Remarks:
min
max
Remarks:
Costs:
- investment
- operation
(Euro/kg)
average
0.06
min
max
Remarks:
0.06
0.02*
0.09
Rough estimate based on CBS 1999 and 1999b, data for
collection of solid domestic waste. * RIVM/CBS 2000,
F1.12.
STOWA 1998, p.157, dewatering, transport and
incineration.
average
0.1
average
min
0.002
max
0.28
Remarks:
Bos 198 p.258
min
max
Remarks:
0.0008a
0.46a
0.0005b
0.001b
545
+ treatment
(Euro/kg d.s.)
- life time
Emissions:
CO2 (g/kg/km)
CH4
NH3
Resources:
- energy:
Sludge
(kWh/kg/km)
Urine (kWh/m3/km)
- nutrients
- space
- water
Qualitative indicators:
- acceptance
- adaptability
- expertise
- quality of space
- institutional
requirements
- maintenance
- participation
- reliability
- robustness
- sustainable
behaviour
-
(a) Bengtsson et al. 1997, p.17+20, (b) Bos 1998 p.258.
-
0
0
0
0
0
0
0
0
0
0
Remarks:
Less comfortable than sewerage but acceptable if emptying is required once in a
while.
Can empty on demand therefore transport systems is sized on needs.
May stimulate sustainable behaviour in the sense that the amount of waste may be
visible, this depends on system design.
Sustainable Wastewater Treatment
Appendix 3: Technology data sheets
Technology Data Sheet 14: Infiltration.
Description:
Scale:
Constraints:
Processing unit:
Incoming :
Outgoing :
Assumptions:
Infiltration of rainwater or treated wastewater onsite instead of transport.
Per household.
Requires suitable soil conditions and low enough water table, in case of wastewater treatment is
required. Constraints on the influent are: BOD < 20 mg/l39, TSS < 30 mg/l1, and FC <
2,000/100ml40.
Blackwater, greywater, and rainwater treatment (bw4, gw2, rw1).
Rainwater and/or treated wastewater (R8, O3, GW3, BW6).
None – meaning that the water infiltrates in the soil, topsoil must be refreshed form time to time.
(1) assuming 100% infiltration, all water supplied is taken up by the soil.
* Includes also rapid infiltration (Sperling in 1996), **
Soak
Swales &
Infiltration
Includes Subsurface infiltration, Overland flow (both
away
ditches*
field**
Sperling 1996) and Infiltration field (RIONED 1998)
Removal rate (%):
TS
TSS
BOD
90
99 (> 90a)
95 (> 95a)
Ntot-N
20
Ptot-P
80 (> 80a)
Cu
Zn
FC
H2O
General info:
Temperature
80 (> 80a)
90 (> 90a)
99
100
Costs:
- investment
(Euro/household)
- o & m.
- life time
Emissions:
CO2
CH4
NH3
Resources:
- energy: (kWh/y)
- nutrients
- space
(m2/household)
- water
80 (> 80a,
30 – 99c)
80 (> 80a)
90 (> 90a)
99 (> 99c)
100
90
90
95 (90c, 8598d)
20 (10-15c,
10-80c)
80 (20-95c)
80
90
99 (90- >99c)
100
Remarks:
Remarks:
136 (136a,
5,000b)
20
42 (25-60c)
20
20
-
-
-
42 (25-60c)
0
0c
0.5a
17 (5 –30d)
0 (350–
1,325c, 0d)
17 (5 –30d)
-
-
(b) Burkhard et al. 2001 in US$/hh
Remarks:
Remarks:
General
Qualitative indicators:
- acceptance
0
- adaptability
0
- expertise
0
- quality of space
1
- institut. requir.
0
- maintenance
0
- participation
0
- reliability
1
- robustness
0
- sustain. behav.
0
39
90
90 (> 90a)
95 (> 95a,
86 – 98c)
20
Remarks: amount removed remains in topsoil, rest
to groundwater and soil.
Rough estimate based on TSS.
(a) Geldof et al. 1997b, p.29-35
(c) RIONED 1998
(d) Sperling 1996, p.66-67
Rough estimate.
Assuming 5 pe per household
Remarks:
If designed correctly – readily accepted.
Depends on design, and local situation
Swales, ditches and infiltration fields offer esthetical green areas.
Requires no electricity, or control.
Estimated to be somewhat stricter than European guidelines for discharge (BOD < 25 and TSS < 35 mg/l, 91/271/EEC). Note BOD and TSS are not mentioned
in bathing water or irrigation guidelines.
The FC restriction is based on the bathing water standard (2,000 FC/100ml, 76/160/EEC) for swales and ditches and public accessible infiltration fields this is
reasonable. For un-accessible infiltration facilities the restriction on FC is non applicable. Note that guideline for irrigation in public parks and sports facilities is
lower 1,000 FC / 100ml (Mara et al. 1989).
40
Sustainable Wastewater Treatment
Appendix 3: Technology data sheets
Technology Data Sheet 15: Activated sludge
Description:
Scale:
Constraints:
Processing unit:
Incoming:
Outgoing:
Assumptions:
Size (pe)
Removal (%):
TS
Biological degradation though micro-organisms that are held in suspension in an aerated tank.
5, 50, 400, 1000, 100,000 pe.
Onsite treatment (ost, 5 pe), blackwater treatment (bw2, > 5 pe).
Black or mixed domestic wastewater (with or without rainwater) (BW1, GW1, B2=0, BW4).
Effluent and sludge stream (BW2, BS1, BW5, BS3, BGout=neglected=0).
(1)
Activated sludge here refers to a unit operation not including pre and post treatment,
(2)
In large systems nitrification and denitrification is included,
(3)
Treatment on larger scale more efficient,
(4)
Assumed is that all what is removed goes to sludge except 50% of the removed TS, TSS,
BOD, N-total, and FC (rough estimate !!).
5
50
400
1,000
>100,000
50
55
60
60
70
TSS
70
75
80
80
90
80-95
BOD
Ntot-N
Ptot-P
Cu
Zn
FC
H2O
General info:
Temperature
Retention time
Size (pe)
Costs:
- investment
(Euro/pe)
80
50
30
30
30
80
30
85
60
50
50
40
90
35
90
70
60
50
50
95
20
90
70
60
50
50
95
20
90
70
70
60
60
99
15
-O&M
(Euro/pe/y)
- life time (y)
Size (pe)
Emissions:
CO2
CH4
NH3
Resources:
- energy:
(kWh/pe/y)
- nutrients
- space (m2/pe)
Remark:
Rough estimate based on data from literature and
assumption 3, note TSS approximately 30% of TS in
medium strength wastewater (Tchobanoglous 1991,
p.56).
Literature: Alexandre 2000, RIONED 1998, 10.2,
Henze 1997, p.58, bosman 1997, Helmer 1997, p.58,
STOWA 1998, p.39, 156.
Remark:
5
50
400
1,000
>100,000
800
500
400
200
20
100
70
40
30
7
10
5
10
50
20
400
20
1,000
40
>100,000
Remark:
Estimated based on literature: Alexandre 2000,
Geenens 2000, Geldof 1997b, 45-53, Nieuwenhuijzen
2000, Nowak 1999.
See above.
Remark:
Neglected.
Neglected.
Neglected.
75
75
40
40
15
0.4
0.2
0.1
0.1
0.02
Estimated, RIONED 1998 50-54, Nieuwenhuijzen
2000
Estimated see: RIONED 1998 50-54, Nieuwenhijzen
2000, Khurana 1997
- water
Qualitative indicators:
- acceptance
1
- adaptability
0
- expertise
-1
- quality of space
-1
- institutional
-1 / 0 /1
requirement
- maintenance
0
- participation
0
- reliability
0
- robustness
-1 / 0
- sustainable
0
behaviour
Remarks:
Commonly used.
High expertise level required in design and operation.
All space is lost.
Decentralised hard to control / Centralised fits in existing institutional set-up.
Depends on electricity and control equipment.
Small-scale system less robust (5 pe).
Sustainable Wastewater Treatment
Appendix 3: Technology data sheets
Technology Data Sheet 16: Anaerobic digestion
Description:
Scale:
Constraints:
Proces. units:
Incoming
streams:
Outgoing:
Assumptions:
Wastewater treatment in absence of molecular oxygen.
Household (5 pe), neighbourhood (400 pe) and centralised (50,000 pe).
Removal rate
(%):
TS
5 pe
400 pe
50,000 pe
40
45
55
TSS
60
70
97
BOD
Ntot-N
Ptot-P
Cu
Zn
FC
H2O
General info:
Temperature
HRT (days)
60
10
20
40
40
95
20
70
20
40
80
80
99.9997b
10
95
40
50
95
95
99.9997b
10
35-55e
15
35-55e
15
On-site treatment (ost), blackwater (bw2) and black sludge treatment (bs1).
Blackwater, maybe mixed with urine and possibly with low amounts of flush water, and
biowaste (BW1+B2+GW2, or BW4, or BS1, BS2, BS3, BS4)..
Sludge and biogas (BS1, BG1 or BG2, BS3, or BG2out, BSout).
(1) of gaseous emissions 65% CH4 and the rest CO2, (2) inhibitors not present, (3) Large scale
treatment is more efficient than small scale, (4) Assumed is that all what is removed goes to
sludge except 50% of the removed TS, TSS, BOD, N-total, and 20% FC (Grey 1999, p.446,
rough estimate!! assumed: higher temperatures gives higher FC reduction).
Costs:
- investment
(Euro/pe)
-O&M
(Euro/pe/y)
- life time
5 pe
460
400 pe
400
50,000 pe
280
25
25
60
10
20
20
Emissions:
CO2 (g/pe/d)
5 pe
8
400 pe
10
50,000 pe
20
CH4 (g/pe/d)
Gas (l/pe/d)
% - methane
CH4 (kWh/pe/y)
NH3
Resources:
- energy
(kWh/pe/y)
- nutrients
- space (m2/pe)
- water
6
12
65a
18
5 pe
13
28
65a
42
50,000 pe
-15
0.4
-
8
16
65
24
400 pe
-110i
-20
0.1
-
-36=0.85*42
0.04
-
Qualitative:
- acceptance
- adaptability
- expertise
- quality space
- institut. req.
- maintenance
- participation
- reliability
- robustness
- sustain. behav.
5 pe
1
0
-1
-1
-1
0
1
-1
-1
1
400 pe
1
0
-1
-1
0
0
1
-1
0
1
50,000 pe
1
0
-1
-1
1
0
0
-1
0
0
Remarks:
Estimated based on assumption 4 and literature and
spreadsheet.
Baymoumy 1999, Gray 1999, p.446, Geldof et al. 1997b,
Huyard et al. 2000.
Rough estimate no data except 10 for 5 pe.
(b)
Remarks:
(e) Oldenburg 1997, p.5
(c) Tchobanoglous 1979, p.820, 825, 10-20 thermo, 30-60
meso.
Remarks:
Beek 1998, p.29 and Loetscher 1999, p.154, note 50,000
pe may be cheaper reported not full capacity.
Remarks:
CO2=gas production*0.35*density CO2 (1.98g/l),
assumption 1.
Estimated on: Lettinga 1993, 12-15 l CH4/cap/d, density
methane 0.72 kg/m3, Gray 1999, p.454, 545, % methane
ranges between 46-80, average 65%. Methane: 23 MJ/m3
(Gray 1999, p.454).
Remarks:
Estimated Otterpohl (1997),
Energy use 5% of prod. gas, los 10% Dalemo 1996, p5
Estimated, only 5pe in Geldof 1997b, Loetscher 1999
Remarks:
Used around the world, positive due to biogas.
Once constructed configuration fixed
Quite some expertise required in design and operation.
Space lost, maybe built underground though.
Decentralised is difficult to control.
Possibly in providing waste and using produced energy.
Depends on energy for heating and electricity.
Sensitive to temperature, influent and certain compounds.
Sustainable Wastewater Treatment
Appendix 3: Technology data sheets
Technology Data Sheet 17: Composting
Description:
Scale:
Constraints:
Processing unit:
Incoming :
Outgoing :
Assumptions:
Removal (%):
TS
TSS
BOD
Ntot-N
Ptot-P
Cu
Zn
FC
H2O
Larger scale composting (aerobic stabilisation) of sludge and or biowaste.
> 100 pe (note: small scale composting in included in toilet).
Dry solids content > 40 and < 80%.
Sludge treatment (bs1).
Sludge and/or biowaste (BS5 sum of BS1=BW1+B2, BS2, BS3, BS4).
Compost and gaseous emissions (BG2out, BSout).
(1) C:N ratio favourable (20 to 30) or corrected through addition of woodchips, addition neglected.
(2) No adding or recycling of bulking agents. (3) No leachate. (4) Emissions to air are assumed to be
mainly H2O, CO2, and NH3, reduced by use of condenser and filter.
average
18
15
95
4
0
0
0
99.99
min
13
max
24
6
73
In final product <0.25PFU/g ds Haug 1993, p.192, safe but risk is
regrowth
86% Haug 1993 p46, 35% Lasardi 2000 p220, 52% Trubetskaya
2001 p52
Remarks:
80
35
86
General info:
Temperature
Retention time
average
min
14
max
60
60
Costs:
- investment
(Euro/kg)
-O&M
(Euro/kg/y)
- life time (y)
average
0.052
min
max
Remarks:
CBS 2000, p.11
0.003
5%
10%
Low, estimate 5 to 10% of investment
Emissions:
CO2
CH4
average
min
max
average
0.02
min
0.01
max
0.03
Remarks:
CO2=(TSin*0.18-CH4)/ 12*(12+2*16), see assumption 4
CH4=(NH3/(14+3))/3*4*(12+4)*0.5, see assumption 4, 0.5 is due
to filter
NH3=Ntot-N,in*0.4/14*(14+3)*0.1, see assump. 4, 0.1 due to
condensor
Remarks:
Kärrman 2000, p.89, Björklund et al. 2000, p.47
Resources:
- energy:
(kWh/kg)
- nutrients
- space (m2/kg)
- water
Gray 1999, p.433, Tchobanoglous 1979, p.844, Crites 1998,
p.357+968
20
NH3
0.01
Recycled
Estimate: (m2)=(TS+H2O)in*0.6*10-3*32/2.5 (density 0.6 kg/l,
retention time average 32 days, height of piles estimated at 2.5 m)
-
Qualitative indicators41:
- acceptance
1
- adaptability
0
- expertise
0
- quality of space
-1
- institut. require.
0
- maintenance
0
- participation
0
- reliability
1
- robustness
0
- sust. behaviour
0
41
Remarks:
21% Haug 1993 p46, 13% Lasardi 2000 p220, 24% Trubetskaya
2001 p52
Rough estimate.
Rough estimate.
See below, 28%, >0.5 and << 15%, 13%, 54%, >20%, 6 to 73%.
Remarks:
Valuable product, natural process, low tech imago though.
Some expertise needed in operation (too wet, aeration, C/N-ratio, too cold, worms).
Space is lost, covers maybe necessary to educe smell.
Some participation possible in waste separation and use of final product.
Does not need electricity or water supply, possibly heated or aerated.
Sensitive to temperature and moisture content.
Waste separation may stimulate of visualise sustainable behaviour.
For the qualitative indicators, 1 indicates a potential advantage, 0 neutral, and -1 a potential disadvantage.
Sustainable Wastewater Treatment
Appendix 3: Technology data sheets
Estimating the removal of Water, Carbon and Nitrogen:
From the mass balance in Haug 1993 (see Figure 3.2) we find removal of water of 86% and a reduction of total
solids of 21%. Based on the measurements of Lasardi 2000 (see Table 3.3) we can calculate the total solids
removal based on the assumption that the P is inert (P-total unchanged), this gives us a s decrease of solids of
13% while an increase of 23% is measured (%ww). If we assume this difference due to water evaporation, the
water removal is 35%. Similar calculations can be done based on the measurements of Trubetskaya 2001 (see
also Table 3.3), giving a TS removal of 24% and a water removal of about 52%. In the vent gas carbon dioxide
was measured to be 0.2%, humidity was 95%, hydrogen sulphide was 0.5 ppm, ammonia 3 ppm, and methane 4
ppm (not detectable were sulphur dioxide, carbon monoxide, and methyl mercaptan) (CM 2000). From these
measurements, it becomes plausible that the main loss in TS is due to carbon losses. In the ORWARE model it is
assumed that 50 to 70% of the carbon is transformed into CO2, to trace amounts of methane (0.35% of CO2) and
to humus (Björklund et al. 2000, p.47).
Assuming, as done in the ORWARE model, that the major part of nitrogen in compost is bound to humus, while
the remainder is mineralised as NO3- and NH4+. Gaseous losses are estimated according the following
equation; N loss = 0.55903 − 0.01108 * (C / N ) in which Nloss is the lost fraction of the incoming nitrogen, C
is the incoming carbon, and N is the incoming nitrogen. The nitrogen loss is mainly NH3 (96% while 2% is N2O,
and 2% is N2) (Kärrman 2000, p.88, and Sonesson 1998, p.28). According to the formula, the nitrogen loss at a
C:N ratio of 25 is about 28%. According to Gotaas (in Jenkins 1994, on p.29) this would be much lower, namely
14.8% for a C:N ratio of 22 and 0.5% at a C:N ratio of 30. According to the measurements of Lasardi the Total
N as % of the dry weight is the same in the sludge straw mixture and final compost, however, as TS the TS
contents increases, the nitrogen loss can be estimated at 13%. Similar for the Trubetskaya measurements an
estimate of 54% N-total reduction can be estimated. Bengtsson et al (1996, p.44) estimates a nitrogen loss for
kitchen waste composting of 20%, however, in kitchen waste the nitrogen contents is significantly lower than in
mixed faeces, urine and biowaste.
Through a biofilter and a condenser, 90% of the nitrogen loss is returned to the compost, and 50% of the CH4 is
oxidised to CO2 (Kärrman 2000, p.88, and Sonesson 1998, p.28).
Energy:
Bengtsson et al. (1997, pp.42), estimate the energy use of liquid composter for the wastewater and kitchen waste
of 57 households, to be 84 kWh/(cap*y) for a continuous and 57kWh/(cap*y) for a batch process, including
grinding of the waste excluding 27 kWh/(cap*y) for vacuum transport of the waste. According to Kärrman 2000
(p.89), 0.015 MJ oil and 0.025 MJ electricity is consumed per kg wet weight compost in windrow composting.
Kärrman (2000, p.151) also mentions 335 MJ/(cap*y) for a liquid composting reactor and no oil use for windrow
composting. Björklund et al. (2000, p.47) report an energy use of 97 MJ/103 kg electricity and 5 MJ/103 kg diesel
(compost reactor with biofilter, without heat recovery, an ORWARE sub-model). The electricity demand for the
drum composter for kitchen waste used in the ORWARE model is 7.5 kWh/(cap*y) (Bengtsson et al. 1997,
p.44).
Sustainable Wastewater Treatment
Appendix 3: Technology data sheets
Technology Data Sheet 18: Constructed wetlands.
Description:
Scale:
Constraints:
Processing unit:
Incoming streams:
Outgoing streams:
Assumptions:
Treatment of wastewater in sub-surface flow wetland, which is a soil filter with reeds.
Different scales: single household (5 pe), neighbourhood (200 pe), and large scale (> 1000pe).
Blackwater has to be pre-treated to lower the solid content, (TS < 360 mg/l42).
On-site treatment (ost), blackwater treatment (bw3), and greywater treatment (gw2).
Pre-treated wastewater or greywater (BW1, BW4, GW3).
Treated water and accumulation of solids in and on the wetland (BW2, BW6, GWout).
(a) gaseous emissions neglected, (b) accumulated solids neglected.
5 pe
75
75
200 pe
90
90
>1,000 pe
99
99
70
40
30
80
80
98
10
average
-
90
50
40
90
90
99
10
min
98
80
60
95
99
99.9
10
max
0.3b, 3a
10b, 14a
5 pe
blackwater:
850
greywater:
200
17
200 pe
blackwater:
370
greywater:
225
5
> 1,000 pe
blackwater:
14
greywater:
1.5
0.00004
10
20
20
5 pe
5 pe
6
200 pe
> 1,000 pe
200 pe
1.5
> 1,000 pe
0.15
blackwater:
6
greywater:
2
-
blackwater:
3
greywater:
1
-
blackwater:
1
greywater:
0.5
-
Removal rate (%):
TS
TSS
BOD
Ntot-N
Ptot-P
Cu
Zn
FC
H2O
General info:
Temperature
Retention time
(days)
Costs:
- investment
(Euro/pe)
-O&M
(Euro/pe/y)
- life time (y)
Emissions:
CO2
CH4
NH3
Resources:
- energy:
(kWh/pe/y)
- nutrients
- space (m2/pe)
- water
Qualitative indicators:
- acceptance
- adaptability
- expertise
- quality of space
- institut. requir.
- maintenance
- participation
- reliability
- robustness
- sustain. behaviour
42
1
0
0
1
-1 / 0
0
0
1
0/1
1/0
Remarks: all vertical flow.
Rough estimate
Scierup 1990 in Haberl 1995, Billore 1999, Haberl
1995, Hartjes 1996
Reed et al. 1995, p.191, of which 16 % by plants
Jenssen et al. 1996 using porous media
Rough estimate no data.
Rough estimate only data for > 1,000 pe.
Buuren 1998, Green in Buuren 1998
Rough estimate, depends on climate.
Remarks:
(a) Reed et al. 1995, p.5, (b) Kampf et al. 1998
effluent polishing
Remarks:
Kampf et al. 1998 in US$, Rietland Brochure
2000, Jenssen 1996, Burkhard et al. 2001, Geldof
et al. 1997b, p.54, RIONED 1998b, p.32, 39.
Estimated, Burkhard 2001, Kampf et al. 1998 in
US$/m3
Remarks:
Neglected
Neglected
Neglected
Remarks:
Rough estimate, only data for 5 pe from RIONED
1998b, p.32, 39
Kampf et al. 1998 polishing, Cooper 1996, 1
m2/cap for BOD removal, 2 m2 additional for nitrification, Geldof et al. 1997b, p.54, Otterpohl 2000,
p.7, 11, RIONED 1998b, p.32, 39, Buuren 1998.
Remarks:
Natural treatment systems, widely used.
Once designed little to adapt.
Nice green area created.
Depends on scale.
Low: cutting reeds and checking on performance one in a while.
No energy or water supply required.
Larger systems resistant to shock loads.
Visualise treatment when onsite, encourage use of biological degradable products.
Average in medium strength wastewater is a TS content of about 720 mg/l (Tchobanoglous 1991, p.56), after
sedimentation about 50% is removed. Thus this constraint is set to require settled medium strength wastewater as
influent to the wetland.
Sustainable Wastewater Treatment
Appendix 3: Technology data sheets
Technology Data Sheet 19: Fixed bed reactor
Description:
Scale:
Constraints:
Processing unit:
Incoming streams:
Outgoing streams:
Assumptions:
Size (pe)
Removal rate (%):
TS
TSS
BOD
Ntot-N
Ptot-P
Cu
Zn
FC
H2O
General info:
Temperature
Retention time
Size (pe)
Costs:
- investment
(Euro/pe)
- O. & M
(Euro/pe/y)
- life time (y)
Size (pe)
Emissions:
CO2
CH4
NH3
Resources:
- energy:
(kWh/pe/y)
- nutrients
- space (m2/pe)
- water
Qualitative indicators:
- acceptance
- adaptability
- expertise
- quality of space
- institutional
requirements
- maintenance
- participation
- reliability
- robustness
- sustainable
behaviour
43
Attached growth systems of micro-organisms on filter bed.
5, 50 pe.
Medium strength settled wastewater as influent (TS < 360 mg/l43).
On-site treatment (ost) and blackwater treatment (bw2).
Mixed wastewater (BW1 or BW4)
Treated wastewater (BW2 or BW5), solids accumulate in the filter bed these are neglected.
(5) 50 pe system is more effective than 5 pe,
(6) Waste such as filter material and settled solids are neglected.
5
50
Remarks:
90
95
Estimated based on Geldof 1997b, 45-53 and RIONED 1998.
90
95
90
98
80
90
35
70
50
60
50
60
90
95
0
0
5
50
1,245
420
70
23
10
5
10
50
-
-
110
51
RIONED 1998
0.4
0.16
RIONED 1998
Remarks:
Estimated based on Geldof 1997b, 45-53 and RIONED 1998
Geldof 1997b, 45-53 only data on 5 pe, rough estimate for 50 pe (3:1 same as
investment costs).
Remarks:
1
0
0
0
-1
0
0
0
0
0
Remarks:
Combination of filter and biological treatment, regularly used, commonly accepted.
Decentralised difficult to control.
Mostly closed system, no real feed back on user, although it may stimulate use of
biodegradable cleaning agents.
Average in medium strength wastewater is a TS content of about 720 mg/l (Tchobanoglous 1991, p.56), after
sedimentation about 50% is removed. Thus this constraint is set to require settled medium strength wastewater as
influent to the wetland.
Sustainable Wastewater Treatment
Appendix 3: Technology data sheets
Technology Data Sheet 20: Membrane
44
Description:
Scale:
Constraints:
Processing unit:
Incoming streams:
Outgoing streams:
Assumptions:
Membrane filtration for disinfection and solid liquid separation as tertiary treatment.
Any scale.
Medium strength settled wastewater as influent (TS < 360 mg/l44).
Blackwater treatment (bw1 and bw4)
Mixed domestic wastewater (BW3, BW6).
Treated wastewater and concentrate (BW4, BS2 or BWout, BS4).
(1) Only separation no reactions occur (everything removed to sludge),
(2) No gaseous emissions.
Removal rate (%):
TS
TSS
BOD
Ntot-N
Ptot-P
Cu
Zn
FC
H2O
General info:
Temperature
Retention time
Disinfection + L/S
95
90b
83a
30
22a
50
50
100a
15
Remarks:(removed means lost in concentrate)
Estimated on TSS.
(b) STOWA 1998b, p.18 – MF/UF.
(a) Ban Aim 1993 MF-membrane
Roughly estimated.
Costs:
- investment
(Euro/m3)
- O & M (Euro/m3)
- life time (years)
Disinfection + L/S
135b
Remarks:
Emissions:
CO2
CH4
NH3
Resources:
- energy: (kWh/m3)
- nutrients
- space (m2/pe)
- water
Disinfection + L/S
0
0
0
Rough estimate – no data available.
Rough estimate – no data available.
Rough estimate, depends on influent, membrane, pressure.
Remarks:
0.3b
4.5
0.15c
0.02h
-
Qualitative indicators:
- acceptance
1
- adaptability
1
- expertise
0
- quality of space
- institutional
requirements
- maintenance
- participation
- reliability
- robustness
- sustainable
behaviour
-1
0
0
0
-1
1
0
Remarks:
Remarks:
(c) Broens 2001, p.14 dead end, cross flow several kWh/m3 for RO-units.
(h) Fane 1996, one tenth of conventional processes
Remarks:
High tech and simple to operate – may not be accepted as only barrier
against pathogens though.
Modular set-up allows adding or taking out of modules to adapt to
changing flows.
Expertise in needed in design and installation, operation can be selfcontrolled.
Space is lost.
Depends on scale.
Requires replacement of membranes once in while.
In greywater recycling reuse of water involves end-users.
Depend on energy and control equipment, high tech spare parts.
Effluent quality independent of influent.
Average in medium strength wastewater is a TS content of about 720 mg/l (Tchobanoglous 1991, p.56), after
sedimentation about 50% is removed. Thus this constraint is set to require settled medium strength wastewater as
influent to the wetland.
Sustainable Wastewater Treatment
Appendix 3: Technology data sheets
Technology Data Sheet 21: Membrane bioreactor
Description:
Scale:
Constraints:
Processing unit:
Incoming streams:
Outgoing streams:
Assumptions:
Integration of aerobic treatment with membrane filtration for solid-liquid separation.
Modular can go from household scale up to centralised treatment.
Medium strength settled wastewater as influent (TS < 360 mg/l45).
On-site treatment (ost), blackwater and greywater treatment (bw2, gw1).
Greywater or mixed wastewater (BW1, BW4, GW2).
Treated wastewater and sludge (BW2, BS1, BWout, BS4, GWout).
(3) Only separation no reactions occur (everything removed to sludge).
(4) No gaseous emissions.
Mixed ww
99
100
Greywater
99
100
97.5
90
70
50
50
100
98.5
85
72
50
50
100
H2O
General info:
Temperature
HRT (days)
SRT (days)
15
Mixed ww
0.21a, 8b
5a, 60-70b
5
Greywater
Costs:
- investment
(Euro/m3)
- O & M (Euro/m3)
- life time (y)
Mixed ww
140
Greywater
110
0.6
2
0.5
3
Emissions:
CO2
CH4
NH3
Resources:
- energy: (kWh/m3)
Mixed ww
-
Greywater
-
1.5f (0.15 –
2.4)
0.04h
-
0.2
Removal rate (%):
TS
TSS
BOD
Ntot-N
Ptot-P
Cu
Zn
FC
- nutrients
- space (m2/pe)
- water
1
- adaptability
- expertise
- quality of space
- institut. requir.
- maintenance
- participation
- reliability
- robustness
1
0
-1
0
0
0
-1
1
- sustainable
behaviour
0
45
Qualitative indicators:
- acceptance
0.08d, 1c
0.002h
-
Remarks:
Rough estimate based on TSS.
(a) Xing 2000 – activated sludge and UF, (g) Stephensons 2000,
p.162
(b) Kishino 1996, (d) Parameshwaran 1999
(e) Chiemchaisri 1992 in (d).
Rough estimate – no data available.
Rough estimate – no data available.
(a) Xing 2000, below detection limit, (c) Jefferson 2000 (7log), (d)
> 6log
Rough estimate, depends on influent, membrane, pressure.
Remarks:
(c) Jefferson 2000,
Remarks:
(f) STOWA 1998b, p.24, note only data for blackwater, greywater
estimated by taking lower values of blackwater.
Remarks:
Remarks:
note only data for blackwater, greywater estimated by taking lower
values of blackwater.
(h) Fane 1996, one tenth of conventional processes
Remarks:
High tech and simple to operate – may not be accepted as only barrier against
pathogens though.
Modular set-up allows adding or taking out of modules to adapt to changing flows.
Expertise in needed in design and installation, operation can be self-controlled.
Space is lost.
Requires replacement of membranes once in while.
In greywater recycling reuse of water involves end-users.
Highly dependent on electricity and control equipment.
Effluent quality independent of influent (higher rejection if influent of lower
quality)
Average in medium strength wastewater is a TS content of about 720 mg/l (Tchobanoglous 1991, p.56), after
sedimentation about 50% is removed. Thus this constraint is set to require settled medium strength wastewater as
influent to the wetland.
Sustainable Wastewater Treatment
Appendix 3: Technology data sheets
Technology Data Sheet 22: Rotating biological contactors
Description:
Scale:
Constraints:
Processing unit:
Incoming streams:
Outgoing streams:
Assumptions:
Size (pe)
Removal rate (%):
TS
TSS
BOD
Ntot-N
Ptot-P
Cu
Zn
FC
H2O
General info:
Temperature
Retention time
Size (pe)
Costs:
- investment
(Euro/pe)
- O. & M. (Euro/pe/y)
- life time (y)
Size (pe)
Emissions:
CO2
CH4
NH3
Resources:
- energy: (kWh/pe/y)
- nutrients
- space (m2/pe)
- water
Qualitative indicators:
- acceptance
- adaptability
- expertise
- quality of space
- institutional
requirements
- maintenance
- participation
- reliability
- robustness
- sustainable
behaviour
Rotating disk with micro organisms go through the wastewater and the air to obtain an aerated
biological treatment of the wastewater.
5, 50, and 400 pe.
On-site treatment (ost), black and greywater treatment (bw2, gw1).
Wastewater (BW1, BW4, GW2).
Treated wastewater (BW2, BW5, GW3).
(7) Gaseous emissions neglected,
(8) Accumulation of solids and biomass on the disks neglected.
5
50
400
Remarks:
75
80
90
Rough estimate no data.
80 (80a)
85 (80a)
95 (95-96b)
(a) Geldof 1997b, 45-53, (b) Bosman 1997
90 (>90a)
93 (>90a)
96 (95-97b)
40 (50a)
50 (50a)
60 (28-89b)
a
a
20 (< 50 )
20 (< 50 )
24 (13-24)
50
50
50
Geldof 1997b, 45-53
50
50
50
Geldof 1997b, 45-53
80
85
90
No data.
0
0
0
No data.
5
50
400
1000 (6001360)
18.2
10
5
415 (300-626)
300
4.5
10
50
0.03
20
400
-
-
-
70
26.5
10
0.7
0.3
0.1
-
-
-
1
0
0
0
-1
0
0
0
0
0
Remarks:
Alexandre 2000, Geenens 2000, RIONED
1998, Geldof 1997b, 45-53
Alexandre 2000, Geldof 1997b,45-53
Remarks:
Neglected
Neglected
Neglected
RIONED 1998, no data 400 pe thus
estimated.
RIONED 1998, no data 400 pe thus
estimated.
Remarks:
Generally used.
Some expertise needed in design and operation.
Decentralised and therefore probably difficult to control.
Requires energy supply.
Depends on size.
Sustainable Wastewater Treatment
Appendix 3: Technology data sheets
Technology Data Sheet 23:Sedimentation
Description:
Scale:
Constraints:
Processing unit:
Incoming streams:
Outgoing streams:
Assumptions:
Separation of liquid and solid phase through sedimentation.
Large scale (on household scale a septic tank can be used for sedimentation).
Blackwater treatment (bw1 and bw4).
Mixed wastewater (BW3, BW6).
Treated wastewater and sludge (BW4, BS2, BWout, BS4).
(1)
In precipitation chemicals are used to form flocs to enhance sedimentation, this results in a
larger sludge volume 34.5 ± 16 (Ødegaard 1992) or 15 to 23 % (STOWA 1998, p.40) and
higher costs. The amount of chemicals added ranges between 9 and 15 g/m3 (STOWA 1998,
p.40, 50) with additional costs of 0.006 Euro/g (STOWA 1998, p.205).
(2)
Size on surface loading: 1.5 to 3.0 m3/(m2*h) (STOWA 1998).
(3)
Assumed is that 50% of FC removed is inactivated through natural die-off rest to sludge.
Removal rate (%):
TS
TSS
Sedimentation
57 (50-64c)
55 (30-40b, 50 –
70a, 85d)
30 (24-40a, 20-30b,
25-33c)
10 (-10b, 9c)
10 (0-20b, 11e)
40
30
60
10
Precipitation
75
77 (60 – 80b, 75d,
91e)
44 (20-30b, 81e)
Sedimentation
1.5-2.5a-
Precipitation
Sedimentation
pre: 340b, {0.9}
post; 170, {0.45}
Precipitation
1,134b {3.15}
pre: 2b post: 1
11b + 0.006/m3,b
20
20
Sedimentation
Sedimentation
pre: 1.9b,
post: 1
Pre: 0.014,
post: 0.007
-
Precipitation
Precipitation
BOD
Ntot-N
Ptot-P
Cu
Zn
FC
H2O
General info:
Temperature
HRT (hours)
Costs:
- investment
(Euro/m2)
{Euro/pe}
-o&m
(Euro/m2/y)
- life time
Emissions:
CO2
CH4
NH3
Resources:
- energy:
(kWh/y/pe)
- nutrients
- space (m2/pe)
- water
Qualitative indicators:
- acceptance
- adaptability
- expertise
- quality of space
- instit requirements
- maintenance
- participation
- reliability
- robustness
- sustai. behaviour
1
-1
0
0
0
0
0
1
1
0
25 (20-30b, 28e)
78 (60-80b, 94e)
81e
50e
80
10
1.9b
0.014
Remarks:
(c) COST WG5 2000, no data precipitation.
(a) Crites 1998, p.300, (b) STOWA 1998 p.25, (d)
Kärrman 2000 p.83, (e) Ødegaard 1992, p.261, 263
No data pre sedimentation – rough estimate.
No data pre sedimentation – rough estimate.
Rough estimate, No data.
Rough estimate, (d) Kärrman 2000 p.83, 2.5% d.s in
sludge, (b) 0.5 – 1%
Remarks:
Process retards at lower temperatures.
(a) Crites 1998, p.309
Remarks:
(b) for tanks > 700m2, approx. 2.25 m3/m2/h, 0.15
m3/d/pe --> 252.000 pe
(b) precipitation in addition flocculator p.155
Remarks:
Remarks:
(b) rectangular tank 500 m2, approx. 180.000 pe
(b) pre 1.5 –3 after biological treatment: 0.7-1.5
m3/m2/h, 0.00625 m3/h/pe.
Remarks:
Simple and general practice treatment.
Once designed flexibility is limited.
Some in design.
Lost, although some ducks may make use of the tank.
Low in labour and maintenance.
Only requires electricity for sludge take out.
As long as not over loaded – little can disturb the process.
Sustainable Wastewater Treatment
Appendix 3: Technology data sheets
Technology Data Sheet 25: Trickling filter
Description:
Scale:
Constraints:
Processing unit:
Incoming:
Outgoing:
Assumptions:
Size (pe)
Removal (%):
TS
TSS
BOD
Ntot-N
Ptot-P
Cu
Zn
FC
H2O
General info:
Temperature
Retention time
Size (pe)
Costs:
- investment
(Euro/pe)
- O. & M.
(Euro/pe/y)
- life time
Size (pe)
Emissions:
CO2
CH4
NH3
Resources:
- energy:
(kWh/pe/y)
- nutrients
- space (m2/pe)
- water
Treatment through micro-organisms in a biofilm formed on a porous filter on which wastewater is
trickled. Aeration is passive.
5, 50, 400, and 1000 pe.
For high rate trickling filters pre-treated wastewater (TS < 360 mg/l46), else low rate filter.
On-site treatment (ost), black and greywater treatment (bw3, gw1).
Wastewater (BW1, BW5, or GW2).
Treated wastewater (BW2, BW6, GW3).
(1) The removal rates listed by Geldof et al. (1997b, p.45-53) for < 16 pe are used for the 5, 10 and 20
pe systems, (2) All literature data not specific for small-scale systems is used for systems of 50 pe and
larger, (3) Depth filter 1.8 m.
5
50
400
1,000
Remarks:
45 / 75
45 / 75
45 / 75
45 / 75
Rough estimate based on TSS, no data.
50 / 80
50 / 80
50 / 80
50 / 80
Geldof 1997b, p.45-53, Helmer 1997, p.58.
55 / 85
55 / 85
55 / 85
55 / 85
Crites 1998 p.485 low rate 40-70%, high rate 8090%.
30 / 50
30 / 50
40 / 60
40 / 60
Rough estimate, only data 50 for 5 pe system.
30 / 50
30 / 50
40 / 60
40 / 60
Rough estimate, only data 50 for 5 pe system.
30 / 50
30 / 50
40 / 60
40 / 60
Rough estimate, only data 50 for 5 pe system, and >
50 larger systems.
30 / 50
30 / 50
40 / 60
40 / 60
Rough estimate, only data 50 for 5 pe system, and >
50 for larger systems.
90 / 95
90 / 95
90 / 95
90 / 95
5
5
5
5
Rough estimate, some evaporation.
Remarks:
5
50
400
1,000
1,100
425
300
150
4.6
0.72
0.03
0.07
10
5
10
50
20
400
30
1,000
75
40
10
3.75
0.8
-
0.08
-
0.01
-
0.005
-
Qualitative indicators:
- acceptance
0
- adaptability
0
- expertise
0
- quality of space
0
- institution. req.
0
- maintenance
1
- participation
0
- reliability
1
- robustness
0
- sustain. behav.
0
46
Remarks:
RIONED 1998, p50-54, Geldof 1997b, p.45-53,
Geenens 2000, Alexandre 2000.
(a) Geldof 1997b, (d) Alexandre 2000.
Remarks:
Neglected.
Neglected.
Neglected.
Remarks:
RIONED 1998, p50-54, only 5 and 1,000 pe, rest
estimated.
RIONED 1998, p50-54, space= BODin* Qin/1.08.
Remarks:
Problems with smell and or insects may occur.
Depends on design
Low in expertise
Space is lost although can be built partly underground.
Depends on design – small decentralised or large scale centralised systems
Low in maintenance
Can operate without power.
Can cope with shock loads, low temperatures slows down the process.
Average in medium strength wastewater is a TS content of about 720 mg/l (Tchobanoglous 1991, p.56), after
sedimentation about 50% is removed. Thus this constraint is set to require settled medium strength wastewater as
influent to the wetland.
Sustainable Wastewater Treatment
Appendix 3: Technology data sheets
Technology Data Sheet 26:UV-disinfection of wastewater
Description:
Scale:
Constraints:
Processing unit:
Incoming streams:
Outgoing streams:
Assumptions:
Disinfection of treated wastewater with UV.
Large scale (>100 pe, for small scale UV-disinfection see TDS3).
Turbidity of the water has to be low (TSS < 30 mg/l).
Black and greywater treatment (bw4, gw2).
Treated wastewater (BW6, GW3).
Disinfected wastewater (BWout, GWout).
(1) No other changes to the water than die-off of pathogens.
Removal rate (%):
TS
TSS
BOD
Ntot-N
Ptot-P
Cu
Zn
FC
average
0
0
0
0
0
0
0
99.9
H2O
General info:
Temperature
Retention time
0
average
-
Costs:
- investment
(Euro/m3/d)
-O&M
(Euro/(m3/d)/y)
- life time
average
0.14
Emissions:
CO2
CH4
NH3
Resources:
- energy:
(kWh/m3/y)
- nutrients
- space (m2/system)
- water
average
average
1,600
Qualitative indicators:
- acceptance
- adaptability
- expertise
- quality of space
- institutional
requirements
- maintenance
- participation
- reliability
- robustness
- sustainable
behaviour
min
max
99
99.999
min
max
Remarks:
min
0.03
max
0.23
Remarks:
EPA 1999 (original data in US$, in 1999 US$
0.004
Remarks:
Serpieri et al. 2000 p.18 down to 1-2 CFU/100ml, carbon
filter adds bacteria to the water, Medema et al. 1996 2 to
5-log reduction for UV-disinfection.
Euro).
EPA 1999, 2.5 % of investment.
10
5
-
1
0
0
0
0
0
0
-1
0
0
min
max
Remarks:
min
max
Remarks:
Calculated based on Valenti 1997.
Rough estimate, Minimal – lamps in water flow.
Remarks:
Relatively easy to operate.
Some expertise is required – but can be a self controlling unit.
Decentralised approach may be difficult for institutions to control and inspect.
Little maintenance, cleaning and replacing bulbs and filter.
When reuse participation may be possible.
Depends on availability of bulbs and electricity.
Cannot handle turbidity, measures light intensity and stops if turbidity of
influent is too high.
Sustainable Wastewater Treatment
Appendix 3: Technology data sheets
Technology Data Sheet 27: Yellowwater storage.
Description:
Scale:
Constraints:
Processing unit:
Incoming streams:
Outgoing streams:
Assumptions:
Storage of yellowwater (urine) before using it as fertiliser (production).
Any scale
Yellowwater treatment (yw1).
Yellowwater (YW5).
Yellowwater (YWout)
(1) Storage no reactions or nitrogen loss.
(2) Assume storage period 6 months.
Removal rate (%):
TS
TSS
BOD
Ntot-N
Ptot-P
Cu
Zn
FC
H2O
General info:
Temperature
Retention time
average
0
0
0
0
0
0
0
99
0
average
-
min
max
Remarks:
min
max
Remarks:
Costs:
- investment
(Euro/m3)
-O&M
- life time (y)
average
100
min
150
max
50
Remarks:
Rough estimate, no data.
Emissions:
CO2
CH4
NH3
Resources:
- energy:
- nutrients
- space (m2)
average
min
max
0
0
0
average
min
max
0
(H2Oin/1000*182.5/1)
- water
Qualitative indicators:
- acceptance
- adaptability
- expertise
- quality of space
- institutional
requirements
- maintenance
- participation
- reliability
- robustness
- sustainable
behaviour
0
20
Remarks:
Remarks:
Half year (182.5 days), height tank 1 m, Maybe
underground.
0
0
0
0
0
0
Remarks:
Storage can be out of sight, with out smell.
Depends on design.
Little required – maybe NH3 losses, salt formation.
Space lost, possibly underground.
Depends on size.
0
0
1
0
0
Limited.
None.
Depends on design – no electricity required.
Only size is essential.
No feed back to end users in this stage.
Sustainable Wastewater Treatment
Appendix 4: Results
Appendix 4: Results
Table 1 gives an overview of the results by different solvers for the same solution space and data set. The
following pages give an overview of results for the two scenarios, nutrient recycling and water recycling, for the
first 6999 evaluations with the global solver. The overview is created using the m-files dws2003out and
SortResults.
Table 1: Systems for nutrient recycling selected by the different solvers within the 2
solution spaces defined, weighting fertiliser (100), soil conditioner (50), and waste (-100).
Solution space:
Data-set:
Evaluations:
min(f)=min(-objective)
max(f)=max(-objective)
number of systems with min(f)
place first min(f) in unsorted
solspace_nr1
data2003_nutrients
gcl
25920#
-82.59
99.98
120
1177
ga
584$
-82.59
97.18
124
215
mip
1500&
-82.59
99.98
11
18
choices
contained
in
The best domestic water systems found:
solspace_nr1
Household water
{0}
{0}
{0}
0
Rainwater for toilet,
{0}
{0}
{0}
0
washing & outdoor
x3 Rainwater for kitchen
{0}
{0}
{0}
0
& personal hygiene
x4 Water saving shower &
{0}
{0}
{0}
0
washing machine
x5 Rainwater system
{3}
{3}
{3}
3
x6 Disinfection unit
{4}
{4}
{4}
4
x7
Toilet
6
6
6
5,6,8
x8
On-site treatment
{6}
{6}
{6}
6
x9
Blackwater transport
1,2,3
1,2,3
1,2,3
2,3,4*
x10 Yellowwater transport
{3}
{3}
{3}
3
x11 Greywater transport
{2}
{2}
{2}
2*
x12 Black sludge transport
{2}
{2}
{2}
2
x13 Blackwater treatment 1
4
4
4
1,2,3,4
x14 Blackwater treatment 2
6
6
6
2,4,5,6,13,14
x15 Blackwater treatment 3
3
3
3
3,7,8
x16 Blackwater treatment 4
{7}
{7}
{7}
7
x17 Greywater treatment 1
9
9
9
1,2,4,8,9
x18 Greywater treatment 2
8
8
8
5,8
x19 Rainwater treatment
{3}
{3}
{3}
38
x20 Sludge treatment
{3}
{3}
{3}
3*
x21 Greywater splitter
1
1
1
0,1
x22 Rainwater splitter (R7)
{0}
{0}
{0}
0
x23 Rainwater splitter (R8)
{1}
{1}
{1}
1
x24 Yellowwater splitter
0
0
0
0,1
# 25920 is the number for combinations contained in this solution space the number of domestic water
systems is lower due to overruled choices in de model which the solver cannot see but the data are
corrected and this leads to some doubles in the solution set, $ the ga-solvers stopped after 20
generations without improvement, & the mip-solver is set to do 1500 evaluations, {0} the brackets
indicate that the lower and upper boundaries fix the x on this value, * the model can overrule the value.
x1
x2
9
4
4
0
0
4
4
1
1
1
1
-
2
0
0
-
3
1
1
-
4
0 (original choices)
0 (corrected for flags)
-
9
4
4
0
9
4
4
0
Of the 5 best domestic water systems found, this is nr.: 3:
f-value: -82.592
1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8
x0-vector:
0 0 0 0 3 4 6 6 3 3 1 2 4 6 3 7 9 8
x-vector:
0 0 0 0 3 4 6 6 3 3 2 2 4 6 3 7 9 8
flags:
0 - 0 - - - 0 0 0 - 1 - 1 0 - 0 - 0
Of the 5 best domestic water systems found, this is nr.: 4:
f-value: -82.592
1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8
x0-vector:
0 0 0 0 3 4 6 6 2 3 1 2 4 6 3 7 9 8
x-vector:
0 0 0 0 3 4 6 6 2 3 2 2 4 6 3 7 9 8
flags:
0 - 0 - - - 0 0 0 - 1 - 1 0 - 0 - 1
0
4
4
1
0
4
4
1
1
1
1
-
1
1
1
-
2
0
0
-
2
0
0
-
3
1
1
-
3
1
1
-
4
0 (original choices)
0 (corrected for flags)
-
4
0 (original choices)
0 (corrected for flags)
-
Of the 5 best domestic water systems found, this is nr.: 2:
f-value: -82.592
1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4
x0-vector:
0 0 0 0 3 4 6 6 3 3 1 2 4 6 3 7 9 8 4 4 1 0 1 0 (original choices)
x-vector:
0 0 0 0 3 4 6 6 3 3 2 2 4 6 3 7 9 8 4 4 1 0 1 0 (corrected for flags)
flags:
0 - 0 - - - 0 0 0 - 1 - 0 0 - 0 - 1 0 1 - - - -
Of the 5 best domestic water systems found, this is nr.: 1:
f-value: -82.592
1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8
x0-vector:
0 0 0 0 3 4 6 6 3 3 1 2 4 6 3 7 9 8
x-vector:
0 0 0 0 3 4 6 6 3 3 2 2 4 6 3 7 9 8
flags:
0 - 0 - - - 0 0 0 - 1 - 1 0 - 0 - 1
Number of EVALUATIONS: 6999 (based on size xtot)
Nutrient Recycling:
Output of dws2003out.m for sorted results of global solver (first 6999 evaluations):
>> clear all
>> load data2003_nutrients
>> save data2003
>> clear all
>> load solspace_ls1
>> save solspace
>> clear all
>> load Sorted_1_nr1
>> dws2003out
Sustainable Wastewater Treatment
9
4
4
0
0 1 2 3 4
4 1 0 1 0 (original choices)
4 1 0 1 0 (corrected for flags)
1 - - - -
1. SELECTED DOMESTIC WATER SYSTEM:
'1
cHW = 0 - no household water use'
'2
cRWa = 0 - no rainwater for toilet, wash.machine & outdoor'
'3
cRWb = 0 - no rainwater use in kitchen and for personal hygiene'
'4
cCON = 0 - no use of water conserving shower and washing machine'
'5
cRWS = 3 - no rainwater available or chosen not to use rainwater'
'6
cDis = 4 - no disinfection of rainwater and/or householdwater'
'7 cToilet = 6 - urine separation toilet'
'8
cOST = 6 - no on-site treatment'
'9
cBWT = 3 - vacuum sewerage'
'10 cYWT = 3 - no separate transport of yellowwater'
'11 cGWT = 2 - greyswater to blackwater, no transport'
'12
cBST = 2 - no transport of blacksludge'
'13 cBW1 = 4 - no treatment in bw1'
'14 cBW2 = 6 - anaerobic digestion (50,000 pe)'
'15 cBW3 = 3 - constructed wetland (1,000 pe)'
'16 cBW4 = 7 - no treatment in bw4'
'17 cGW1 = 9, SplitGW1=1 - greywater to blackwater'
'18 cGW2 = 8, SplitGW1=1 - greywater to blackwater'
'19 cRW1 = 4 - no treatment in rw1'
'20
cBS1 = 4, vBS1=1 - no treamt. (in 0 or wwtConstraint(1,24))'
'21 SplitGW1= 1 - all greywater to on-site treatment'
'22 SplitR7 = 0 - no rainwater to greywater (to infiltr. or backw)'
'23 SplitR8 = 1 - rainwater to infiltration'
'24 SplitYW2= 0 - yellowwater to greywater'
There are 5 optima selected (or the same optimum is found several times).
Enter the column number of the optima you want to see the details off: 1
Note that the details shown below are generated by loading data2003.mat and running
dws2003.mdl - if you used a different data-file during optimisation the results will differ from
the ones shown above!! To see the details of the other optima - start new run later (clear all
and run dws2003out)
Of the 5 best domestic water systems found, this is nr.: 5:
f-value: -82.592
1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8
x0-vector:
0 0 0 0 3 4 6 6 2 3 1 2 4 6 3 7 9 8
x-vector:
0 0 0 0 3 4 6 6 2 3 2 2 4 6 3 7 9 8
flags:
0 - 0 - - - 0 0 0 - 1 - 0 0 - 0 - 1
Appendix 4: Results (Nutrient Recycling)
Costs: (euro/cap/y)
Energy: (kWh/cap/y)
Space: (m2/cap)
Total:
W.Cons:
1.03e+003 0.00e+000
4.74e+005 0.00e+000
1.76e+004
2. SUSTAINABILITY INDICATORS:
Population: 200000 people
Total water use:
36.5 (m3/cap/y)
Drinking water:
36.5 (m3/cap/y)
Householdwater:
0
(m3/cap/y)
Rainwater:
0
(m3/cap/y)
Water:
7.47e+001
8.04e+000
1.76e+004
Toilet:
4.10e+001
0.00e+000
5.00e-001
Chosen x in the x-vector:
'5 cRWS = 3 - no rainwater available or chosen not to use rainwater'
'6
cDis = 4 - no disinfection of rainwater and/or householdwater'
'7 cToilet = 6 - urine separation toilet'
'8
cOST = 6 - no on-site treatment'
'9 cBWT = 3 - vacuum sewerage'
'14 cBW2 = 6 - anaerobic digestion (50,000 pe)'
'15 cBW3 = 3 - constructed wetland (1,000 pe)'
'16 cBW4 = 7 - no treatment in bw4'
'17 cGW1 = 9, SplitGW1=1 - greywater to blackwater'
'18 cGW2 = 8, SplitGW1=1 - greywater to blackwater'
Disinf:
0.00e+000
0.00e+000
0.00e+000
Rainwsys:
0.00e+000
0.00e+000
0.00e+000
Based on the lower and upper boundaries found in solspace.mat the following x variables are fixed:
x1 is always 0
x2 is always 1
x3 is always 1
x4 is always 1
x10 is always 3
x11 is always 2
x12 is always 2
x13 is always 4
x19 is always 3
x20 is always 3
x21 is always 0
x22 is always 1
x23 is always 0
x24 is always 1
Sustainable Wastewater Treatment
Transport:
8.40e+002
4.74e+005
0.00e+000
Treatment:
7.47e+001
-3.59e+001
1.40e+000
Appendix 4: Results (Nutrient Recycling)
Treatment total:
7.47e+001
-3.59e+001
1.40e+000
Costs: (euro/cap/y)
Energy: (kWh/cap/y)
Space: (m2/cap)
0
0
0
0
0
0
0
0
0
0
1
0
1
0
0
0
0
1
0
0
DISADVANTAGES
Acceptance:
Adaptability:
Expertise:
Quality of space:
Institutional req.:
Maintenance:
Participation:
Reliability:
Robustness:
Sustainable beh.:
ADVANTAGES:
Acceptance:
Adaptability:
Expertise:
Quality of space:
Institutional req.:
Maintenance:
Participation:
Reliability:
Robustness:
Sustainable behaviour:
2
1
0
0
0
0
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
0
0
0
0
0
0
1
0
Water:
OST:
0.00e+000
0.00e+000
0.00e+000
W.Cons:
ADVANTAGES:
Acceptance:
Adaptability:
Expertise:
Quality of space:
Institutional req.:
Maintenance:
Participation:
Reliability:
Robustness:
Sustainable beh.:
Total:
Sustainable Wastewater Treatment
0
0
0
0
0
0
0
0
0
0
BW1:
0.00e+000
0.00e+000
0.00e+000
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
0
1
Toilet:
1
0
0
0
1
0
0
0
0
0
BW2:
7.40e+001
-3.60e+001
4.00e-002
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Disinf:
1
0
0
1
0
0
0
1
1
0
BW3:
7.00e-001
1.50e-001
1.00e+000
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
BW4:
0.00e+000
0.00e+000
0.00e+000
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
GW1:
0.00e+000
0.00e+000
0.00e+000
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Rainwsys: Transport: Treatment:
0
0
0
0
0
0
0
0
0
0
GW2:
0.00e+000
0.00e+000
0.00e+000
0
0
0
0
0
0
0
0
0
0
BS1:
0.00e+000
0.00e+000
0.00e+000
0
0
0
0
0
0
0
0
0
0
RW1:
0.00e+000
0.00e+000
0.00e+000
Appendix 4: Results (Nutrient Recycling)
0
0
0
0
0
0
0
0
0
0
Treatment total:
OST:
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
BW1:
0
0
1
1
0
0
0
1
0
0
BW2:
0
0
0
0
0
0
0
0
0
0
BW3:
0
0
0
0
0
0
0
0
0
0
BW4:
0
0
0
0
0
0
0
0
0
0
GW1:
ADVANTAGES:
Acceptance:
Adaptability:
Expertise:
Quality of space:
2
1
0
0
7
7
7
7
0
0
0
0
0
0
0
0
3. NORMALISATION and WEIGHTING factors (note: weight can be + or -, in optimisation -1*objective is minimised)
Value:
Norm:
Weight:
In objective:
Drinking water:
3.65e+001
3.76e+001
0.00e+000
0.00e+000
Householdwater:
0.00e+000
3.76e+001
0.00e+000
0.00e+000
Rainwater:
0.00e+000
1.00e+000
0.00e+000
0.00e+000
Total water use:
3.65e+001
1.09e+000
0.00e+000
0.00e+000
Costs:
1.03e+003
3.31e+006
0.00e+000
0.00e+000
Energy:
4.74e+005
4.74e+005
0.00e+000
0.00e+000
Space:
1.76e+004
2.01e+004
0.00e+000
0.00e+000
REUSE OR WASTE?:
RESTRICTIONS for categorisation:
Landfill:
0.00e+000 (kg/cap/y)
TS: 1e+002 Cu: 110 Zn: 350 (mg/kg total weight and mg/kg TS)
Soil condit.r: 2.86e+003 (kg/cap/y)
TS: 5e+002 Cu: 110 Zn: 350 (mg/kg total weight and mg/kg TS)
Fertiliser:
2.24e+004 (kg/cap/y)
Cu: 70 Zn: 500 Ptot: 15
(mg/kg P and mg/kg total weight)
Irrigation:
0.00e+000 (kg/cap/y)
TSS: 2e+001 Cu: 0.2 Zn: 2
BOD: 10 FC: 1e+004 (mg/l and no/l)
Domestic reuse: 0.00e+000 (kg/cap/y) TSS: 1e+001 Cu: 2
FC: 2e+004 (mg/l and no/l)
Discharge:
0.00e+000 (kg/cap/y)
TSS: 35
BOD: 25 Ptot: 1
Ntot: 10
(mg/l)
Infiltration:
0.00e+000 (kg/cap/y)
TSS: 0.5
Cu: 0.01 Zn: 0.07 Ptot: 0.4
(mg/l)
CSO:
0.00e+000 (kg/cap/y)
(calculated as percentage of water through combined sewer)
Gas:
2.92e+000 (kg/cap/y)
(calculated in models for anaerobic treatment)
Waste:
2.16e+004 (kg/cap/y)
(not categorised as one above)
DISADVANTAGES
Acceptance:
Adaptability:
Expertise:
Quality of space:
Institutional req.:
Maintenance:
Participation:
Reliability:
Robustness:
Sustainable beh.:
Sustainable Wastewater Treatment
0
0
0
0
0
0
0
0
0
0
GW2:
0
0
0
0
0
0
0
0
0
0
BS1:
0
0
0
0
0
0
0
0
0
0
RW1:
Appendix 4: Results (Nutrient Recycling)
1
0
1
0
0
0
0
1
0
0
Value:
0
0
1
1
1
1
1.00e+000
1.20e+003
2.24e+004
2.36e+004
2.48e+004
1.00e+000
2.38e+004
4.94e+004
1.00e+000
2.92e+000
7
7
7
7
7
7
7
7
7
7
Norm:
7
7
7
7
7
7
0.00e+000
5.00e+001
1.00e+002
0.00e+000
0.00e+000
0.00e+000
0.00e+000
-1.00e+002
0.00e+000
0.00e+000
0
0
0
0
0
0
0
0
0
0
Weight:
0
0
0
0
0
0
0.00e+000
1.19e+002
9.99e+001
0.00e+000
0.00e+000
0.00e+000
0.00e+000
-4.37e+001
0.00e+000
0.00e+000
0
0
0
0
0
0
0
0
0
0
In objective:
0
0
0
0
0
0
OUT-IN:
TS (g/d)
TSS (g/d)
BOD (g/d)
0.00e+000
0.00e+000
0.00e+000
5. MASS BALANCE:
4. OBJECTIVE:
Objective: 1.756e+002 (normalised and weighted sum of sus. indicators - in optimisation -1*objective is minimised)
REUSE OR WASTE?:
Landfill:
0.00e+000
Soil conditioner:
2.86e+003
Fertiliser:
2.24e+004
Irrigation:
0.00e+000
Domestic reuse:
0.00e+000
Discharge:
0.00e+000
Infiltration:
0.00e+000
Waste:
2.16e+004
CSO:
0.00e+000
Gas:
2.92e+000
DISADVANTAGES
Acceptance:
Adaptability:
Expertise:
Quality of space:
Institutional req.:
Maintenance:
Participation:
Reliability:
Robustness:
Sustainable beh.:
Institutional req.:
Maintenance:
Participation:
Reliability:
Robustness:
Sustainable beh.:
Sustainable Wastewater Treatment
Appendix 4: Results (Nutrient Recycling)
Biowaste:
1.54e+007
6.17e+006
3.86e+006
6.49e+005
1.35e+005
1.89e+003
5.59e+003
0.00e+000
2.85e+007
0.00e+000
0.00e+000
0.00e+000
0.00e+000
0.00e+000
0.00e+000
Faeces:
9.79e+006
7.83e+006
2.63e+006
4.06e+005
1.08e+005
4.70e+002
2.15e+003
3.80e+013
3.16e+007
Urine:
1.08e+007
1.43e+005
1.20e+006
2.33e+006
2.12e+005
1.31e+001
6.14e+001
0.00e+000
2.31e+008
Contaminants: DrinkW:
4.53e+007
1.50e+002
9.51e+006
0.00e+000
8.85e+006
0.00e+000
2.94e+005
1.32e-001
3.90e+005
2.70e-002
5.68e+003
0.00e+000
2.64e+004
0.00e+000
6.33e+010
0.00e+000
0.00e+000
9.98e+004
HouseHW:
0.00e+000
0.00e+000
0.00e+000
0.00e+000
0.00e+000
0.00e+000
0.00e+000
0.00e+000
0.00e+000
RainW:
6.12e+007
1.02e+005
0.00e+000
4.90e+003
1.02e+002
0.00e+000
0.00e+000
1.02e+011
1.01e+010
Evap.+Cons.+Infil.:
9.90e+006
1.11e+006
1.08e+006
3.61e+004
4.78e+004
5.04e+002
2.40e+001
7.00e+009
2.91e+009
Appendix 4: Results (Nutrient Recycling)
6. SOLUTION SPACE Note: solspace is loaded to print this info.
DECISION VARIABLES and their BOUNDARIES:
Explanation:
Name: Choices:
(no) (boundaries) (explanation on choices)
1 householdwater
pHW =0
(0,0)
(0=optimise, 1=off, 2=on)
2 rainwater (t,w,o)
pRWa =2
(1,1)
(0=optimise, 1=off, 2=on)
3 rainwater (k,ph)
pRWb =2
(1,1)
(0=optimise, 1=off, 2=on)
4 water conservation pCONS =2
(1,1)
(0=optimise, 1=off, 2=on)
5 rainwater system
pRWS =1,2,0
(2)
(1,2)
(1=small, 2=large, 3=no)
6 disinfection
pDis =1,0,3,0
(2)
(2,3)
(1=RO, 2=sun, 3=UV, 4=no)
7 toilet
pToilet =0,0,0,0,0,6,0,8,0,10
(3)
(8,10)
(1=old,2=new,3=int.,4=low,5=vac.,6=u.sep.,7=urinal,8=c.lf,9=c.dry,10=sol)
OUT:
Biowaste:
BlackW:
YellowW:
GreyW:
RainW*:
CSO:
Sludge:
Compost:
Accuml.:
TS (g/d)
0.00e+000
4.26e+005
0.00e+000
0.00e+000
6.45e+007
0.00e+000
5.20e+007
0.00e+000
4.57e+007
TSS (g/d)
0.00e+000
6.31e+003
0.00e+000
0.00e+000
6.98e+005
0.00e+000
2.04e+007
0.00e+000
1.54e+006
BOD (g/d)
0.00e+000
1.20e+004
0.00e+000
0.00e+000
1.74e+005
0.00e+000
1.14e+007
0.00e+000
3.90e+006
TN (g/d)
0.00e+000
3.92e+005
0.00e+000
0.00e+000
4.11e+004
0.00e+000
1.31e+006
0.00e+000
1.93e+006
TP (g/d)
0.00e+000
1.58e+005
0.00e+000
0.00e+000
1.25e+004
0.00e+000
3.95e+005
0.00e+000
2.37e+005
Cu (g/d)
0.00e+000
1.10e+001
0.00e+000
0.00e+000
3.16e+003
0.00e+000
4.17e+003
0.00e+000
2.09e+002
Zn (g/d)
0.00e+000
3.95e+000
0.00e+000
0.00e+000
2.63e+004
0.00e+000
7.50e+003
0.00e+000
3.91e+002
FC (g/d)
0.00e+000
0.00e+000
0.00e+000
0.00e+000
1.02e+011
0.00e+000
3.84e+012
0.00e+000
3.42e+013
H2O (g/d)
0.00e+000
1.23e+010
0.00e+000
0.00e+000
1.18e+010
0.00e+000
1.51e+009
0.00e+000
1.91e+009
Note: to the outgoing stream rainwater can be larger than the incomming rainwater stream,as added are contaminants from the roof and water with
contaminants used outdoor (for garden irrigation, carwashing, etc.) is added.
IN:
TS (g/d)
TSS (g/d)
BOD (g/d)
TN (g/d)
TP (g/d)
Cu (g/d)
Zn (g/d)
FC (g/d)
H2O (g/d)
TN (g/d)
TP (g/d)
Cu (g/d)
Zn (g/d)
FC (g/d)
H2O (g/d)
Sustainable Wastewater Treatment
Explanation:
8 on-site treatment
9 bw transport
10 yw transport
11 gw transport
12 sludge transport
13 bw treatment 1
14 bw treatment 2
15 bw treatment 3
16 bw treatment 4
17 gw treatment 1
18 gw treatment 2
19 sludge treatment
20 storm/rainwater
21 gw direction
rw direction
22 rw direction
23 rw direction
24 yw direction
Name: Choices:
pOST =1,2,3,4,0,0
pBWT =0,2,0,4
pYWT =0,0,3
pGWT =0,2
pBST =0,2
pBW1 =0,0,0,4
pBW2 =1,0,0,0,5,0,0,0,0,0,11,0,0,14
pBW3 =1,0,0,4,0,0,0,8
pBW4 =0,0,0,4,0,6,7
pGW1 =1,0,0,0,0,0,0,0,9
pGW2 =0,0,0,4,0,0,7,8
pBS1 =0,0,3,0
pRW1 =0,0,3,0
pSplitGW1 =0
pSplitR =2
pSplitR7 =1
pSplitR8 =2
pSplitYW2 =1
Sustainable Wastewater Treatment
(no) (boundaries) (explanation on choices)
(4)
(1,4)
(1=AS5, 2=AD5, 3=membrane, 4=mbr, 5=septic tank, 6=no)
(2)
(3,4)
(1=combined sewer, 2=separate, 3=vacuum, 4=no)
(1)
(3,3)
(1=separate sewer, 2=truck, 3=none)
(1)
(2,2)
(1=separate sewer, 2=none)
(1)
(2,2)
(1=truck, 2=none)
(1)
(4,4)
(1=membrane, 2=pre-sedimentation, 3=precipitation, 4=no)
(4)
(11,14)
(1=AS50,400,1E3,1E6,5=AD400,5E5,7=FB5,50,9=mem,mbr,11=RBC5,50,400,14=no)
(3)
(6,8)
(1=CW5, 2=CW200, 3=CW1,000, 4=TF5, 5=TF50, 6=TF400, 7=TF1000, 8=no)
(3)
(5,7)
(1=soak away, 2=swales, 3=infil.field, 4=membrane, 5=post sed., 6=UV, 7=no)
(2)
(8,9)
(1=MBR, 2=RBC5, 3=RBC50, 4=RBC500, 5=TF5, 6=TF50, 7=TF400, 8=TF1000, 9=no)
(3)
(6,8)
(1=soak away, 2=swales, 3=infil.field, 4=CW5, 5=CW200, 6=CW>1000, 7=UV, 8=no)
(1)
(3,3)
(1=AD400, 2=AD50,000, 3=composting>5, 4=no)
(1)
(3,3)
(1=soak away, 2=swales & ditches, 3=infiltration field, 4=no)
(0,0)
(0=optimise, 1=to greywater treatment, 2=to on-site treatment)
(0=optimise, 1=to infiltration, 2=to greywater treatment, 3=to blackwater)
(1,1)
(0=optimise, 1=to greywater treatment, 2=other direction,)
(0,0)
(0=optimise, 1=to infiltration, 2=other direction)
(1,1)
(0=optimise, 1=to yellowwater treatment 2=to greywater treatment)
Appendix 4: Results (Nutrient Recycling)
Number of EVALUATIONS: 6999 (based on size xtot)
Finding levels in objective values...
1 The objective value f=Inf is found 5 times (column 1 to 5)
2 The objective value f=-82.59 is found 115 times (column 6 to 120)
3 The objective value f=-81.51 is found 240 times (column 121 to 360)
4 The objective value f=-7.50 is found 120 times (column 361 to 480)
5 The objective value f=-6.17 is found 120 times (column 481 to 600)
6 The objective value f=-5.70 is found 240 times (column 601 to 840)
7 The objective value f=2.39 is found 432 times (column 841 to 1272)
8 The objective value f=9.20 is found 432 times (column 1273 to 1704)
9 The objective value f=13.23 is found 120 times (column 1705 to 1824)
10 The objective value f=14.99 is found 120 times (column 1825 to 1944)
11 The objective value f=15.28 is found 168 times (column 1945 to 2112)
12 The objective value f=15.28 is found 240 times (column 2113 to 2352)
13 The objective value f=15.34 is found 24 times (column 2353 to 2376)
14 The objective value f=17.94 is found 120 times (column 2377 to 2496)
15 The objective value f=18.18 is found 240 times (column 2497 to 2736)
16 The objective value f=36.31 is found 29 times (column 2737 to 2765)
17 The objective value f=36.42 is found 240 times (column 2766 to 3005)
18 The objective value f=37.06 is found 120 times (column 3006 to 3125)
19 The objective value f=38.66 is found 60 times (column 3126 to 3185)
20 The objective value f=39.38 is found 120 times (column 3186 to 3305)
21 The objective value f=39.47 is found 240 times (column 3306 to 3545)
22 The objective value f=39.49 is found 6 times (column 3546 to 3551)
23 The objective value f=39.55 is found 6 times (column 3552 to 3557)
24 The objective value f=39.62 is found 6 times (column 3558 to 3563)
25 The objective value f=39.99 is found 12 times (column 3564 to 3575)
26 The objective value f=40.03 is found 12 times (column 3576 to 3587)
27 The objective value f=40.05 is found 12 times (column 3588 to 3599)
28 The objective value f=40.11 is found 12 times (column 3600 to 3611)
29 The objective value f=40.35 is found 12 times (column 3612 to 3623)
30 The objective value f=41.32 is found 12 times (column 3624 to 3635)
31 The objective value f=41.39 is found 12 times (column 3636 to 3647)
32 The objective value f=41.99 is found 102 times (column 3648 to 3749)
33 The objective value f=42.03 is found 12 times (column 3750 to 3761)
34 The objective value f=42.04 is found 6 times (column 3762 to 3767)
35 The objective value f=42.13 is found 12 times (column 3768 to 3779)
36 The objective value f=42.14 is found 6 times (column 3780 to 3785)
Nutrient Recycling:
Output of SortResults.m for sorted results of global solver (first 6999 evaluations):
Sustainable Wastewater Treatment
37 The objective value f=42.16 is found 6 times (column 3786 to 3791)
38 The objective value f=42.23 is found 6 times (column 3792 to 3797)
39 The objective value f=42.23 is found 12 times (column 3798 to 3809)
40 The objective value f=42.26 is found 6 times (column 3810 to 3815)
41 The objective value f=42.29 is found 12 times (column 3816 to 3827)
42 The objective value f=42.35 is found 6 times (column 3828 to 3833)
43 The objective value f=42.37 is found 24 times (column 3834 to 3857)
44 The objective value f=42.37 is found 6 times (column 3858 to 3863)
45 The objective value f=42.40 is found 12 times (column 3864 to 3875)
46 The objective value f=42.44 is found 24 times (column 3876 to 3899)
47 The objective value f=42.44 is found 6 times (column 3900 to 3905)
48 The objective value f=42.47 is found 24 times (column 3906 to 3929)
49 The objective value f=42.51 is found 6 times (column 3930 to 3935)
50 The objective value f=42.56 is found 24 times (column 3936 to 3959)
51 The objective value f=42.88 is found 12 times (column 3960 to 3971)
52 The objective value f=42.92 is found 12 times (column 3972 to 3983)
53 The objective value f=42.94 is found 12 times (column 3984 to 3995)
54 The objective value f=43.00 is found 12 times (column 3996 to 4007)
55 The objective value f=44.21 is found 12 times (column 4008 to 4019)
56 The objective value f=44.28 is found 12 times (column 4020 to 4031)
57 The objective value f=44.49 is found 18 times (column 4032 to 4049)
58 The objective value f=44.61 is found 18 times (column 4050 to 4067)
59 The objective value f=44.92 is found 9 times (column 4068 to 4076)
60 The objective value f=45.02 is found 9 times (column 4077 to 4085)
61 The objective value f=45.05 is found 6 times (column 4086 to 4091)
62 The objective value f=45.12 is found 9 times (column 4092 to 4100)
63 The objective value f=45.15 is found 6 times (column 4101 to 4106)
64 The objective value f=45.24 is found 6 times (column 4107 to 4112)
65 The objective value f=45.60 is found 18 times (column 4113 to 4130)
66 The objective value f=45.75 is found 18 times (column 4131 to 4148)
67 The objective value f=45.77 is found 12 times (column 4149 to 4160)
68 The objective value f=45.88 is found 18 times (column 4161 to 4178)
69 The objective value f=50.30 is found 162 times (column 4179 to 4340)
70 The objective value f=50.32 is found 162 times (column 4341 to 4502)
71 The objective value f=52.97 is found 162 times (column 4503 to 4664)
72 The objective value f=55.64 is found 162 times (column 4665 to 4826)
73 The objective value f=55.82 is found 162 times (column 4827 to 4988)
74 The objective value f=59.84 is found 162 times (column 4989 to 5150)
75 The objective value f=59.87 is found 162 times (column 5151 to 5312)
76 The objective value f=62.66 is found 162 times (column 5313 to 5474)
77 The objective value f=65.24 is found 36 times (column 5475 to 5510)
Appendix 4: Results (Nutrient Recycling)
78 The objective value f=65.49 is found 162 times (column 5511 to 5672)
79 The objective value f=65.71 is found 162 times (column 5673 to 5834)
80 The objective value f=65.73 is found 108 times (column 5835 to 5942)
81 The objective value f=65.90 is found 54 times (column 5943 to 5996)
82 The objective value f=68.31 is found 94 times (column 5997 to 6090)
83 The objective value f=68.50 is found 54 times (column 6091 to 6144)
84 The objective value f=88.84 is found 54 times (column 6145 to 6198)
85 The objective value f=89.95 is found 9 times (column 6199 to 6207)
86 The objective value f=89.96 is found 9 times (column 6208 to 6216)
87 The objective value f=90.05 is found 9 times (column 6217 to 6225)
88 The objective value f=90.06 is found 9 times (column 6226 to 6234)
89 The objective value f=90.07 is found 84 times (column 6235 to 6318)
90 The objective value f=90.14 is found 9 times (column 6319 to 6327)
91 The objective value f=90.16 is found 9 times (column 6328 to 6336)
92 The objective value f=90.28 is found 2 times (column 6337 to 6338)
93 The objective value f=90.35 is found 2 times (column 6339 to 6340)
94 The objective value f=90.38 is found 2 times (column 6341 to 6342)
95 The objective value f=90.47 is found 2 times (column 6343 to 6344)
96 The objective value f=90.63 is found 1 times (column 6345 to 6345)
97 The objective value f=91.38 is found 54 times (column 6346 to 6399)
98 The objective value f=91.89 is found 1 times (column 6400 to 6400)
99 The objective value f=91.97 is found 1 times (column 6401 to 6401)
100 The objective value f=92.10 is found 94 times (column 6402 to 6495)
101 The objective value f=92.40 is found 2 times (column 6496 to 6497)
102 The objective value f=92.46 is found 28 times (column 6498 to 6525)
103 The objective value f=92.52 is found 2 times (column 6526 to 6527)
104 The objective value f=92.61 is found 7 times (column 6528 to 6534)
105 The objective value f=92.71 is found 7 times (column 6535 to 6541)
106 The objective value f=92.80 is found 6 times (column 6542 to 6547)
107 The objective value f=93.51 is found 14 times (column 6548 to 6561)
108 The objective value f=93.53 is found 18 times (column 6562 to 6579)
109 The objective value f=93.66 is found 10 times (column 6580 to 6589)
110 The objective value f=93.67 is found 6 times (column 6590 to 6595)
111 The objective value f=93.68 is found 168 times (column 6596 to 6763)
112 The objective value f=93.80 is found 10 times (column 6764 to 6773)
113 The objective value f=93.81 is found 6 times (column 6774 to 6779)
114 The objective value f=93.98 is found 3 times (column 6780 to 6782)
115 The objective value f=94.00 is found 3 times (column 6783 to 6785)
116 The objective value f=94.08 is found 3 times (column 6786 to 6788)
117 The objective value f=94.10 is found 3 times (column 6789 to 6791)
118 The objective value f=94.10 is found 84 times (column 6792 to 6875)
Sustainable Wastewater Treatment
119 The objective value f=94.17 is found 3 times (column 6876 to 6878)
120 The objective value f=94.19 is found 3 times (column 6879 to 6881)
121 The objective value f=94.66 is found 1 times (column 6882 to 6882)
122 The objective value f=94.70 is found 1 times (column 6883 to 6883)
123 The objective value f=94.72 is found 1 times (column 6884 to 6884)
124 The objective value f=94.78 is found 1 times (column 6885 to 6885)
125 The objective value f=94.89 is found 5 times (column 6886 to 6890)
126 The objective value f=95.29 is found 3 times (column 6891 to 6893)
127 The objective value f=95.39 is found 3 times (column 6894 to 6896)
128 The objective value f=95.46 is found 3 times (column 6897 to 6899)
129 The objective value f=95.48 is found 3 times (column 6900 to 6902)
130 The objective value f=95.56 is found 3 times (column 6903 to 6905)
131 The objective value f=95.62 is found 2 times (column 6906 to 6907)
132 The objective value f=95.66 is found 3 times (column 6908 to 6910)
133 The objective value f=95.69 is found 2 times (column 6911 to 6912)
134 The objective value f=95.72 is found 2 times (column 6913 to 6914)
135 The objective value f=95.81 is found 2 times (column 6915 to 6916)
136 The objective value f=95.99 is found 1 times (column 6917 to 6917)
137 The objective value f=96.06 is found 1 times (column 6918 to 6918)
138 The objective value f=96.17 is found 6 times (column 6919 to 6924)
139 The objective value f=96.32 is found 6 times (column 6925 to 6930)
140 The objective value f=96.46 is found 6 times (column 6931 to 6936)
141 The objective value f=96.70 is found 3 times (column 6937 to 6939)
142 The objective value f=96.80 is found 3 times (column 6940 to 6942)
143 The objective value f=96.90 is found 3 times (column 6943 to 6945)
144 The objective value f=97.75 is found 2 times (column 6946 to 6947)
145 The objective value f=97.86 is found 2 times (column 6948 to 6949)
146 The objective value f=98.85 is found 10 times (column 6950 to 6959)
147 The objective value f=99.00 is found 2 times (column 6960 to 6961)
148 The objective value f=99.03 is found 6 times (column 6962 to 6967)
149 The objective value f=99.14 is found 2 times (column 6968 to 6969)
150 The objective value f=99.17 is found 6 times (column 6970 to 6975)
151 The objective value f=99.31 is found 6 times (column 6976 to 6981)
152 The objective value f=99.45 is found 3 times (column 6982 to 6984)
153 The objective value f=99.55 is found 3 times (column 6985 to 6987)
154 The objective value f=99.64 is found 3 times (column 6988 to 6990)
155 The objective value f=99.66 is found 3 times (column 6991 to 6993)
156 The objective value f=99.76 is found 3 times (column 6994 to 6996)
157 The objective value f=99.85 is found 3 times (column 6997 to 6999)
Appendix 4: Results (Nutrient Recycling)
3
4
6
6
2
3
This range contains 431 different combination of the x variables.
The values of the x-variables within this best level are:
x1 is always 0
x2 is always 1
x3 is always 1
x4 is always 1
x5 within the range (841 to 1272 datapoints) has the values: 3
x6 within the range (841 to 1272 datapoints) has the values: 4
x7 within the range (841 to 1272 datapoints) has the values: 8
x8 within the range (841 to 1272 datapoints) has the values: 6
x9 within the range (841 to 1272 datapoints) has the values: 4
x10 is always 3
x11 is always 2
Do you want to see x-plots of a range?
(type 0 for yes) 0
Give the starting point: 841
Give the ending point: 1272
This m-file offers 4 types of plots, namely:
(1) full plot or smaller plot of best data
(2) plot of best levels
(3) plot of certain range of data
(4) plot of f versus x
You can see all these plots if you want to.
When choosing plot range, the values of the x-variables and the min-max-values of the
sustainability indicators in this range are also shown.
The values of the x-variables within this best level are:
x2 is always 1
x3 is always 1
x4 is always 1
x5 within the best level (1 to 5 datapoints) has the values:
x6 within the best level (1 to 5 datapoints) has the values:
x7 within the best level (1 to 5 datapoints) has the values:
x8 within the best level (1 to 5 datapoints) has the values:
x9 within the best level (1 to 5 datapoints) has the values:
x10 is always 3
x11 is always 2
x12 is always 2
x13 is always 4
Sustainable Wastewater Treatment
ADVANTAGES:
Acceptance:
Adaptability:
Expertise:
1
2
1
1
2
1
The values of the sustainability indicators within this range:
Min:
Max:
Drinking water: 3.76e+001 3.76e+001
Householdwater: 0.00e+000 0.00e+000
Rainwater:
0.00e+000 0.00e+000
Total water use: 3.76e+001 3.76e+001
Costs:
9.91e+005 9.93e+005
Energy:
6.00e+003 6.00e+003
Space:
1.81e+004 1.81e+004
x1 is always 0
x14 within the best level (1 to 5 datapoints) has the values:
x15 within the best level (1 to 5 datapoints) has the values:
x16 within the best level (1 to 5 datapoints) has the values:
x17 within the best level (1 to 5 datapoints) has the values:
x18 within the best level (1 to 5 datapoints) has the values:
x19 is always 3
x20 is always 3
x21 is always 0
x22 is always 1
x23 is always 0
x24 is always 1
The sorted results are saved in Sorted.mat
x12 is always 2
x13 is always 4
x14 within the range (841 to 1272 datapoints) has the values:
x15 within the range (841 to 1272 datapoints) has the values:
x16 within the range (841 to 1272 datapoints) has the values:
x17 within the range (841 to 1272 datapoints) has the values:
x18 within the range (841 to 1272 datapoints) has the values:
x19 is always 3
x20 is always 3
x21 is always 0
x22 is always 1
x23 is always 0
x24 is always 1
14
8
7
1
5
6
3
7
9
8
Appendix 4: Results (Nutrient Recycling)
1
0
0
0
0
0
0
0
0
0
DISADVANTAGES
Acceptance:
Adaptability:
Expertise:
Quality of space:
Institutional req.:
Maintenance:
Participation:
Reliability:
Robustness:
Sustainable beh.:
0.00e+000
8.26e+002
1.52e+003
0.00e+000
0.00e+000
0.00e+000
2.25e+004
2.16e+004
0.00e+000
0.00e+000
1
0
0
0
0
0
0
0
0
0
Max:
1
1
1
1
1
2
1
The sorted range of the results and information on the levels of the objective value are
saved in SortedRange.mat
REUSE OR WASTE?:
Landfill:
0.00e+000
Soil conditioner: 8.26e+002
Fertiliser:
1.52e+003
Irrigation:
0.00e+000
Domestic reuse: 0.00e+000
Discharge:
0.00e+000
Infiltration:
2.25e+004
Waste:
2.16e+004
CSO:
0.00e+000
Gas:
0.00e+000
Min:
1
1
1
1
1
2
1
Quality of space:
Institutional req.:
Maintenance:
Participation:
Reliability:
Robustness:
Sustainable beh.:
Sustainable Wastewater Treatment
Appendix 4: Results (Nutrient Recycling)
3
0
0
-
3
0
0
-
Of the 5 best domestic water systems found, this is nr.: 3:
f-value: 47.217
1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2
x0-vector: 0 1 1 1 2 3 9 6 4 3 2 2 4 14 8 7 1 8 4 4 0 1
x-vector: 0 1 1 1 2 3 9 6 4 3 2 2 4 14 8 7 1 8 4 4 0 1
flags:
0 - 0 - - - 1 1 1 - 0 - 0 1 - 0 - 0 1 1 - -
Of the 5 best domestic water systems found, this is nr.: 4:
f-value: 47.217
1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9
x0-vector: 0 1 1 1 2 3 9 6 4 3 2 2 4 14 8 7 1 8 4
x-vector: 0 1 1 1 2 3 9 6 4 3 2 2 4 14 8 7 1 8 4
flags:
0 - 0 - - - 1 1 0 - 0 - 0 1 - 0 - 0 1
0
4
4
1
1 2
0 1
0 1
- -
3
0
0
-
1 2
0 1
0 1
- -
Of the 5 best domestic water systems found, this is nr.: 2:
f-value: 47.217
1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9
x0-vector: 0 1 1 1 2 3 9 6 4 3 2 2 4 14 8 7 1 8 4
x-vector: 0 1 1 1 2 3 9 6 4 3 2 2 4 14 8 7 1 8 4
flags:
0 - 0 - - - 1 1 1 - 0 - 0 1 - 0 - 0 1
0
4
4
1
3
0
0
-
Number of EVALUATIONS: 6999 (based on size xtot)
Of the 5 best domestic water systems found, this is nr.: 1:
f-value: 47.217
1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2
x0-vector: 0 1 1 1 2 3 9 6 4 3 2 2 4 14 8 7 1 8 4 4 0 1
x-vector:
0 1 1 1 2 3 9 6 4 3 2 2 4 14 8 7 1 8 4 4 0 1
flags:
0 - 0 - - - 1 1 1 - 0 - 0 1 - 0 - 0 1 1 - -
>> clear all
>> load data2003_water
>> save data2003
>> clear all
>> load solspace_ls1
>> save solspace
>> clear all
>> load Sorted_gcl_ls1
>> dws2003out
4
1 (original choices)
1 (corrected for flags)
-
4
1 (original choices)
1 (corrected for flags)
-
4
1 (original choices)
1 (corrected for flags)
-
4
1 (original choices)
1 (corrected for flags)
-
Life support Systems
Output of dws2003out.m for sorted results of global solver (first 6999 evaluations):
Sustainable Wastewater Treatment
0
4
4
1
1 2
0 1
0 1
- -
3
0
0
-
4
1 (original choices)
1 (corrected for flags)
-
1. SELECTED DOMESTIC WATER SYSTEM:
Explaining x-vector:
'1
cHW = 0 - no household water use'
'2
cRWa = 1 - rainwater use for toilet, wash.machine & outdoor'
'3
cRWb = 1 - rainwater use in kitchen and for personal hygiene'
'4
cCON = 1 - use of water conserving shower and washing machine'
'5
cRWS = 2 - large advanced rainwatersystem in use'
'6
cDis = 3 - disinfection by small scale UV-disinfection (k only)'
'7
cToilet = 9 - composting toilet dry (urine sepration possible)'
'8
cOST = 6 - no on-site treatment'
'9
cBWT = 4 - no blackwater no transport'
'10
cYWT = 3 - no separate transport of yellowwater'
'11
cGWT = 2 - no separate transport of greywater'
'12
cBST = 2 - no transport of blacksludge'
'13
cBW1 = 4 - vBW1=1 - no blackwater no treat.'
'14
cBW2 = 14, vBW2=1 - no blackwater no treat.'
'15
cBW3 = 8, vBW3=1 - no blackwater no transport'
'16
cBW4 = 7, vBW4=1 - no blackwater no transport'
'17
cGW1 = 1 - membrane bioreactor'
'18
cGW2 = 8 - no treatment in gw2'
'19
cRW1 = 4 - no treatment in rw1'
'20
cBS1 = 4, vBS1=1 - no treamt. (in 0 or wwtConstraint(1,24))'
'21 SplitGW1= 0 - dry toilet thus no blackwater line'
'22 SplitR7 = 1 - rainwater to greywater transport'
'23 SplitR8 = 0 - no rainwater to infiltration (to greyw or blackw)'
'24 SplitYW2= 1 - yellowwater to yellowwater treatment'
There are 5 optima selected (or the same optimum is found several times).
Enter the column number of the optima you want to see the details off: 1
Note that the details shown below are generated by loading data2003.mat and running
dws2003.mdl if you used a different data-file during optimisation the results will differ from
the ones shown above!! To see the details of the other optima - start new run later (clear all
and run dws2003out)
Of the 5 best domestic water systems found, this is nr.: 5:
f-value: 47.217
1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9
x0-vector: 0 1 1 1 2 3 9 6 4 3 2 2 4 14 8 7 1 8 4
x-vector: 0 1 1 1 2 3 9 6 4 3 2 2 4 14 8 7 1 8 4
flags:
0 - 0 - - - 1 1 0 - 0 - 0 0 - 0 - 0 1
Appendix 4: Results (Life Support Systems)
Costs: (euro/cap/y)
Energy: (kWh/cap/y)
Space: (m2/cap)
Total:
W.Cons: Water:
4.20e+005 0.00e+000 3.80e+001
3.83e+007 -2.00e+001 1.13e+000
2.49e+003
2.48e+003
2. SUSTAINABILITY INDICATORS:
Population:
200000
people
Total water use: 23.8
(m3/cap/y)
Drinking water: 5.15
(m3/cap/y)
Householdwater: 0
(m3/cap/y)
Rainwater:
18.6
(m3/cap/y)
Toilet:
1.75e+002
2.50e+001
3.00e-000
Chosen x in the x-vector:
'5 cRWS = 2 - large advanced rainwatersystem in use'
'6
cDis = 3 - disinfection by small scale UV-disinfection (k only)'
'7 cToilet = 9 - composting toilet dry (urine sepration possible)'
'8
cOST = 6 - no on-site treatment'
'9 cBWT = 4 - no blackwater no transport'
'14 cBW2 = 14, vBW2=1 - no blackwater no treat.'
'15 cBW3 = 8, vBW3=1 - no blackwater no transport'
'16 cBW4 = 7, vBW4=1 - no blackwater no transport'
'17 cGW1 = 1 - membrane bioreactor'
'18 cGW2 = 8 - no treatment in gw2'
Disinf:
1.77e+005
2.28e+001
0.00e+000
Rainwsys:
1.48e+002
3.83e+004
1.50e+000
Based on the lower and upper boundaries found in solspace.mat the following x variables are fixed:
x1 is always 0
x2 is always 1
x3 is always 1
x4 is always 1
x10 is always 3
x11 is always 2
x12 is always 2
x13 is always 4
x19 is always 3
x20 is always 3
x21 is always 0
x22 is always 1
x23 is always 0
x24 is always 1
Sustainable Wastewater Treatment
Transport:
0.00e+000
0.00e+000
0.00e+000
Treatment:
2.43e+005
1.30e+003
3.67e+001
Appendix 4: Results (Life Support Systems)
Treatment total:
2.43e+005
1.30e+003
3.67e+001
Costs: (euro/cap/y)
Energy: (kWh/cap/y)
Space: (m2/cap)
0
0
0
0
0
0
0
0
0
0
1
0
0
0
2
1
0
0
0
0
DISADVANTAGES
Acceptance:
Adaptability:
Expertise:
Quality of space:
Institutional req.:
Maintenance:
Participation:
Reliability:
Robustness:
Sustainable beh.:
ADVANTAGES:
Acceptance:
Adaptability:
Expertise:
Quality of space:
Institutional req.:
Maintenance:
Participation:
Reliability:
Robustness:
Sustainable behaviour:
3
1
0
0
0
0
3
2
2
3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
0
0
0
0
0
0
1
0
Water:
OST:
0.00e+000
0.00e+000
0.00e+000
W.Cons:
ADVANTAGES:
Acceptance:
Adaptability:
Expertise:
Quality of space:
Institutional req.:
Maintenance:
Participation:
Reliability:
Robustness:
Sustainable beh.:
Total:
Sustainable Wastewater Treatment
0
0
0
0
0
0
0
0
0
0
BW1:
0.00e+000
0.00e+000
0.00e+000
1
0
0
0
1
1
0
0
0
0
0
0
0
0
0
0
1
1
0
1
Toilet:
0
0
0
0
0
0
0
0
0
0
BW2:
0.00e+000
0.00e+000
0.00e+000
0
0
0
0
1
0
0
0
0
0
1
0
0
0
0
0
1
1
0
1
Disinf:
0
0
0
0
0
0
0
0
0
0
BW3:
0.00e+000
0.00e+000
0.00e+000
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
1
0
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
BW4:
0.00e+000
0.00e+000
0.00e+000
0
0
0
0
0
0
0
0
0
0
1
1
0
0
0
0
0
0
1
0
GW1:
2.41e+005
1.30e+003
2.00e-003
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Rainwsys: Transport: Treatment:
0
0
0
0
0
0
0
0
0
0
GW2:
0.00e+000
0.00e+000
0.00e+000
0
0
0
0
0
0
0
0
0
0
BS1:
0.00e+000
0.00e+000
0.00e+000
0
0
0
0
0
0
0
0
0
0
RW1:
0.00e+000
0.00e+000
0.00e+000
Appendix 4: Results (Life Support Systems)
0
0
0
0
0
0
0
0
0
0
Treatment total:
0
0
0
0
0
0
0
0
0
0
OST:
0
0
0
0
0
0
0
0
0
0
BW1:
0
0
0
0
0
0
0
0
0
0
BW2:
0
0
0
0
0
0
0
0
0
0
BW3:
0
0
0
0
0
0
0
0
0
0
BW4:
0
0
0
1
0
0
0
1
0
0
GW1:
3. NORMALISATION and WEIGHTING factors (note: weight can be + or -, in optimisation -1*objective is minimised)
Value:
Norm:
Weight:
In objective:
Drinking water: 5.15e+000
8.07e+000
-1.00e+002 -6.38e+001
Householdwater: 0.00e+000
1.00e+000
0.00e+000
0.00e+000
Rainwater:
1.86e+001
1.86e+001
0.00e+000
0.00e+000
Total water use: 2.38e+001
2.92e+000
0.00e+000
0.00e+000
Costs:
4.20e+005
2.94e+006
0.00e+000
0.00e+000
Energy:
3.96e+004
4.86e+007
0.00e+000
0.00e+000
Space:
2.49e+003
2.35e+004
0.00e+000
0.00e+000
REUSE OR WASTE?:
RESTRICTIONS for categorisation:
Landfill:
0.00e+000 (kg/cap/y)
TS: 1e+002 Cu: 110 Zn: 350 (mg/kg total weight and mg/kg TS)
Soil condit.: 6.94e+001 (kg/cap/y)
TS: 5e+002 Cu: 110 Zn: 350 (mg/kg total weight and mg/kg TS)
Fertiliser:
3.53e+002 (kg/cap/y)
Cu: 70 Zn: 500 Ptot: 15
(mg/kg P and mg/kg total weight)
Irrigation:
0.00e+000 (kg/cap/y)
TSS: 2e+001 Cu: 0.2 Zn: 2
BOD: 10 FC: 1e+004 (mg/l and no/l)
Domestic reuse 6.06e+003 (kg/cap/y)
TSS: 1e+001 Cu: 2
FC: 2e+004 (mg/l and no/l)
Discharge:
0.00e+000 (kg/cap/y)
TSS: 35
BOD: 25 Ptot: 1
Ntot: 10
(mg/l)
Infiltration:
0.00e+000 (kg/cap/y)
TSS: 0.5
Cu: 0.01 Zn: 0.07 Ptot: 0.4
(mg/l)
CSO:
0.00e+000 (kg/cap/y)
(calculated as percentage of water through combined sewer)
Gas:
0.00e+000 (kg/cap/y)
(calculated in models for anaerobic treatment)
Waste:
2.99e+003 (kg/cap/y)
(not categorised as one above)
DISADVANTAGES
Acceptance:
Adaptability:
Expertise:
Quality of space:
Institutional req.:
Maintenance:
Participation:
Reliability:
Robustness:
Sustainable beh.:
Sustainable Wastewater Treatment
0
0
0
0
0
0
0
0
0
0
GW2:
0
0
0
0
0
0
0
0
0
0
BS1:
0
0
0
0
0
0
0
0
0
0
RW1:
Appendix 4: Results (Life Support Systems)
1
0
0
0
2
1
0
0
0
0
DISADVANTAGES
Acceptance:
Adaptability:
Expertise:
Quality of space:
Institutional req.:
Maintenance:
Participation:
Reliability:
Robustness:
Sustainable beh.:
1.00e+000
2.68e+003
6.47e+002
1.00e+000
2.22e+004
1.00e+000
1.00e+000
2.81e+004
7.02e+002
4.38e+000
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
Norm:
0.00e+000
0.00e+000
0.00e+000
1.00e+002
1.00e+002
-1.00e+002
0.00e+000
-1.00e+002
0.00e+000
0.00e+000
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Weight:
0.00e+000
0.00e+000
0.00e+000
0.00e+000
2.72e+001
0.00e+000
0.00e+000
-1.06e+001
0.00e+000
0.00e+000
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
In objective:
4. OBJECTIVE:
Objective: -4.722e+001 (normalised and weighted sum of sus. indicators - in optimisation -1*objective is minimised)
REUSE OR WASTE?:
Landfill:
0.00e+000
Soil conditioner: 1.19e+002
Fertiliser:
3.53e+002
Irrigation:
0.00e+000
Domestic reuse: 6.06e+003
Discharge:
0.00e+000
Infiltration:
0.00e+000
Waste:
2.99e+003
CSO:
0.00e+000
Gas:
0.00e+000
3
1
0
0
0
0
3
2
2
3
ADVANTAGES:
Acceptance:
Adaptability:
Expertise:
Quality of space:
Institutional req.:
Maintenance:
Participation:
Reliability:
Robustness:
Sustainable beh.:
Value:
Sustainable Wastewater Treatment
Appendix 4: Results (Life Support Systems)
Biowaste:
0.00e+000
0.00e+000
0.00e+000
0.00e+000
0.00e+000
0.00e+000
0.00e+000
0.00e+000
0.00e+000
OUT:
TS (g/d)
TSS (g/d)
BOD (g/d)
TN (g/d)
TP (g/d)
Cu (g/d)
Zn (g/d)
FC (g/d)
H2O (g/d)
BlackW:
0.00e+000
0.00e+000
0.00e+000
0.00e+000
0.00e+000
0.00e+000
0.00e+000
0.00e+000
0.00e+000
Faeces:
9.79e+006
7.83e+006
2.63e+006
4.06e+005
1.08e+005
4.70e+002
Faeces:
2.15e+003
3.80e+013
3.16e+007
YellowW:
8.66e+006
1.14e+005
9.63e+005
1.87e+006
1.69e+005
1.05e+001
4.91e+001
0.00e+000
1.85e+008
Urine:
1.08e+007
1.43e+005
1.20e+006
2.33e+006
2.12e+005
1.31e+001
Urine:
6.14e+001
0.00e+000
2.31e+008
GreyW:
5.21e+005
0.00e+000
9.13e+004
2.87e+004
7.46e+004
0.00e+000
0.00e+000
0.00e+000
3.32e+009
RainW*:
3.34e+006
1.78e+005
1.72e+005
8.16e+003
8.44e+003
0.00e+000
0.00e+000
0.00e+000
1.63e+009
Contaminants: DrinkW:
4.53e+007
1.50e+002
9.51e+006
0.00e+000
8.85e+006
0.00e+000
2.94e+005
1.32e-001
3.90e+005
2.70e-002
5.68e+003
0.00e+000
Contaminants: DrinkW:
2.64e+004
0.00e+000
6.33e+010
0.00e+000
0.00e+000
9.98e+004
CSO:
0.00e+000
0.00e+000
0.00e+000
0.00e+000
0.00e+000
0.00e+000
0.00e+000
0.00e+000
0.00e+000
HouseHW:
0.00e+000
0.00e+000
0.00e+000
0.00e+000
0.00e+000
0.00e+000
HouseHW:
0.00e+000
0.00e+000
0.00e+000
Sludge:
0.00e+000
0.00e+000
0.00e+000
0.00e+000
0.00e+000
0.00e+000
0.00e+000
0.00e+000
0.00e+000
RainW:
6.12e+007
1.02e+005
0.00e+000
4.90e+003
1.02e+002
0.00e+000
RainW:
0.00e+000
1.02e+011
1.01e+010
Compost:
2.47e+007
1.40e+007
6.73e+006
1.52e+006
2.85e+005
2.36e+003
7.75e+003
1.90e+012
1.06e+007
Accuml.:
5.16e+007
6.38e+006
6.00e+006
1.62e+005
1.92e+005
0.00e+000
0.00e+000
3.62e+013
2.70e+008
Note: to the outgoing stream rainwater can be larger than the incomming rainwater stream,as added are contaminants from the roof and water with
ontaminants used outdoor (for garden irrigation, carwashing, etc.) is added.
Biowaste:
1.54e+007
6.17e+006
3.86e+006
6.49e+005
1.35e+005
1.89e+003
Biowaste:
5.59e+003
0.00e+000
2.85e+007
0.00e+000
0.00e+000
0.00e+000
0.00e+000
0.00e+000
0.00e+000
0.00e+000
0.00e+000
0.00e+000
IN:
TS (g/d)
TSS (g/d)
BOD (g/d)
TN (g/d)
TP (g/d)
Cu (g/d)
IN:
Zn (g/d)
FC (g/d)
H2O (g/d)
OUT-IN:
TS (g/d)
TSS (g/d)
BOD (g/d)
TN (g/d)
TP (g/d)
Cu (g/d)
Zn (g/d)
FC (g/d)
H2O (g/d)
5. MASS BALANCE:
Sustainable Wastewater Treatment
Evap.+Cons.+Infil.:
8.10e+006
1.13e+006
1.08e+006
3.38e+004
4.72e+004
0.00e+000
0.00e+000
8.35e+009
5.60e+008
Appendix 4: Results (Life Support Systems)
Explanation:
10 yw transport
11 gw transport
12 sludge transport
13 bw treatment 1
14 bw treatment 2
15 bw treatment 3
16 bw treatment 4
17 gw treatment 1
18 gw treatment 2
19 sludge treatment
20 storm/rainwater
Explanation:
21 gw direction
rw direction
22 rw direction
23 rw direction
24 yw direction
Name: Choices:
pYWT =0,0,3
pGWT =0,2
pBST =0,2
pBW1 =0,0,0,4
pBW2 =1,0,0,0,5,0,0,0,0,0,11,0,0,14
pBW3 =1,0,0,4,0,0,0,8
pBW4 =0,0,0,4,0,6,7
pGW1 =1,0,0,0,0,0,0,0,9
pGW2 =0,0,0,4,0,0,7,8
pBS1 =0,0,3,0
pRW1 =0,0,3,0
Name: Choices:
pSplitGW1 =0
pSplitR =2
pSplitR7 =1
pSplitR8 =2
pSplitYW2 =1
DECISION VARIABLES and their BOUNDARIES:
Explanation:
Name: Choices:
1 householdwater
pHW =0
2 rainwater (t,w,o)
pRWa =2
3 rainwater (k,ph)
pRWb =2
4 water conservation pCONS =2
5 rainwater system
pRWS =1,2,0
6 disinfection
pDis =1,0,3,0
7 toilet
pToilet =0,0,0,0,0,6,0,8,0,10
8 on-site treatment
pOST =1,2,3,4,0,0
9 bw transport
pBWT =0,2,0,4
(no)
(1)
(1)
(1)
(1)
(4)
(3)
(3)
(2)
(3)
(1)
(1)
(no)
(2)
(2)
(3)
(4)
(2)
(no)
6. SOLUTION SPACE Note: solspace is loaded to print this info.
Sustainable Wastewater Treatment
(boundaries) (explanation on choices)
(3,3) (1=separate sewer, 2=truck, 3=none)
(2,2) (1=separate sewer, 2=none)
(2,2) (1=truck, 2=none)
(4,4) (1=membrane, 2=pre-sedimentation, 3=precipitation, 4=no)
(11,14)
(1=AS50,400,1E3,1E6,5=AD400,5E5,7=FB5,50,9=mem,mbr,11=RBC5,50,400,14=no)
(6,8) (1=CW5, 2=CW200, 3=CW1,000, 4=TF5, 5=TF50, 6=TF400, 7=TF1000, 8=no)
(5,7) (1=soak away, 2=swales, 3=infil.field, 4=membrane, 5=post sed., 6=UV, 7=no)
(8,9) (1=MBR, 2=RBC5, 3=RBC50, 4=RBC500, 5=TF5, 6=TF50, 7=TF400, 8=TF1000, 9=no)
(6,8) (1=soak away, 2=swales, 3=infil.field, 4=CW5, 5=CW200, 6=CW>1000, 7=UV, 8=no)
(3,3) (1=AD400, 2=AD50,000, 3=composting>5, 4=no)
(3,3) (1=soak away, 2=swales & ditches, 3=infiltration field, 4=no)
(boundaries) (explanation on choices)
(0,0) (0=optimise, 1=to greywater treatment, 2=to on-site treatment)
(0=optimise, 1=to infiltration, 2=to greywater treatment, 3=to blackwater)
(1,1) (0=optimise, 1=to greywater treatment, 2=other direction,)
(0,0) (0=optimise, 1=to infiltration, 2=other direction)
(1,1) (0=optimise, 1=to yellowwater treatment 2=to greywater treatment)
(boundaries) (explanation on choices)
(0,0) (0=optimise, 1=off, 2=on)
(1,1) (0=optimise, 1=off, 2=on)
(1,1) (0=optimise, 1=off, 2=on)
(1,1) (0=optimise, 1=off, 2=on)
(1,2) (1=small, 2=large, 3=no)
(2,3) (1=RO, 2=sun, 3=UV, 4=no)
(8,10) (1=old,2=new,3=int.,4=low,5=vac.,6=u.sep.,7=urinal,8=c.lf,9=c.dry,10=sol)
(1,4) (1=AS5, 2=AD5, 3=membrane, 4=mbr, 5=septic tank, 6=no)
(3,4) (1=combined sewer, 2=separate, 3=vacuum, 4=no)
Appendix 4: Results (Life Support Systems)
Sustainable Wastewater Treatment
Curriculum Vitae
Curriculum Vitae
After graduating from high school and I chose to study Environmental Technology, at the
section Chemical Engineering of the Higher Technical School in Eindhoven (1987-1992).
While studying environmental engineering I got more and more interested in sustainable
development. Therefore, I decided to continue my studies at the Technology Management
Faculty of the Technical University Eindhoven where I chose International Technological
Development Studies (1992-1996). My MSc-project, "Industrial wastewater in Dar es Salaam,
the Vingunguti Waste Stabilisation Ponds", gave me the opportunity to live and work in
Tanzania. The different dynamics of this society and the different culture gave me a broader
view on life and sustainable development. After my study, I participated in the inspiring
workshop “Sustainable municipal wastewater treatment" which was organised by the
consultancies ETC and Waste. One of the conclusions of the workshop was that it would be
interesting to make a comparison on treatment technologies on many different aspects of
sustainability. I got the opportunity to make such a comparison within the framework of this
PhD-project, which was original titled "Small scale sustainable wastewater treatment" and
was a joint project of research groups Systems and Control at the faculty of Applied Physics
and Environment and Energy at the faculty of Technology Management (1997-2003). Besides
the research I enjoyed teaching, supervising MSc-students, and participating in international
conferences and the working groups. In a next occupation, I would like to contribute to the
implementation of sustainable water systems.
Annelies van der Vleuten-Balkma
Eindhoven, September 2003
Sustainable Wastewater Treatment
Samenvatting
Samenvatting
De schatting van de Wereld Gezondheidsorganisatie is dat anno 2003 ongeveer 18% van de
wereldbevolking geen toegang heeft tot veilig drinkwater en dat ongeveer 40% niet is
voorzien van hygiënische sanitatie. Dit heeft grote gevolgen voor de gezondheid en de
kwaliteit van het leven van deze mensen en is levensbedreigend voor kleine kinderen. Het
streven, vastgelegd in een doelstelling van de Verenigde Naties, is dan ook om in 2025 de
totale wereld bevolking te voorzien van veilig water en hygiënische sanitatie. Voor het
vervullen van deze services kan gekozen worden uit een grote variatie aan technologieën.
Toch blijkt dat de systemen die nu in de Westerse landen in gebruik zijn geen integrale
oplossing bieden, zo worden we nu geconfronteerd met problemen als eutrofiëring,
verdroging, en zware metalen in het zuiveringsslib. En dreigen er meer complexe problemen
als verminderde vruchtbaarheid door sporen van chemische stoffen en medicijnen in het
afvalwater en tekorten aan nutriënten in de landbouw door het ontregelen van de stikstof en
fosfaat kringlopen.
Hoe kunnen deze problemen het hoofd geboden worden? Cruciaal voor het vinden van een
oplossing is wellicht dat we ons moeten realiseren dat de problemen zo complex zijn dat er
niet één beste oplossing is maar er gezocht moet worden naar een verzameling van
oplossingen. Deze oplossingen moeten worden geformuleerd met een mondiale en lange
termijn visie zodat de oplossingen geen nieuwe problemen initiëren. In andere woorden, het
is van belang duurzame oplossingen te vinden die een weloverwogen balans bieden met
betrekking tot economische, ecologische, en sociaal-culturele kosten, zodat de bijdrage aan
lokale en mondiale problemen wordt geminimaliseerd of in ieder geval bekend is en kan
worden opgevangen.
Om inzicht te krijgen in de duurzaamheid van de verschillende huishoudelijke water
systemen, watergebruik en afvalwaterzuivering omvattend, hebben wij een
duurzaamheidanalyse ontwikkeld die is samengesteld uit bestaande methoden als kostenbatenanalyse, levenscyclusanalyse, en sociale analyses. De belangrijkste kenmerken die deze
analyse als geheel meerwaarde geven zijn; de brede visie, de set van indicatoren, de
ontwerpgerichte methode van modelleren die is gebaseerd op een superstructuur, en de multiobjective optimalisatie. Deze duurzaamheidanalyse hebben we geïmplementeerd in een
beslissingsondersteunend computermodel voor de selectie van duurzame huishoudelijke water
systemen. De drie belangrijkste onderdelen van de analyse zijn:
(1) Duurzaamheidindicatoren:
Gebaseerd op de verschillende dimensies van duurzaamheid is een set van
indicatoren samengesteld die bestaat uit economische, ecologische en sociaalculturele indicatoren. Daarbij zijn functionele indicatoren opgenomen die
technologie eigenschappen zoals bijvoorbeeld robuustheid en onderhoud
omvatten. De totale set bestaat uit 27 indicatoren waarvan sommige directe
uitkomsten van de massabalans zijn, bijvoorbeeld regenwatergebruik, terwijl
andere worden gecategoriseerd op basis van restricties, bijvoorbeeld gezuiverd
afvalwater met een kwaliteit die zo goed is dat het water in het huishouden kan
worden hergebruikt. Weer andere indicatoren worden niet berekend maar worden
meegenomen als kwalitatieve indicatoren die potentiële voor en nadelen van een
technologie aangeven, bijvoorbeeld de sociaal-culturele acceptatie van
verschillende toiletten.
Sustainable Wastewater Treatment
Samenvatting
(2) Model:
De duurzaamheidindicatoren worden berekend in een model dat de massabalans
van huishoudelijke watersystemen weergeeft. Dit model is geconstrueerd aan de
hand van een superstructuur die is opgebouwd uit de massabalansen van vele
verschillende systemen voor watervoorziening (drinkwater, huishoudwater, en
regenwater), water disinfectie, watergebruik, en verschillende stappen van
afvalwaterbehandeling variërend in schaalgrote van kleinschalige systemen per
huishouden tot grootschalige systemen waarmee hele stadsdelen kunnen worden
voorzien. De model structuur omvat 37 eenvoudige modellen van 13 verschillende
technologieën. De optimalisatie selecteert een aantal technologieën die samen een
compleet huishoudelijk watersysteem vormen.
(3) Optimalisatie
De selectie van duurzame systemen is gedefinieerd als een multi-objective integer
optimalisatie probleem. Verschillende objectives voor duurzame watersystemen
zijn bijvoorbeeld: het minimaliseren van kosten, watergebruik, energiegebruik, en
ruimtegebruik, en het maximaliseren van waardevolle producten als schoon water,
nutriënten en biogas en tevens het maximaliseren van de sociaal-culturele
inpassing door passende organisatie en wetgeving en groot gebruikersgemak. Om
deze soms tegenstrijdige doelstellingen te combineren in één doelstelling voor de
optimalisatie moeten de indicatoren die de verschillende doelstellingen
kwantificeren worden genormaliseerd en gewogen zodat deze kunnen worden
opgeteld.
Voor de optimalisering van huishoudelijke water systemen voor verschillende
scenario's hebben we verschillende oplossingsmethoden toegepast. Het was
moeilijk een oplossingsmethode te vinden die in een redelijk rekentijd het globale
optimum kan bepalen dit komt door het grote aantal mogelijkheden in het model,
er kunnen ongeveer 7*1012 verschillende systemen worden doorgerekend, en door
de discrete verandering van de duurzaamheidindicatoren. Daarom is er voor
gekozen voor de definitie van een kleinere oplossingsruimte waarin technologieën
worden geanalyseerd die speciaal voor het gekozen scenario interessant zijn.
Om duurzame huishoudelijke watersystemen te selecteren die kunnen bijdragen aan het
oplossen van de eerder genoemde waterproblematiek, zijn twee scenario's doorgerekend met
de beslissingsondersteunende software, namelijk: (1) het recyclen van nutriënten naar de
landbouw en het voorkomen van eutrofiering en (2) het minimaliseren van drinkwatergebruik
en het maximaliseren van waterrecycling. Voor beide scenario’s is vervolgens gezocht naar
betaalbare systemen omdat armoede een belangrijke rol speelt in de water problematiek.
Gebaseerd op de keuzes die worden gemaakt door de beslissingsondersteunende software
kunnen we concluderen dat technologieën die vandaag de dag niet veelvuldig worden
toegepast, zoals urine separatie en compost toiletten, membraan bioreactoren en regenwater
systemen belangrijke componenten kunnen zijn in toekomstige water systemen die zich
richten op de recycling van water en nutriënten. De meer conventionele systemen worden
door ons niet gevonden omdat wij ons richten op recycling waar veel andere onderzoekers
zich richten op de optimalisatie van bestaande systemen. Wat ook een rol kan spelen is dat
technologieën voor slibverwerking in het model beperkt zijn. Daarom moeten we stellen dat
als schoon slib geproduceerd kan worden of als slib op alternatieve wijze kan worden
hergebruikt dat dan meer conventionele oplossingen wellicht ook veelbelovend kunnen zijn.
Maar afgaande op de resultaten van de huidige berekeningen, komen wij tot de conclusie dat,
Sustainable Wastewater Treatment
Samenvatting
als toekomstige huishoudelijke watersystemen zich gaan richten op de recycling van water en
nutriënten, conventionele systemen zullen moeten worden vervangen door alternatieven die in
staat zijn afvalwaterstromen aan de bron te scheiden en geschikt te maken voor hergebruik.
Hergebruik van water zal kleinschalige decentrale systemen introduceren voor
regenwatergebruik en grijswater-hergebuik en wellicht ook waterzuinige of zelfs droge
toiletten en de disinfectie van drinkwater in het huishouden. Voor het recyclen van nutriënten
is decentralisatie minder noodzakelijk maar systemen als urine separatie en waterzuinige
toiletten zullen dit wel bevorderen.
Betaalbare systemen voor drinkwaterbesparing en nutriënten recycling zijn voor handen maar
waterhergebruik is duur door de keuze van een membraan bioreactor. Verder onderzoek moet
uitwijzen of het realistisch is data als ingaande stromen, zuiveringsrendementen of restricties
aan te passen zodat de beslissingsondersteunende software kiest voor goedkopere
technologieën als bijvoorbeeld een helofyten filter in plaats van de membraan bioreactor.
Omdat waterbesparing en recycling gecombineerd kunnen worden met het recyclen van
nutriënten is het mogelijk kleinschalige systemen te ontwikkelen die beide kringlopen op
kleine schaal sluiten, de zogenoemde "life support systems".