Batch Heat Exchanger Network: Minimum Units Targeting & Evolution

Available online at www.sciencedirect.com
Applied Thermal Engineering 28 (2008) 2089–2099
www.elsevier.com/locate/apthermeng
Minimum units targeting and network evolution for batch
heat exchanger network
Dominic Chwan Yee Foo a,*, Yin Hoon Chew b, Chew Tin Lee b
a
School of Chemical and Environmental Engineering, University of Nottingham Malaysia, Broga Road, 43500 Semenyih, Selangor, Malaysia
b
Bioprocess Engineering Department, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia
Received 17 September 2007; accepted 8 February 2008
Available online 19 February 2008
Abstract
Process integration techniques have been widely accepted as an effective tool in synthesising a cost optimal heat exchanger network
(HEN). However, most works on HEN synthesis have mainly focused on continuous processes. Much less work has been carried out for
the synthesis of batch HEN. This paper extends the minimum units targeting and network evolution techniques that were developed for
batch mass exchange network (MEN) into batch HEN. Two examples are used to illustrate the developed method.
Ó 2008 Elsevier Ltd. All rights reserved.
Keywords: Batch processes; Heat integration; Pinch analysis; Network evolution
1. Introduction
Systematic approach in addressing energy integration in
a process plant began during the global energy crisis in the
1970s. Since then, pinch analysis has been widely accepted
as an effective tool in synthesising a cost optimal heat
exchanger network (HEN) [1]. With pinch analysis, various
network targets (energy, heat exchanger area, capital cost,
etc.) may be obtained prior to the detailed design of HEN.
Towards the late 1980s, the works on HEN synthesis have
become rather well established. Other aspects of heat integration have also been reported, e.g. utility design, heatintegrated distillation, total site integration, etc. A few
good reviews on the well established heat integration techniques are available in reviews [2,3] and textbook [4]. It
should also be noted that many of these techniques have
now mainly incorporated into processes, design textbooks
[5–7]. However, most of the works on HEN synthesis have
mainly focused on continuous processes, while much less
attention has been given for the synthesis of batch HEN.
*
Corresponding author. Tel.: +60 (3) 89248130; fax: +60 (3) 89248017.
E-mail address: [email protected] (D.C.Y. Foo).
1359-4311/$ - see front matter Ó 2008 Elsevier Ltd. All rights reserved.
doi:10.1016/j.applthermaleng.2008.02.006
The earliest works addressing energy integration for
batch processes was reported by Vaselenak et al. [8]. These
authors worked on the heat recovery between vessels where
temperatures vary during operations. They presented a
heuristic rule for the co-current heat exchange and a
mixed-integer linear programming (MILP) solution for
the restricted target temperature. However, these authors
did not consider the time dependence of streams in which
some streams may only exist in the plant for a certain period of time. The opportunity for rescheduling and the generation of rescheduling superstructures were investigated in
a later work [9].
On the other hand, heat integration for batch processes
was investigated by other researchers based on the insightbased pinch analysis techniques. One of the earliest works
to divert pinch analysis from continuous to batch heat integration was reported by Kemp and Macdonald [10,11].
They developed a targeting tool called the time-dependent
heat cascade analysis (TDHCA) technique that allows the
minimum energy and heat storage targets to be identified
for a maximum energy recovery (MER) network. These
authors also reported a systematic technique for batch
HEN design and identified rescheduling opportunities in
their later works [12–15].
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D.C.Y. Foo et al. / Applied Thermal Engineering 28 (2008) 2089–2099
Concurrently, Linnhoff and his co-workers [16,17] individually developed a time average model based on the conventional approach of HEN synthesis in continuous mode
[1]. They also developed a time slice model to obtain the
energy targets for the batch HEN. However, the time slice
model is confined to the use of a simple scheduling diagram. No representation of which streams were thermodynamically capable of exchanging heat was obtained. In a
later work, Obeng and Ashton [18] proved that by
appropriate scheduling (as proposed by Kemp and Deakin
[13]), the utility targets obtained via the TDHCA [10–12]
will approach that of time average model [16,17]. Yet
another graphical approach to locate the minimum energy
targets for a batch HEN is later developed by Wang and
Smith [19], which treats time as the primary constraint
and the heat transfer driving force as the secondary constraint. Most of the early developments were reported in
a European Communities report [20]. Some successful
industrial applications have also been reported [21].
Later work on insight-based approaches focused on the
design of heat storage system for indirect heat recovery.
Stoltze et al. [22,23] developed an algorithm to determine
the number of storage tanks to achieve the established
energy targets. Sadr-Kazemi and Polley [24] reported that
in some batch processes, heat storage might provide a more
flexible alternative compared to direct integration. They
suggested that external heating or cooling could lead to a
higher plant throughput. The authors also examined the
effect of process rescheduling on the reduction of heat storage capacity [24,25]. Jung et al. [26] noted that heat storage
system possesses some practical operation issues (e.g. heat
losses, etc.), which in turn proposed a systematic rescheduling procedure to reduce indirect heat transfer (and hence
heat storage size) by maximising direct heat integration.
The rescheduling option also leads to increased production
yield [26]. Krummenacher and Favrat [27] later developed
a graphical-aided technique to locate the minimum number
of heat storage tanks by assuming vertical heat transfer.
More recently, Pourali et al. [28] proposed a time decomposition strategy to evaluate the combination of different time
intervals to achieve the minimum total cost for a batch
HEN, which include direct and indirect integration. While
insight-based approaches offer good insights into the overall analysis of the HEN problems, its main limitation lies in
its ability to analyse problems with more than a single
dimension. For instance, analysis can hardly be performed
for simultaneous heat integration and process scheduling.
Most often, these steps have to be done sequentially. However, the main advantage of being able to decompose a
complex problem for ease of analysis is always welcomed
by engineers in solving various industrial problems.
Other efforts in developing the heat integration schemes
based on mathematical-based optimisation approaches
have been reported. Papageorgiou et al. [29] developed a
mathematical framework that considers trade-offs between
heat integration and scheduling constraints. Lee and
Reklaitis [30,31] developed scheduling models for maximis-
ing heat recovery in cyclically operated single-product processes and across independently operated batch production
line. Zhao et al. [32] presented a mathematical formulation
for batch process scheduling based on the cascade analysis
of Kemp and Deakin [12], which involves heat integration
without intermediate storage. A three-step procedure was
proposed for the design of HEN for batch and semi-continuous processes in their later work [33]. Yet another work
that incorporates heat integration in the framework of
scheduling was reported by Adonyi et al. [34] that utilised
the S-graph approach. Corominas and co-workers [35–37]
addressed energy and waste minimisation in multi-product
batch processes. Vaklieva-Bancheva and co-workers
[38,39] developed a MILP solution for HEN design for
multipurpose batch plants in which only direct heat
exchange is considered. The same authors reported a case
study on heat integration for an antibiotics batch manufacturing process [40]. Bozan et al. [41] later developed a computer programme to simultaneously determine the product
campaigns, heat exchange areas as well as the overall heat
exchanger network for a multipurpose batch process.
Other works on heat integration for multipurpose batch
plants were also reported [42–44]. More recently, Puigjaner
[45] reported the first attempt to incorporate combined
heat and power scheme for batch processes. While mathematical optimisation approaches offer the flexibility of
incorporating other considerations (e.g. scheduling, multiproduct plant, etc.), their main disadvantage lies in the limitation in allowing designers to incorporate their decision
making view during the network synthesis stage.
From the above review, it is noticed that most of the
work for batch HEN synthesis has been mainly focusing
on energy targeting (insight-based pinch analysis technique), network synthesis, scheduling/rescheduling and
heat storage systems. A clear research gap has been the
identification of minimum number of heat exchanger units
within a batch HEN. Even though this is common knowledge for HEN in continuous mode, its establishment in
batch HEN synthesis problem has not been noticed. A flexible and simple HEN has always been required by batch
processes that have frequent product changeover. Besides
reducing pipework, maintenance and instrumentation
costs, fewer heat exchange units always lead to ease of
operation, which is one of the essential elements for
steady-state batch operation (the reverse is true for
unsteady-state system). Hence, during network evolution
stage, the minimum unit target serves as an important
lower bound before a batch network is simplified. Even
though the early work on network evolution for batch
HEN was reported by Kemp and Deakin [13], the evolution has not been made based on the minimum number
of units target. Further more, the approach does not take
into account of common heat exchangers that are used in
several time intervals. As will be shown in this paper that,
ignoring these common heat exchangers may lead to suboptimal results (i.e. more than the minimum units) during
network evolution.
D.C.Y. Foo et al. / Applied Thermal Engineering 28 (2008) 2089–2099
On the other hand, the establishment of systematic techniques to synthesise mass exchange network (MEN) for
batch processes has been reported lately. Following the
analogy of batch HEN approaches [10–13], Foo et al.
[46] developed a time-dependant cascading analysis technique to locate the minimum flowrates of the process and
external mass separating agents in a batch MEN. This is
the seminal work on extending the well established MEN
techniques for continuous processes [47–49] into batch
MEN problem. In a later work, targeting of minimum
number of mass exchanger units as well as network evolution techniques for a batch MEN were also established [50].
Following the same analogy of HEN and MEN synthesis,
the minimum unit targeting technique is readily extended
into batch HEN problems. This will be demonstrated in
the following hypothetical example.
2. Hypothetical example
Table 1 shows the stream data for a hypothetical example with a process cycle time of 10 h. As shown, there are
two hot streams (to be cooled) and one cold stream (to
be heated) that exist in different time zone, with their
respective supply (TS) and target (TT) temperatures. Hot
stream H1 exists in the first six hours while H2 exists
between hours 4 and 10. The cold stream C3 on the other
hand, exists between 2.5 and 9 h. The enthalpy rate (DH),
total enthalpy (DHDt) and heat capacity flowrate (CP)
are each streams are also given. The first step in pinch analysis study for a batch network is to locate the individual
process streams in their respective time intervals [10–13],
such as that shown in Table 2. The boundary of the time
intervals were set based on the start (tST) and end time
(tEND) of the hot and cold streams.
Next, energy targeting is carried out for the non-heat
storage system for the example, following the TDHCA
technique of Kemp and co-workers [10–12]. The energy
targeting result is shown in Table 3. As shown, the DHDt
column in each time interval represents the enthalpy
available in each temperature interval while the Cum.
Table 1
Stream data for hypothetical example
Streams
TS
(°C)
TT
(°C)
tST
(h)
tEND
(h)
DH
(kW)
DHDt
(kWh)
CP
(kW/K)
H1
H2
C3
180
150
20
20
10
120
0
4
2.5
6
10
9
640
420
1000
3840
2520
6500
4
3
10
Table 2
Existence of hot and cold streams in respective time intervals
0–2.5 h
2.5–4.0 h
4.0–6.0 h
6.0–9.0 h
9.0–10.0 h
H1
H1
H1
H2
C3
H2
C3
H2
C3
2091
DHDt column represents the heat cascade throughout the
interval. Summing the values in the first (175 °C) and last
row (5 °C) of Table 3 gives the total hot and cold energy
targets respectively for the overall network, i.e. 2600
(QH,min) and 2460 kWh (QC,min).
3. Minimum units targeting
For HEN in continuous mode, the minimum number of
units U is related to the total number of streams, given as
[1,4–7]
U ¼ N H þ N C N SN
ð1Þ
where NH is the number of hot streams in the system
(including hot utility); NC, the number of cold streams
(including cold utility), and NSN is the number of independent sub-networks into which the original network can be
sub-divided. Due to the existence of the pinch temperature
which divides the problem into two thermodynamic regions, Eq. (1) is applied separately in the region above
and below pinch to yield the minimum heat exchanger
units in these regions [1,4–7]. On the other hand, due to
the existence of different time intervals in a batch HEN
problem, Eq. (1) is applied for each of the time interval k
[50]
U k ¼ N H;k þ N C;k N SN;k
ð2Þ
By applying Eq. (2), the minimum heat exchangers in
example problem is found to be 12 units (Table 4). Note
that the calculation for number of units will have to include
the hot (HU) and cold (CU) utilities in the network (identified from Table 3).
Due to the existence of different time intervals in a batch
HEN problem, process hot and/or cold streams may exist
in more than one time interval. In order to reduce the number of heat exchange units, the heat exchangers connecting
the same pair of hot and cold streams are normally reused
in each time interval. In other words, if possible, one would
like to make use of a ‘‘common exchanger” in every time
interval. Hence, the targeting approach should consider
the opportunity to reuse these exchangers.
If we further examine the streams in their respective time
intervals, one may realise that some of the streams actually
exist in a few time intervals. For instant, both streams H1
and cold utility (CU) exist from the first to third time intervals (Table 4). By applying Eq. (2), the common exchanger
which exchanges heat between these two streams is considered three times in these time intervals. In fact, only one
heat exchanger is necessary for these two streams. Hence,
it could be concluded that if the same pair of streams exist
in more than one time interval, the numbers of additional
exchangers, UAE are given as
U AE ¼ N TI 1
ð3Þ
where NTI is the number of time interval where both
streams co-exist. Hence, the minimum exchanger units in
a batch HEN with l additional exchangers are given by
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D.C.Y. Foo et al. / Applied Thermal Engineering 28 (2008) 2089–2099
Table 3
Energy targeting for hypothetical example
Duration (h)
0–2.5
T (°C)
DHDt
2.5–4.0
Cum. DHDt
175
DHDt
Cum. DHDt
DHDt
600
0
300
4.0–6.0
300
200
780
500
45
1300
25
180
15
0
1600
0
1600
Uk
1
2
3
4
5
H1
H1, HU
H1, H2, HU
H2, HU
H2
CU
C3, CU
C3, CU
C3, CU
CU
P
kUk
1
3
4
3
1
k
X
12
ð4Þ
U AE;l
l
Applying Eq. (3) to example problem without considering
the pinch temperature, one will obtain the additional
exchangers that exist in the network to be 8 units (Table
5). Hence, the minimum units needed for this batch HEN
is actually 12 8 = 4 (Eq. (4)).
However, due to the existence of pinch temperature that
divides the problem into two different sub-networks, i.e.
regions above and below the pinch, Eq. (4) can be applied
separately in these sub-networks. Hence Eq. (4) becomes
U min;MER ¼ U above þ U below
where
U above ¼
X
U k;above k
X
390
30
180
X
U k;below k
Cold streams
Uk 90
90
U below ¼
Hot streams
X
30
200
Time interval, k
360
0
140
60
300
60
90
60
Table 4
Minimum units targeting regardless of pinch temperature
U¼
420
0
60
60
240
420
140
0
5
2100
120
60
0
60
1680
120
1500
100
1920
600
180
200
0
180
480
Cum. DHDt
0
320
900
DHDt
0
280
720
800
Cum. DHDt
DHDt
1920
240
120
125
Cum. DHDt
9.0–10.0
80
180
145
6.0–9.0
X
420
ð5cÞ
U AE;l;below
l
Table 6 further locates the hot and cold streams that exist
in their respective time intervals in two separate regions, i.e.
the regions above and below the pinch. Applying Eq. (2) in
these separate regions yields the minimum units target to
be 7 + 6 = 13 (Table 6). The number of additional
exchangers is next calculated in Table 7. Four additional
exchangers are found in the region above and below the
pinch respectively. Hence, the total numbers of exchangers
are calculated using Eq. (5), i.e. 7 + 6 4 4 = 5.
Note that the minimum units target is achieved by considering the regions above and below the pinch as two separate networks. There is a possibility to reduce Umin,MER
below the minimum by assessing the regions above and
below the pinch as one entire network. The same situation
is also observed in the HEN for continuous mode [1,4–7] as
well as continuous and batch MEN problems [47–49]. By
assessing the network as a whole, the network can be further simplified to eliminate the extraneous exchangers using
ð5aÞ
Table 6
Hot and cold streams in the regions above and below the pinch
U AE;l;above
ð5bÞ
l
and
Table 5
Calculation of additional exchangers
Time interval, k
Hot streams
Cold streams
Uk,above
Above pinch
1
2
3
4
5
–
H1, HU
H1, H2, HU
H2, HU
–
–
C3
C3
C3
–P
0
2
3
2
0
7
Hot streams
Cold streams
Uk,below
H1
H1
H1, H2
H2
H2
CU
CU
CU
CU
CU
P
k U k;below
1
1
2
1
1
6
k U k;above
Stream matches
UAE,l
H1–C3
H1–CU
H2–C3
H2–CU
HU–C3
P
l U AE;l
1
2
1
2
2
8
Time interval, k
Below pinch
1
2
3
4
5
D.C.Y. Foo et al. / Applied Thermal Engineering 28 (2008) 2089–2099
Table 7
Calculation of the additional exchangers for the regions above and below
pinch
Above pinch
Below pinch
Stream matches
UCE,l,above
Stream matches
UCE,l,below
H1–C3
H2–C3
HU–C3
P
l U AE;l;above
1
1
2
H1–CU
H2–CU
2
2
P
4
4
l U AE;l;below
the loop and path network ‘‘relaxation” technique. This
will be demonstrated in the next section.
4. Network evolution technique for batch HEN
Upon the identification of energy targets, the batch
HEN is designed individually in each time interval [13].
The MER network for the hypothetical example is shown
in Fig. 1, represented on the overall time grid diagram
developed for batch MEN [50]. This new network representation that includes time dimension provides good visualisation of a batch network, for both design and
relaxation stage.
Owing to the existence of pinch temperature, the HEN is
decomposed into two thermodynamic regions, i.e. the
region above and below the pinch. Hence, the minimum
heat exchanger units for a MER network, Umin,MER is
obtained by applying Eq. (5). However, as mentioned in
the previous section, a MER network will normally possess
more exchangers compared to that for which the pinch is
ignored, i.e.
ð6Þ
U 6 U min;MER
Note however, that ignoring the pinch would incur extra
utilities and additional operating cost. We will examine
how the conventional network evolution techniques can be
utilised to reduce the complexity of a preliminary network.
Previous section shows that, for example problem, five
exchangers are needed for a MER network. If the pinch temperature is ignored, only four exchangers are required. A further examination of the batch HEN reveals that a cross pinch
heat loop exists in the third time interval (Fig. 1). Hence, it is
clear that by breaking this heat loop, one heat exchanger will
be eliminated from the preliminary MER network. However, any attempt to eliminate any process-to-process heat
exchanger in the third time interval will not give us much impact on the overall network complexity, since both heat
exchangers in this time interval are also used in other time
intervals. Hence, we should handle this problem by considering the overall network across all time intervals.
Summing the heat load across the time interval, one
obtains the total heat load for the process-to-process heat
exchangers to be 2100 kWh (Exchanger 1) and 1800 kWh
(Exchanger 2) respectively. Following the heuristic to eliminate the heat exchanger with the smallest heat load [1],
Exchanger 2 is eliminated in the third and fourth time
intervals (4.0–9.0 h) in order to reduce network complexity.
Using heat loop and path, an alternative network is developed, with a utility penalty of 1800 kWh (720 kWh in third
time interval + 1080 kWh in fourth time interval) for both
hot and cold utilities. The resulting network is shown in
Fig. 2.
Alternatively, if Exchanger 2 is justified to be maintained due to technical considerations, one may eliminate
Exchanger 1 in second and third time intervals (2.5–
6.0 h), with the resulting network shown in Fig. 3. A higher
utility penalty of 2100 kWh is expected, as Exchanger 1
recovers more energy as compared to Exchanger 2.
The elimination of process-to-process heat exchanger
(e.g. Exchangers 1 and 2 in example problem) provides flexibility to process plants to trade-off between energy recovery and network complexity. A comparison between the
different schemes is shown in Table 8. As shown, the total
network area assuming a constant overall heat transfer
coefficient (UA) is reduced for each scheme with the
removal of process-to-process heat exchanger. Two examples are used to illustrate the developed method.
5. Example 1
Table 9 shows the stream data for the classical batch
HEN problem taken from Kemp and Deakin [12,13]. As
H1
H1
1
1600
1
1
1600
60
2093
1
60
80
80
H2
H2
2
2
Loop
120
120+720
180
180+1080
420
420
C3
C3
600 900
600 900
80
720
80+720
1200
1920+1080
1920 1080
1200
0
0
2.5
4.0
6.0
9.0
Time (h)
Fig. 1. Batch network design for hypothetical example.
10.0
2.5
4.0
6.0
9.0
10.0
Time (h)
Fig. 2. Simplified network for hypothetical example with elimination of
Heat Exchanger 2.
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D.C.Y. Foo et al. / Applied Thermal Engineering 28 (2008) 2089–2099
H1
1600
60+900
80+1200
H2
2
2
120
180
420
C3
600+900
0
720
80+1200
1920 1080
4.0
2.5
6.0
9.0
10.0
Time (h)
Fig. 3. Simplified network for hypothetical example with elimination of
Heat Exchanger 1.
Table 8
Energy targets for different scheme (hypothetic example)
Schemes
Number
of
exchangers
Hot
utility,
QH,min
(kWh)
Cold utility,
QC,min
(kWh)
Total
network
UA (kWh/
°C)
MER network
With H2
eliminated
With H1
eliminated
Base case
4
3
2600
4400
2460
4260
163.52
133.86
3
4700
4560
114.36
2
6500
6360
97.25
removed respectively. For a minimum approach temperature (DTmin) of 10 °C, the minimum hot and cold utility
targets for the overall network are determined as 198
(QH,min) and 238 kWh (QC,min), respectively [12]. The
MER network that achieves the energy targets is shown
in Fig. 4, with four process-to-process exchangers and utility exchangers respectively.
When the pinch temperature is taken into consideration,
the minimum units target for the example is calculated as
eight using Eq. (5), i.e. Uabove = 6 1 and Ubelow = 7 4.
However, when the pinch temperature is ignored, only
seven heat exchangers are needed (as determined by Eq.
(4)). A closer inspection reveals that a heat loop exists in
the network in time interval 0.3–0.5 h (shown in Fig. 4),
which causes the additional heat exchanger unit. Hence,
network evolution technique may be used to reduce the
additional heat exchanger in the network. From Fig. 4, it
is noted that Exchanger 2 is the smallest heat exchanger
in the entire network. Following the heuristic to eliminate
heat exchanger with the smallest heat load [1], a HEN with
seven heat exchangers is resulted, (Fig. 5). Note that, due to
the elimination of Exchanger 2, utility penalty is incurred.
Hence, the network in Fig. 5 has 32 kWh higher consumption for both hot and cold utilities, as compared to the
MER network in Fig. 4. This is similar to the result presented in the original work [13].
6. Example 2: Batch production of oleic acid
Table 9
Steam data for Example 1
Stream
TS
(°C)
TT
(°C)
Time
(h)
Heat flow
(kW)
Heat load
(kWh)
CP
(kW/K)
C1
H2
C3
H4
20
170
80
150
135
60
140
30
0.5–0.7
0.25–1.0
0–0.5
0.3–0.8
1150
440
480
360
230
330
240
180
10
4
8
3
shown, there are two process cold (C1 and C3) and hot
streams (H2 and H4) where heat need to be supplied/
H2
Fig. 6 shows the simulation flowsheet for the production
of oleic acid from palm olein using immobilised lipase,
modelled in the batch simulation software of SuperPro
Designer v6.0 [51]. Palm olein, a mixture of triglycerides
that contains oleic, linoleic, stearic and palmitic acids, is
firstly fed with water into a Batch Stirred Tank Reactor
(BSTR). Immobilised lipase is next added into the BSTR
for the conversion of palm olein into fatty acids and glycerine. The effluent from the BSTR which consists of fatty
acids, glycerine and other unreacted compounds is filtered
1
1
6
3
28
H4
44
2
88
4
36
36
Loop
88
C1
72
70
60
C3
16
120
0
0.25
36
8
0.30
0.50
Time (h)
Fig. 4. Network designs for Example 1.
0.70
0.80
1.00
D.C.Y. Foo et al. / Applied Thermal Engineering 28 (2008) 2089–2099
H2
1
1
6
2095
3
24
44
H4
88
4
36
72
88
C1
72
70
C3
16
120
0
0.25
64
8
32
0.30
0.50
0.70
0.80
1.00
Time (h)
Fig. 5. Network designs for Example 1 after elimination of Exchanger 2.
Fig. 6. Process flowsheet for oleic acid production.
using a plate-and-frame (P&F) filter press to separate the
immobilised lipase for reuse in the BSTR.
Due to the insolubility of palm olein and fatty acids in
water, the filtrate from the press filter will form a liquid
mixture of aqueous and organic phases. This two-phase filtrate is then sent to a decanter where it will settle into the
different aqueous and organic phases. The aqueous phase
mainly consists of water and glycerine while the organic
phase is made up of the unreacted palm olein, fatty acids
and trace amount of water and glycerine. The aqueous
phase is sent to a triple-effect evaporator in which the
water-glycerine solution (sweet-water) is concentrated to
a glycerine purity of 80%. Water recovered from the evaporator is recycled to the BSTR. Purification proceeds in a
distillation column where glycerine is purified to 99% and
is sold as a by-product of the process.
On the other hand, the organic phase from the decanter
is sent to a crystalliser where the stearic and palmitic acids
are crystallised. The crystallised stearic and palmitic acids
is then separated from the mother liquor using another
P&F filter press. The mother liquor is next sent to a vacuum distillation column where oleic acid of 80% purity is
recovered as the bottom product while other components
are emitted as waste in the column top product stream.
2096
D.C.Y. Foo et al. / Applied Thermal Engineering 28 (2008) 2089–2099
Table 10
Steam data for oleic acid case study
ID
Process stream
TS (°C)
TT (°C)
Time (h)
Heat flow (kW)
Heat load (kWh)
CP (kW/K)
C1
C2
H3
H4
H5
H6
Heater P-6
Heater P-9
P-7 top stream
P-7 bottom stream
P-10 top stream
P-10 bottom stream
10.00
98.70
76.60
321.74
78.80
262.80
310.00
230.00
50.00
50.00
50.00
50.00
9.80–11.30
6.60–11.60
9.80–11.30
9.80–11.30
6.60–11.60
6.60–11.60
626.20
31.36
2.09
279.30
0.69
12.87
939.30
156.80
3.14
418.95
3.44
64.35
1.957
0.239
0.079
1.028
0.024
0.060
Table 11
Energy targeting for oleic acid production case study (without heat
storage)
Duration
(h)
T (°C)
6.6–9.8
DHDt
316.74
9.8–11.3
Cum.
DHDt
DHDt
70.53
0.00
315.00
103.70
466.89
219.08
5.79
71.60
180.12
6.38
45.00
0.54
0.06
177.33
30.57
13.56
0.60
0.67
146.77
146.77
0.00
0.00
0.54
2.79
7.18
7.03
7.03
38.96
0.59
5.00
437.18
0.00
73.80
6.61
0.41
218.11
5.79
6.61
0.00
29.71
74.94
Cum.
DHDt
6.61
546.60
70.53
74.94
DHDt
0.00
79.71
4.41
235.00
Cum.
DHDt
543.92
70.53
257.80
11.3–11.6
2.68
0.00
streams (H3–H6) in this case study. Before heat integration
is carried out, this process requires a total amount of
1096.10 kWh for hot utility and 489.88 kWh for cold
utility.
Table 11 shows the TDHCA carried out for the example
for system without heat storage. The minimum approach
temperature DTmin is taken as 10 °C. As shown in Table
11, the total hot and cold utility targets for the overall network are determined as 621.06 (QH, min) and 14.83 kWh
(QC, min) respectively. On the other hand, if heat storage
system is installed, the total hot utility targets is reduced
to 606.23 kWh, while cold utility is completely removed
(Table 12).
Two alternative MER networks for Example 2 are
shown in Fig. 7, for network without heat storage system.
Both designs feature nine heat exchangers, i.e. five processto-process exchangers and four utility exchangers. Network A has a common exchanger (Exchanger 1) that serves
streams H1 and C2 throughout all time intervals, which is
more favourable than Network B, due to ease of operation
and maintenance.
To reduce network complexity, network evolution techniques may be employed. Since a heat loop is identified in
Network A (see Fig. 7a), one may eliminate a heat exchanger in the loop by incurring utility penalty. In this case,
Exchanger 3 is a good candidate to be eliminated due to
13.56
1.27
0.00
1.27
0.00
Table 10 shows the stream data that is extracted from
the simulation for heat integration study. As shown, there
are two process cold streams (C1–C2) and four process hot
Table 12
Energy targeting for repeated batch production (with heat storage)
Duration (h)
6.6–9.8
T (°C)
DH
316.74
9.8–11.3
Cum. DH
Heat transfer
70.53
70.53
531.77
70.53
452.05
74.94
103.70
422.35
38.96
?0.59?
2.79
?7.18?
30.57
0.06
0.06
0.67
0.67
0.00
0.00
146.77
146.77
0.00
0.54
169.48
0.00
0.00
0.00
0.54
171.62
0.00
7.18
5.00
?5.79?
0.00
0.59
45.00
7.03
7.03
204.24
0.00
71.60
6.61
0.41
218.11
5.79
73.80
6.61
0.00
29.71
74.94
Cum. DH
0.00
79.71
4.41
235.00
DH
6.61
2.68
0.00
257.80
Heat transfer
529.08
0.00
315.00
11.3–11.6
Cum. DH
DH
0.00
0.00
0.00
0.00
D.C.Y. Foo et al. / Applied Thermal Engineering 28 (2008) 2089–2099
Network A
H5
2097
2
2.21
H6
0.21
Loop
3
1
1
1
11.27
1.06
H3
4
H4
5
C2
29.59
70.83
13.87
33.20
2.77
6.64
C1
Network B
1.04
9.8
6.6
H5
5.28
3.15 419.02
510.86
11.3
11.6 Time (h)
2
2.21
H6
0.21
3
1
1
11.27
1.06
H3
4
H4
5
C2
29.59
47.07
70.83
2.77
6.64
C1
1.04
9.8
6.6
19.15 3.15 419.02
496.99
11.3
11.6 Time (h)
Fig. 7. Network designs for oleic acid case study (without heat storage).
its lower heat duty as compared to Exchanger 1. Further
more, a utility exchanger is currently in use to cold hot
stream H6 in time intervals 6.6–9.8 h and 11.3–11.6 h.
Hence this exchanger may be used to provide cooling needs
for stream H6 in time interval 9.8–11.3 h upon the removal
of Exchanger 3. Fig. 8 shows the resulting network after
the elimination of Exchanger 3, with 5.28 kWh higher consumption on both hot and cold utilities. On the other hand,
if one were to break the loop by removing Exchanger 1,
much higher utility penalty is expected, since Exchanger 1
is used in all time intervals. Further evolution using heat
path may also be considered by removing heat exchangers
with small heat duty in both networks, e.g. Exchangers 2
and 4. The extent of evolution is dependent on the accept-
H5
able level on the aspect of network complexity and
operability.
On the other hand, network design for the case with heat
storage system is shown in Fig. 9a, achieving the energy
targets identified in Table 12. For a given scenario (e.g. ease
of operation, technical consideration, etc.), Exchanger 1 is
justified to be removed from the network, one may use heat
path technique to shift the heat load of Exchanger 1 to
both hot and cold utilities in time intervals 6.6–9.8 h and
11.3–11.6 h; and higher utility consumption is resulted.
Another option to better utilise the energy penalty would
be to store the energy for a later use. As shown in
Fig. 9b, larger amount of heat is stored between time interval 6.6–9.8 h and 11.3–11.6 h and later used in 9.8–11.3 h.
2
2.21
H6
0.21
1
1
11.27
1
1.06
5.28
H3
4
H4
5
C2
29.59
70.83
13.87
33.20
2.77
6.64
C1
6.6
9.8
1.04
3.15 419.02
516.15
11.3
Fig. 8. Network A after elimination of Exchanger 3.
11.6 Time (h)
2098
D.C.Y. Foo et al. / Applied Thermal Engineering 28 (2008) 2089–2099
a
H5
2
S
H6
S
S
S
3
1
H3
1
4
H4
5
C2
29.59
47.07
70.83
C1
2.77
6.64
S
1.04
19.15 3.15
419.02 482.25
ST
6.6 2.21 11.27
b
H5
11.3 0.21 1.06
2
S
H6
14.75
9.8
S
S
3
S
H3
11.6 Time (h)
4
H4
5
C2
47.07
100.42
C1
S
1.04
9.41
S
19.15 3.15
419.02 449.98
ST
2.21
ST
6.6
2.42
40.86
9.8
0.21
44.69
11.3
Time (h)
3.83
11.6
Fig. 9. Network design for oleic acid case study (with heat storage).
Note that, two heat storages are utilised here, since the heat
sources are of different supply temperature levels.
7. Conclusion
In this work, the minimum units targeting and network
evolution techniques that were developed for the conventional HEN and batch MEN synthesis problems are
extended to batch HEN synthesis. In minimum units targeting, common heat exchangers that exist in more than
one time interval will have to be included in the analysis.
The minimum unit target sets the lower bound for a batch
HEN, before the network evolution is used to evolve the
network to reduce its complexity. It is shown that in order
to simplify a batch HEN with the network evolution techniques, a thorough analysis has to be carried out across all
time intervals of the batch process. This ensures a heat
exchanger to be eliminated from the batch HEN completely. Two examples were used to illustrate the applicability of the proposed techniques.
Further work is envisioned to incorporate the targeting
and evolutional techniques into rigorous optimisation
model. In particular, trade-off between capital (heat
exchanger elimination) and operating (utility penalty) costs
may be analysed during network evolution. Furthermore,
various objectives (e.g. productivity, rescheduling opportunity, etc.) may be incorporated into the model to facilitate
for the search of a global optimum batch HEN.
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