chanda2010

Engineering, Construction and Architectural Management
A comparative study of truck cycle time prediction methods in open-pit mining
Emmanuel K. Chanda Steven Gardiner
Article information:
Downloaded by University of Pittsburgh At 11:28 10 March 2015 (PT)
To cite this document:
Emmanuel K. Chanda Steven Gardiner, (2010),"A comparative study of truck cycle time prediction methods
in open-pit mining", Engineering, Construction and Architectural Management, Vol. 17 Iss 5 pp. 446 - 460
Permanent link to this document:
http://dx.doi.org/10.1108/09699981011074556
Downloaded on: 10 March 2015, At: 11:28 (PT)
References: this document contains references to 13 other documents.
To copy this document: [email protected]
The fulltext of this document has been downloaded 928 times since 2010*
Access to this document was granted through an Emerald subscription provided by 121184 []
For Authors
If you would like to write for this, or any other Emerald publication, then please use our Emerald for
Authors service information about how to choose which publication to write for and submission guidelines
are available for all. Please visit www.emeraldinsight.com/authors for more information.
About Emerald www.emeraldinsight.com
Emerald is a global publisher linking research and practice to the benefit of society. The company
manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as
providing an extensive range of online products and additional customer resources and services.
Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee
on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive
preservation.
*Related content and download information correct at time of download.
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/0969-9988.htm
ECAM
17,5
A comparative study of truck
cycle time prediction methods
in open-pit mining
446
Emmanuel K. Chanda
School of Civil, Environmental and Mining Engineering,
The University of Adelaide, Adelaide, Australia, and
Received June 2009
Revised June 2009
Accepted April 2010
Steven Gardiner
Downloaded by University of Pittsburgh At 11:28 10 March 2015 (PT)
Kalgoorlie Consolidated Gold Mines, Kalgoorlie, Australia
Abstract
Purpose – The purpose of this paper is to compare the predictive capability of three methods of truck
cycle time estimation in open-pit mining: computer simulation, artificial neural networks (NNs), and
multiple regressions (MRs). The aim is to determine the best method. The most common method
currently used is computer simulation.
Design/methodology/approach – Truck cycle times at a large open pit mine are estimated using
computer simulation, artificial NNs, and MRs. The estimated cycle times by each method are in turn
compared to the actual cycle times recorded by a computerized mine monitoring system at the same
mine. The errors associated with each method relative to the actual cycle times are documented and
form the basis for comparing the three methods.
Findings – The paper clearly indicates that computer simulation methods used in predicting truck
cycle times in open-pit mining underestimate and overestimate the results for short and long hauls,
respectively. It appears that both NN and regression models are superior in their predictive abilities
compared to computer simulations.
Research limitations/implications – The cycle time prediction models developed apply to a
specific mine site and one has to be careful not to directly apply these models to other operations.
Practical implications – The paper describes the implementation of regression and NN modelling.
An opportunity exists for mines to utilise the large volumes of data generated to predict truck haulage
cycle times more accurately and hence, improve the quality of mine planning.
Originality/value – The paper addresses an area of need in the mining industry. Accurate prediction
of cycle times is critical to mine planners as it impacts on production targets and hence, the budgets.
Keywords Production cycle, Simulation, Multiple regression analysis, Neural nets
Paper type Research paper
Engineering, Construction and
Architectural Management
Vol. 17 No. 5, 2010
pp. 446-460
q Emerald Group Publishing Limited
0969-9988
DOI 10.1108/09699981011074556
Introduction
There are a number of methods being used by the industry to predict load-haul cycle
times in open-pit mining. The most commonly used approach is computer simulation
using the so-called Monte-Carlo technique. This method aims to simulate by computer
the step-by-step operations of the entire truck and loader fleet, including the randomness
of the loader-truck cycle time, and the queuing of trucks at the loader and at the dump or
elsewhere. However, the accuracy of the truck and loader cycle times generated by these
simulations is questionable as evidenced by significant discrepancies between actual
and predicted cycle times (Blackwell, 1999; Hardy, 2007). Computer programs such as
truck and loader productivity and cost (TALPAC) and Caterpillar’s fleet production
and cost (FPC) analysis are currently used by mining engineers to estimate open-pit
Downloaded by University of Pittsburgh At 11:28 10 March 2015 (PT)
haulage truck requirements. A perceived shortcoming of software such as TALPAC is
that it provides a best estimate based on manufacturers’ equipment performance
specifications and capabilities, such as rim-pull curves. This method incorporates
performance assumptions that are optimistic, as equipment is subjected to wear and tear
which reduces its operational efficiency (Hardy, 2007). Furthermore, no account is made
for random events that occur over the course of a truck haul cycle. Queuing to load and
unload, refuelling, operator breaks, and unloaded trucks giving way to loaded trucks on
narrow sections of road or intersections are examples of events that will impact on a
truck cycle time. Such events cannot be accounted for when a predicted cycle time is
based on theoretical equipment capabilities.
In this study, one of the analytical models developed is based on multiple regression
(MR), where factors in the load/haul cycle considered potentially significant are the
independent variables and the cycle time is the dependent variable. Edwards and Holt
(2000) developed an MR model for hydraulic excavator cycle time with satisfactory
results. Edwards and Griffiths (2000) and Tam et al. (2002) have subsequently shown
that a neural network (NN) model for hydraulic excavator cycle times has a predictive
performance that “is superior to the MR models.” However, this assertion may not be
true in every case.
The second analytical model developed for this project is based on NN technology.
To this end the MATLABw (MATrix LABoratory) software package developed by The
Mathworks (Demuth et al., 2005) is utilised to create an NN that is the core of an artificial
intelligence (AI) model. The same significant variables used in the MR models are used
to train the AI model. NN have found application in mining; for example, Alehossein
(2002) discusses an NN model for prediction of three Cartesian force components acting
at and on the cutter-head of a rock cutting machine, Tam et al. (2002) developed an
artificial NN model predict excavator performance, and Grayeli and Moosavi (2006)
used artificial NN modelling to predict the load distribution in mining cable bolts.
In this study, three methods of truck cycle time estimation are compared. These are:
(1) simulation using TALPAC;
(2) artificial NN; and
(3) multiple regression.
The objective is to determine the best method among the three based on comparison with
the actual cycle times for various truck haulage routes at a large open-pit gold mine in
Western Australia. A secondary objective of this study is to investigate whether the
predictive capabilities of the three methods used will increase from TALPAC to the MR
model and from this model to the AI model as previously believed.
The scope of this study is limited to predicting the truck travel time (loaded þ empty)
only; the reason being that these are the elements of truck cycle for which accurate data
were available for modelling. However, a base set of 12 independent variables is used
in both the regression and NN models (Table I).
TALPAC Software
TALPAC is one of the most widely used truck and loader simulation packages
in Australian mining, but most truck manufacturers have programs that calculate
results using similar algorithms, for example, FPC by Caterpillar, Inc. For mine
Truck cycle time
447
ECAM
17,5
Variable
Description
shift
wait
Is a Boolean type data field, 0 for day shift and 1 for night shift
Is a Boolean field equal to 1 where waiting at shovel is non-zero, and a 0 in all
other cases
Is a Boolean field equal to 1 where queuing at shovel is non-zero, and a 0 in all
other cases
Numeric field listing the loading time for each cycle
Numeric field listing distance of haul route
Numeric field listing the change in elevation over the haul route
Numeric filed representing the type of loading unit
Numeric field listing total rainfall for the day of the haul cycle in mm
Numeric field listing evaporation for the day of the haul cycle in mm
Numeric field listing speed of the maximum wind gust on the day of the haul
cycle in km/h
Numeric field representing direction of the maximum wind gust on the day of
the haul cycle
A numeric field averaging the 9 a.m. and 3 p.m. relative humidity values
que
Downloaded by University of Pittsburgh At 11:28 10 March 2015 (PT)
448
Table I.
Base set of independent
variables
load
tot_dist
vert_dist
loading_unit
rain_amount
evap_amount
max_wind_gust
max_wind_direction
av_rel_humidity
planning purposes, the aim of computer simulation is to determine the truck-loader
fleet productivity expressed in bank cubic metres (BCM) per operating hour.
The first step in running a TALPAC simulation is to define the load and haul
equipment. Properties such as bucket/tray capacities, power ratings, and both capital
and operating costs are defined for the load and haul units used by the operation. Also
defined at this point are the expected spotting and loading times. The user is then able
to assign a statistical spread to the times that will be used in the simulation based on
the expected time.
The intended haul route then is defined as a series of sections. Each section is
allocated a length, grade, rolling resistance, and speed limitation. Indication is also
made as to whether the truck is loaded or empty for that section of the route. Truck
spotting and loading/dumping times also are included in the haul cycle as auto entries
of zero distance. The duration of these sections of the cycle are drawn from the
parameters defined for the loading and dumping units.
Material properties and shift rosters are defined before the simulation is run for the
extraction of the target BCM (or tonnes) of rock. A number of reports are generated by
the simulation relating to productivity, tyre and fuel consumption, haul cycle, and cash
flow. For this study, the haul cycle report is the key point of interest.
Artificial NNs
Artificial NNs have been utilised for a variety of applications as the processing power of
computers has improved since the late-1980s. The primary advantage of such networks is
their ability to “learn” non-linear relationships between input variables and target output
values. Artificial NNs are designed in such a way as to attempt to replicate biological NNs
that are part of an animal’s nervous system. The human brain does not use higher
mathematics to solve problems; instead hundreds of billions of neurons interactively put
their contributions (weights) together towards achieving the best and quickest possible
outcome (Mehrotra et al., 1997). This concept has been used in the NN technique to predict
the haulage cycle time from a combination of variables presented in Table I.
Downloaded by University of Pittsburgh At 11:28 10 March 2015 (PT)
A simplified representation of feed-forward NN functionality is shown in Figure 1,
where R inputs are presented to a single neuron transfer function f. The elementary
neuron has R inputs and with each input there is an associated weight w. Before using
the NN for predictions, it is trained for a set of data to calculate the weights of each
neuron-to-neuron connection. This happens by continuous readjustment of the weights
through the redistribution of the feedback errors, which arise by comparing the
calculated output with the actual. A single layer neuron network is similar to a
multivariable linear regression model. The input to the transfer function f comprises the
sum of the weighted inputs and bias and the output from transfer function gives the
activation a of the neuron. Back propagation networks often use the hyperbolic tangent
sigmoid (tansig), ft, or log sigmoid (logsig), fl transfer function as given in equation (1):
ft ¼
2
21
1 þ e 22n
fl ¼
1
1 þ e 2n
Truck cycle time
449
ð1Þ
Each input is multiplied by its individual weighting, w1,1 to w1,R, where the subscripts
refer to (layer 1, input number). The bias, b, is applied to the sum of all weighted inputs
before presentation to the neuron function. Mathematical details of the process of
applying weights and biases in the training of the NN are discussed by Haykin (2008).
Multiple linear regression
Multiple linear regression analysis is a statistical method of determining a formula
with which to find the value of a dependent (unknown) variable by its relationship to a
set of independent (known) variables. Each of the independent variables is assigned
a weighting that reflects its impact on the value of the dependent variable. This
weighting is referred to as the coefficient of the variable in question. The formula is
developed based on a set of data where the values of both independent and dependant
variables are known. Then the formula is applied to a set of known independent
variables in an attempt to estimate the unknown dependent variable:
P1
W11
P2
n
Σ
a
f
P3
W1R
PR
b
l
Input
Source: Demuth et al. (2005)
General neuron
= f (Wp + b)
Figure 1.
Simplified neuron
functionality
ECAM
17,5
.
The general form of a linear regression model is shown in equation (2) (Neter et al.,
1985):
p21
X
bk X ik þ 1i where X i0 ¼ 1
ð2Þ
Yi ¼
k¼0
Downloaded by University of Pittsburgh At 11:28 10 March 2015 (PT)
450
where:
.
there are ( p 2 1) independent variables;
.
the Yi term represents each instance of the dependent variable;
.
the bk term represents each of the coefficient values from 0 to ( p 2 1);
.
the Xik term represents each instance of all independent variables from 0 to
( p 2 1); and
.
1i is a random error term with a mean value of 0.
Assuming that the expected value of the random error term is zero, an expected value
for the dependent variable is shown by equation (3) (Neter et al., 1985):
EðY Þ ¼ b0 þ b1 X 1 þ b2 X 2 þ · · · þ bp21 X p21
ð3Þ
It is not expected that all independent variables will have a purely linear influence on the
cycle time. It has been shown, however, that a linear MR technique can be utilised by
manipulating the data before commencing the regression (Chanda, 1989). In developing
his model to estimate the probability of success in multiple seam room and pillar coal
mining, Chanda (1989) used the linear form of each of the identified independent
variables as well as cube root, square root, square, cubic, and logarithmic forms of each
variable to take into account the non-linear interactions among the variables. A similar
process is applied to the defined independent variables used in this project.
It is recognised that not all defined independent variables will prove significant in
determining the dependent variable. In such cases, forward stepwise regression is
considered to be the preferential regression method (Draper and Smith, 1966). Stepwise
regression is an iterative process where the correlation of each of the independent
variables to the dependent variable is assessed, and the independent variable with the
highest correlation is entered into the regression equation at each iteration. The process
is ended when all variables are included in the equation or the correlation between the
remaining independent variables and the dependent variable is considered insignificant.
Modelling applications
Truck cycle time data
Data recorded by a computerised fleet management system employed at the mine was
utilised for this project. The data were extracted from the onsite database via
specifically developed database queries. Two initial sample sets of data received from
the mine were in the form of Excel spreadsheet files. The datasets were filtered later to
include only truck cycles along a particular haul route. Haul route data were supplied
by the mine in the form of digitised strings from a mine design package. The “all bench
data” refer to data relating to a mining bench from which a loading unit loads broken
rock onto trucks for haulage to the surface. The data were stored in separate
spreadsheets representing 16 individual blasting blocks (block data). An additional
Downloaded by University of Pittsburgh At 11:28 10 March 2015 (PT)
field of loading unit was added to allow filtering of the data in order to focus on a
particular loading unit (shovel) as the source of material for haulage.
The routes were created by starting in the approximate centre of the mine bench in
question and digitising points along the haul route by snapping points to the pit
topography triangulation along the centre of the haul route to the dumping point. The
specific blast blocks represented in the cycle data were selected to correspond with
the actual block where the haul route string originated. Analysis in Vulcan generated the
total length of the route and the total change in elevation along the length of each route –
intended variables in the NN and regression models. The digitised routes were used to
define the haul routes in the TALPAC simulation. Figure 2 shows a typical haul route from
a specific blast block in the pit to the stockpile. Five haul routes were used in the modelling.
Truck cycle time
451
TALPAC model
Haul route information is entered into TALPAC by importing route information in the
form of comma separated value (CSV) text files. The CSV files contain values of the
northing (Y), easting (X), and elevation (Z) at all points digitised along the haul route.
Such files are created by exporting string data from Vulcan Software. Speed limits
applicable to different sections of each haul route are entered with the haul route data.
Speed limits used for this project are the actual limits supplied by the mining company.
Rolling resistance working against trucks is also defined with the haul routes. The
rolling resistance defined for this project is the TALPAC default of 3 per cent. This is
not a measured variable and is often adjusted by TALPAC users as a way of forcing
the software to fit actual cycle times. Once the TALPAC variables are defined, one can
simulate the haulage system to determine cycle times, costs, and productivity.
In order to fully investigate the variation between TALPAC cycle time predictions,
25 simulations are conducted for each haul route in this project.
Artificial NN model
Spreadsheet data for network inputs and targets were imported into the Matlabw for
modelling. For this project, a total of 48 networks are developed. All networks are
two-layer networks where inputs are fed into a layer of tansig neuron/s. Outputs from
layer 1 subsequently report to a single purelin neuron, which in turn produces a single
Blast polygons
Haul road
Stockpile
Figure 2.
Plan of the open pit
showing a haul road
ECAM
17,5
452
output value. For each of the datasets, 12 networks are created with the number of tansig
neurons in layer 1 ranging from one to 12. Figure 3 is a graphical representation of a
network with six tansig neurons in the first layer. Using the tools in MATLAB the network
was trained based on the back-propagation algorithm (Edwards and Griffiths 2000).
Regression model
The regression model uses the same training and simulation matrices as created for the
NN modelling. However, it is necessary to transpose network-training data so that it is in
the correct format to utilise the regress command and obtain variable coefficients.
Equation (4) is an example of a regression model developed for all bench data. As the
same sets of data are used for regression as the NN modelling, regression results are also
filtered to contain only cycle times where trucks are loaded by one of the face shovels:
Downloaded by University of Pittsburgh At 11:28 10 March 2015 (PT)
time ¼ 20:670783171ðshiftÞ2 2 0:407837745ðwaitÞ0:50 þ 0:295354166ðqyeÞ0:33
þ 1:39236775ðload Þ 2 0:029126259ðload Þ2 þ 0:000401797ðload Þ3
þ 0:030977804ðtot_distÞ 2 5:49535 £ 10206 ðtot_distÞ2
2 3:41946 £ 10211 ðtot_distÞ3 þ 0:001092995ðvert_distÞ2
þ 3:29469 £ 10206 ðvert_distÞ3 þ 0:010015433ðrain_amountÞ3
2 0:057618089ðevap_amountÞ2 þ 0:005808809ðevap_amountÞ3
ð4Þ
2 0:012901967ðmax _windgustÞ 2 0:002691516ðmax _windgustÞ2
þ 5:79927 £ 10205 ðmax _windgustÞ3
þ 0:642826831 log10 ðmax _wind_directionÞ
þ 0:136417323ðav_rel_humidityÞ 2 0:004912923ðav_rel_humidityÞ2
þ 5:13972 £ 10205 ðav_rel_humidityÞ3
12
Figure 3.
Network with six tansig
neurons in the first layer
12 Input variables
Input weights
Hidden layer weights
IW{1,1}
LW{2,1}
b{1}
b{2}
Layer 1 bias
6
Layer 2 bias
6 tansig neurons
O
u
t
p
u
t
1
1 purelin neuron
Truck cycle time
453
Artificial NN results
The mean time for each list of outputs is considered the simulated cycle time. As is
shown in Figure 4, the predicted cycle time varies between network structures.
In the majority of cases, the variance is minimal. Note that the outlier prediction for the
eight-neuron networks applied to the filtered bench dataset (Figure 5) has a negligible
effect on the mean cycle time for that network. A similar observation can be made
Time
Mean Minimum Maximum
Variation (%)
(minutes) (minutes) (minutes) ðmaximum 2 minimum=minimumÞ £ 100%
Route
Brownhill – TW4
Chaffers – Blendfinger
Chaffers – SW3
Stores – Blendfinger
Stores – TW4
24.83
23.23
24.97
44.00
48.31
24.70
23.09
24.86
43.78
48.11
24.95
23.34
25.20
44.21
48.57
1.0
1.1
1.4
1.0
1.0
Table II.
TALPAC results
Neural network results for all bench data
45
40
Brownhill to TW4
Chaffers to blendfinger
Chaffers to SW3
Stores to blendfinger
Stores to TW4
35
30
25
1
N
eu
ro
n
2
N
eu
ro
ns
3
N
eu
ro
ns
4
N
eu
ro
ns
5
N
eu
ro
ns
6
N
eu
ro
ns
7
N
eu
ro
ns
8
N
eu
ro
ns
9
N
eu
ro
ns
10
N
eu
ro
ns
11
N
eu
ro
ns
12
N
eu
ro
ns
20
A
ct
ua
l
Time (minutes)
Downloaded by University of Pittsburgh At 11:28 10 March 2015 (PT)
Results
Variation in the predicted cycle time between simulations for each route in TALPAC is
limited. The most significant difference between minimum and maximum predicted
times is a 1.4 per cent variation for the “Chaffers to SW3” haul route. Table II
summarises the simulated times for relevant haul routes as determined by TALPAC.
Mean times listed in Table II are used for comparison with regression and NN cycle
time predictions.
Networks
Figure 4.
Comparative results for all
networks developed for all
bench simulation data
ECAM
17,5
Neural network results for filtered bench data
45
Brownhill to TW4
Chaffers to blendfinger
Chaffers to SW3
Stores to blendfinger
Stores to TW4
35
30
25
Figure 5.
Comparative results for all
networks developed
for filtered bench
simulation data
A
ct
ua
l
1
N
eu
ro
n
2
N
eu
ro
ns
3
N
eu
ro
ns
4
N
eu
ro
ns
5
N
eu
ro
ns
6
N
eu
ro
ns
7
N
eu
ro
ns
8
N
eu
ro
ns
9
N
eu
ro
ns
10
N
eu
ro
ns
11
N
eu
ro
ns
12
N
eu
ro
ns
20
Networks
regarding the outlier for the three-neuron network as shown in Figure 6 for all block
data. However, Figure 7 shows a large discrepancy for the network with 12 tansig
neurons. The filtered block dataset contain the lowest number of sample cycles, and it is
expected that the poor result for the 12-neuron network is an example of “over fitting”
Neural network results for all block data
45
40
Time (minutes)
Brownhill to TW4
Chaffers to blendfinger
Chaffers to SW3
Stores to blendfinger
Stores to TW4
35
30
25
ua
l
N
eu
ro
n
2
N
eu
ro
ns
3
N
eu
ro
ns
4
N
eu
ro
ns
5
N
eu
ro
ns
6
N
eu
ro
ns
7
N
eu
ro
ns
8
N
eu
ro
ns
9
N
eu
ro
ns
10
N
eu
ro
ns
11
N
eu
ro
ns
12
N
eu
ro
ns
20
1
ct
Figure 6.
Comparative results for all
networks developed for all
block simulation data
A
Downloaded by University of Pittsburgh At 11:28 10 March 2015 (PT)
454
Time (minutes)
40
Networks
Neural network results for filtered block data
Truck cycle time
80
Brownhill to TW4
Chaffers to SW3
Stores to blendfinger
Stores to TW4
455
60
50
40
30
1
N
eu
ro
n
2
N
eu
ro
ns
3
N
eu
ro
ns
4
N
eu
ro
ns
5
N
eu
ro
ns
6
N
eu
ro
ns
7
N
eu
ro
ns
8
N
eu
ro
ns
9
N
eu
ro
ns
10
N
eu
ro
ns
11
N
eu
ro
ns
12
N
eu
ro
ns
20
A
ct
ua
l
Downloaded by University of Pittsburgh At 11:28 10 March 2015 (PT)
Time (minutes)
70
Networks
of data due to an excess of neurons in the first layer of the network in relation to the
number of samples presented to the network.
In order to determine the optimal network structure for each dataset, an iterative
process was used in which the target time was subtracted from the simulated time for
each haul route. Results relating to the network with minimum variance were used for
comparison with the other methods.
Regression model results
As the same sets of data were used for regression modelling as the NN modelling,
regression results also were filtered to contain only cycles where trucks were loaded by
one of the face shovels.
Comparison of results
Predicted cycle times for all simulation methods are shown in Figures 8-11. Predicted
times in each graph are plotted against a line representing the mean actual cycle times
for each dataset. Where predicted times were close to the actual cycle times, points
were plotted in close proximity to this line.
Generally speaking, predicted times for both the regression and NN models were
significantly more accurate than the TALPAC predictions for all sets of data. There
appears to be a limited variation between the times predicted by regression and NN
models.
In order to quantify variations between predicted and actual cycle times, a
percentage error for each haul route was calculated using equation (5). Graphical
representations of calculated errors are shown in Figures 12-15:
Figure 7.
Comparative results for
all networks developed
for filtered block
simulation data
ECAM
17,5
Simulated times (minutes)
Regression simulation
Figure 8.
Compared results for all
bench simulation data
45
TALPAC simulation
Neural network simulation
Actual times
40
35
30
25
20
20
25
30
35
Actual time (minutes)
40
45
Filtered bench data
Simulated times (minutes)
50
Figure 9.
Compared results
for filtered bench
simulation data
Regression simulation
TALPAC simulation
45
Neural network simulation
Actual times
40
35
30
25
20
20
25
30
35
Actual time (minutes)
40
45
40
45
All block data
50
Simulated times (minutes)
Downloaded by University of Pittsburgh At 11:28 10 March 2015 (PT)
456
All bench data
50
Figure 10.
Compared results for all
block simulation data
TALPAC simulation
Regression simulation
45
Neural network simulation
Actual times
40
35
30
25
20
20
25
30
35
Actual time (minutes)
Simulated times (minutes)
Regression simulation
TALPAC simulation
45
Neural network simulation
Actual times
40
457
35
30
25
20
20
25
30
35
40
45
Actual time (minutes)
Figure 11.
Compared results
for filtered block
simulation data
Percentage errors all bench data
20
15
10
5
Errors (%)
Downloaded by University of Pittsburgh At 11:28 10 March 2015 (PT)
Truck cycle time
Filtered block data
50
0
–5
Regression
Neural network
TALPAC
–10
–15
–20
–25
Chaffers to blendfinger
Brownhill to TW4
Chaffers to SW3
Stores to blendfinger
Stores to TW4
Figure 12.
Comparative percentage
errors for all bench
simulation data
–30
Haul routes
Percentage error ¼
Mean predicted time 2 Mean actual time
£ 100%
Mean actual time
ð5Þ
Figures 12-15 show that both regression and NN models are more accurate than
TALPAC in all cases. TALPAC simulation consistently is most accurate for the
Chaffers to SW3 haul route through all datasets.
Conclusions
The results outlined above would appear to indicate that both NN and regression
models based on actual mine data are superior in their predictive abilities to TALPAC
simulations. TALPAC Software appears to have under predicted cycle times for
shorter haul routes and over predicted times for the longer haul routes. There appears
ECAM
17,5
Percentage errors filtered bench data
20
15
10
5
Errors (%)
458
0
–5
–20
–25
Neural network
Chaffers to blendfinger
Brownhill to TW4
Chaffers to SW3
Stores to blendfinger
Stores to TW4
TALPAC
–30
Haul routes
Percentage errors all block data
20
15
10
Errors (%)
Downloaded by University of Pittsburgh At 11:28 10 March 2015 (PT)
–15
Figure 13.
Comparative percentage
errors for filtered bench
simulation data
Regression
–10
5
0
–5
Figure 14.
Comparative percentage
errors for all block
simulation data
Chaffers to blendfinger
Brownhill to TW4
Chaffers to SW3
Stores to blendfinger
Stores to TW4
Regression
Neural network
TALPAC
–10
–15
Haul routes
to be minimal quantifiable difference between predictions made by the regression and
NN models.
The data utilised for this project is limited to information made available from an
operating mine only. Therefore, the models developed apply to this specific mine site
and one has to be careful not to directly apply these models to other operations.
However, the regression and NN models similar to those developed in this project
easily can be developed for any open-pit operation providing that sufficient data is
available. In such a case, input data could be refined to include variables considered
significant by the operation itself.
Truck cycle time
Percentage errors filtered block data
25
20
15
Brownhill to TW4
Chaffers to SW3
Stores to blendfinger
Stores to TW4
Errors (%)
10
459
5
0
–5
Regression
Neural network
Downloaded by University of Pittsburgh At 11:28 10 March 2015 (PT)
–10
–15
TALPAC
–20
Haul routes
The quantity, and quality, of data recorded by open-cut mining operations is ever
increasing, particularly with the implementation of computer-based monitoring
systems. An opportunity exists for mines to utilise the collected data to predict load
and haul cycle times more accurately. The benefits to be gained from increased
accuracy include the potential to assist in mine planning, scheduling and budgeting
decisions.
References
Alehossein, H. (2002), “Data mining for miners – the general mixed model (GMM)”, Exploration
and Mining Research News, Vol. 14, pp. 6-8.
Blackwell, G.H. (1999), “Estimation of large open pit haulage truck requirements”, CIM Bulletin,
Vol. 92 No. 1028, pp. 143-69.
Chanda, E.C.K. (1989), “Evaluation of success probability in multiple seam room-and-pillar
mining”, Mining Science and Technology, Vol. 9, pp. 57-73.
Demuth, H., Beale, M. and Hagan, M. (2005), Neural Network Toolbox for Use with MATLABw,
The Mathworks, Natick, MA.
Draper, N.R. and Smith, H. (1966), Applied Regression Analysis, Wiley, New York, NY.
Edwards, D.J. and Griffiths, I.J. (2000), “Artificial intelligence approach to calculation of
hydraulic excavator cycle time and output”, Mining Technology: Transactions of the
Institute of Mining and Metallurgy, Section A, Vol. 109 No. 1, pp. 23-9.
Edwards, D.J. and Holt, G.D. (2000), “ESTIVATE: a model for calculating excavator productivity
and output costs”, Engineering, Construction and Architectural Management, Vol. 7 No. 1,
pp. 52-62.
Grayeli, R. and Moosavi, M. (2006), “Using artificial neural network (ANN) for modelling of load
distribution along fully grouted cable bolts”, Mining Technology: IMM Transactions
Section A, Vol. 115 No. 1, pp. 24-33.
Hardy, R.H. (2007), “Selection criteria for loading and hauling equipment – open pit mining
applications”, PhD thesis, Curtin University of Technology, Sydney, 2 Volumes, 558 pp.
Figure 15.
Comparative percentage
errors for filtered block
simulation data
ECAM
17,5
Downloaded by University of Pittsburgh At 11:28 10 March 2015 (PT)
460
Haykin, S. (2008), Neural Networks and Learning Machines, 3rd ed., Prentice-Hall, Upper Saddle
River, NJ, 938 pp.
Mehrotra, K., Mohan, C.K. and Ranka, S. (1997), Elements of Artificial Neural Networks, The MIT
Press, Cambridge, MA.
Neter, J., Wasserman, W. and Kutner, M.H. (1985), Applied Linear Statistical Models, 2nd ed.,
Richard D. Irwin, Homewood, IL.
Tam, C.M., Tong, T.K.L. and Tse, S.L. (2002), “Artificial neural networks model for predicting
excavator productivity”, Engineering Construction & Architectural Management, Vol. 9
Nos 5/6, pp. 446-52.
About the authors
Emmanuel K. Chanda is an Associate Professor and a Program Leader in School of Civil,
Environmental and Mining Engineering, University of Adelaide (Australia). His expertise is in
mine planning and design. He has over 25 years experience in mine operations, academia, and
consulting, mainly involving base and precious metal projects. His research interests are process
optimization in mining, new generation mine haulage and systems, novel mining techniques,
and measurement and sensing in mining. Chanda has been a recipient of the American Fulbright
Senior Research Scholarship. For ten years prior to joining the University of Adelaide, he worked
for Curtin University of Technology, WA School of Mines, as Senior Lecturer, Associate
Professor, and Head of the Mining Engineering Department and member of the Program Leaders
Committee of Mining Education Australia (MEA). Prior to joining Curtin University, Chanda
worked for Zambia Consolidated Copper Mines Limited where he held various positions
including that of Mine Planning Superintendent. Chanda has BMinSc (Mining Engineering) from
the University of Zambia, MEng (Engineer of Mines) from the Colorado School of Mines, USA,
and PhD (Mining Engineering) from the Technical University of Berlin, Germany. He is a
member of the Australasian Institute of Mining and Metallurgy (AusIMM) and the Society for
Mining, Metallurgy and Exploration (SME). Emmanuel K. Chanda is the corresponding author
and can be contacted at: [email protected]
Steven Gardiner is a Long-Term Planning Engineer at Kalgoorlie Consolidated Gold Mines’
Superpit Operation in Kalgoorlie, Western Australia. He graduated from Curtin University of
Technology, WA School of Mines in 2006 and commenced working as a graduate at BHP Billiton
Iron Ore’s Mt Whaleback mine in the Western Australian Pilbara region. He has returned to
Kalgoorlie to gain experience in a different type of operation and commodity, and to continue his
studies. Gardiner enjoyed a diverse career path before enrolling in university as a mature age
student. He successfully completed a BEng (Mining Engineering) with first class honours and
received a number of academic awards in the process. He is currently working at attaining a
Master of Engineering Science (Mining) and is a member of the Australasian Institute of Mining
and Metallurgy (AusIMM).
To purchase reprints of this article please e-mail: [email protected]
Or visit our web site for further details: www.emeraldinsight.com/reprints
Downloaded by University of Pittsburgh At 11:28 10 March 2015 (PT)
This article has been cited by:
1. Alireza S. Kaboli, David G. Carmichael. 2014. Optimum scraper load time and fleet size for minimum
emissions. International Journal of Construction Management 14, 209-226. [CrossRef]
2. Alireza S. Kaboli, David G. Carmichael. 2014. Truck dispatching and minimum emissions earthmoving.
Smart and Sustainable Built Environment 3:2, 170-186. [Abstract] [Full Text] [PDF]
3. Toly Chen. 2014. A PCA-FBPN Approach for Job Cycle Time Estimation in a Wafer Fabrication Factory.
International Journal of Fuzzy System Applications 2:10.4018/IJFSA.20120401, 50-67. [CrossRef]
4. David G. Carmichael, Beau J. Bartlett, Alireza S. Kaboli. 2014. Surface mining operations: coincident unit
cost and emissions. International Journal of Mining, Reclamation and Environment 28, 47-65. [CrossRef]
5. David G. Carmichael, Evan H. Williams, Alireza S. KaboliMinimum Operational Emissions in
Earthmoving 1869-1878. [CrossRef]
6. Toly Chen. 2011. Job cycle time estimation in a wafer fabrication factory with a bi-directional classifying
fuzzy-neural approach. The International Journal of Advanced Manufacturing Technology 56, 1007-1018.
[CrossRef]