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]