A comparative study of truck cycle time prediction methods in open‐pit mining

Emerald - Tập 17 Số 5 - Trang 446-460 - 2010
Emmanuel K.Chanda1, StevenGardiner2
1School of Civil, Environmental and Mining Engineering, The University of Adelaide, Adelaide, Australia
2Kalgoorlie Consolidated Gold Mines, Kalgoorlie, Australia

Tóm tắt

PurposeThe 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/approachTruck 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.FindingsThe 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/implicationsThe 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 implicationsThe 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/valueThe 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.

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Tài liệu tham khảo

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 MATLAB®, 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.

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.

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Emerald

Tập 17 Số 5

446-460

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