Evaluation of the Economic Performance Index for Loading and Hauling Fleet in Open-Pit Mines

Document Type : Research - Paper

Authors

1 M.Sc, Faculty of Mining, Petroleum & Geophysics Engineering, Shahrood University of Technology, Shahrood, Iran

2 Associate Professor, Faculty of Mining, Petroleum & Geophysics Engineering, Shahrood University of Technology, Shahrood, Iran

3 Assistant Professor, Faculty of Mining Engineering, Sahand University of Technology, Tabriz, Iran

4 M.Sc, Engineering Office Arman Gohar Sirjan Company Sirjan, Kerman, Iran

Abstract

Loading and hauling operations have the highest share of the production costs of open-pit mines. Therefore, in choosing the loading and hauling machines, paying attention to the economic aspects is necessary. For a better understanding of the financial performance of mining machinery, various performance indicators have been proposed by researchers. This research presents a special form of economic performance index, which indicates the ratio of each machine's revenue to its total cost. For this purpose, the cost and revenue of the transport fleet, including dump trucks, mining shovels, and backhoe excavators, were collected for two years (From September 2017 to September 2019) and determined. As the first step, the total cost and the amount of revenue from the operation of each machine then, the Economic Performance Index (EPI) for each of the loading and hauling machines is calculated and compared with each other. The results show that Backhoe excavators perform better than mining shovels. In addition, by examining different trucks from different manufacturers, it was found that C100 series trucks have the best performance among all trucks. Its average economic performance index value for each of those trucks is 2.82.

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  1. Boehm, B. W. (1984). “Software engineering economics”. IEEE Transactions on Software Engineering, 1: 4-21.
  2. Zhang, Y. F., and Fuh, J. Y. H. (1998). “A neural network approach for early cost estimation of packaging products”. Computers & Industrial Engineering, 34(2): 433-450.
  3. Ben-Arieh, D., and Qian, L. (2003). “Activity-based cost management for design and development stage”. International Journal of Production Economics, 83(2): 169-183.
  4. Shehab, E. M., and Abdalla, H. S. (2001). “Manufacturing cost modeling for concurrent product development”. Robotics and Computer-Integrated Manufacturing, 17(4): 341-353.
  5. Shehab, E. M., and Abdalla, H. S. (2002). “A design to cost system for innovative product development”. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 216(7): 999-1019.
  6. Roy, R. (2003). “Cost engineering: why, what, and how?”. Cranfield University, pp. 39.
  7. Cavalieri, S., Maccarrone, P., and Pinto, R. (2004). “Parametric vs. neural network models for the estimation of production costs: A case study in the automotive industry”. International Journal of Production Economics, 91(2): 165-177.
  8. Niazi, A. (2008). “Product and Manufacturing Cost Estimation: Theoretical Development and Industrial Validation (Doctoral dissertation, King’s College London)”. Ph.D Thesis.
  9. Niazi, A., Dai, J. S., Balabani, S., and Seneviratne, L. (2006). “Product cost estimation: Technique classification and methodology review”. Journal of Manufacturing Science and Engineering-Transactions of the Asme, 128(2): 563-575. DOI: https://doi.org/10.1115/1.2137750.
  10. Curran, R., Raghunathan, S., and Price, M. (2004). “Review of aerospace engineering cost modeling”. The Genetic Causal Approach Progress in Aerospace Sciences, 40(8): 487-534.
  11. Caputo, A. C., and Pelagagge, P. M. (2008). “Parametric and neural methods for cost estimation of process vessels”. International Journal of Production Economics, 112(2): 934-954.
  12. Mular, A. L. (1982). “Mining and mineral processing equipment costs and preliminary capital cost Estimation”. Canadian Institute of Mining and Metallurgy (Montreal), CIM Special, 25: pp. 265.
  13. USBM, (1987). “US Bureau of mines cost estimating system handbook”. Mining and beneficiation of Metallic and nonmetallic minerals expected fossil fuels in the United States and Canada. United States Bureau of Mines, Open-file report 10-87, Denver, Colorado.
  14. O’Hara, T. A. (1980). “Quick guide to the evaluation of ore bodies”. CIM Bulletin, 88: 34-43.
  15. Noakes, M., and Lanz, T. (1993). “Cost estimation handbook for the Australian mining industry”. Australasian Institute of Mining and Metallurgy, pp. 412.
  16. Western Mining Engineering Institute, (2002). “Mine and Mill Equipment; an estimator guide”. Aventurine, USA.
  17. Hartman, H. L., and Mutmansky, J. M. (2002). “Introductory mining engineering”. John Wiley & Sons.
  18. Noakes, M., and Lanz, T. (1993). “Cost estimation handbook for the Australian mining industry”. Political Science. DOI: 10.1016/0148-9062(94)91320-x.
  19. Lowrie, R. L. (Ed.). (2002). “SME mining reference handbook”. Society for Mining, Metallurgy, and Exploration, pp. 464.