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A Simulation Model for Estimation of Mine Haulage Fleet Productivity

  • S. Upadhyay
  • M. Tabesh
  • M. BadiozamaniEmail author
  • H. Askari-Nasab
Conference paper
Part of the Springer Series in Geomechanics and Geoengineering book series (SSGG)

Abstract

A good estimation of haulage fleet productivity is a pivotal input to determine the mine fleet requirement. In open pit mining, the two major factors affecting the fleet productivity are the planned tonnage to be moved, that is based on the long-term production schedule, and the time it takes to move the dirt tonnage which depends on the mine road network. The haulage speed, haulage time and the truck load tonnages are all probabilistic values due to the uncertainty involved in real mining operations, and simulation technique is a powerful tool to capture this uncertainty. In this paper, a simulation framework is presented to estimate the productivity of haulage fleet for open pit mining operations with truck-and-shovel system. The Productivity KPI is the Tonne per Gross Operating Hour (TPGOH) for combinations of Dig and Dump locations. Historical data are used to fit probability distributions for haulage cycle components, and the mine road network and long-term production schedule are the main inputs to the model. The developed model is verified and validated through implementing it on a real case from Alberta oil sands open pit operations.

Keywords

Fleet productivity Open pit mining Monte Carlo simulation 

References

  1. 1.
    Curry, J.A., Ismay, M.J., Jameson, G.J.: Mine operating costs and the potential impacts of energy and grinding. Miner. Eng. 56, 70–80 (2014)CrossRefGoogle Scholar
  2. 2.
    Oraee, K., Goodarzi, A.: General approach to distribute waste rocks between dump sites in open cast mines. In: Sixteenth International Symposium on Mine Planning and Equipment Selection (MPES 2007), pp. 701–712. Reading Matrix Inc. (2007)Google Scholar
  3. 3.
    Akbari, A.D., Osanloo, M., Shirazi, M.A.: Minable reserve estimation while determining ultimate pit limits (UPL) under price uncertainty by real option approach (ROA). Arch. Min. Sci. 54, 321–339 (2009)Google Scholar
  4. 4.
    de Cunha, R.E., Lima, H.M., de Tomi, G.: New approach for reduction of diesel consumption by comparing different mining haulage configurations. J. Environ. Manag. 172, 177–185 (2016)CrossRefGoogle Scholar
  5. 5.
    Fisonga, M., Mutambo, V.: Optimization of the fleet per shovel productivity in surface mining: case study of Chilanga Cement, Lusaka Zambia. Cogent Eng. 4, 1386852 (2017)Google Scholar
  6. 6.
    Pasch, O., Uludag, S.: Optimization of the load-and-haul operation at an opencast colliery. J. S. Afr. Inst. Min. Metall. 118, 449–456 (2018)CrossRefGoogle Scholar
  7. 7.
    Chaowasakoo, P., Seppälä, H., Koivo, H., Zhou, Q.: Improving fleet management in mines: the benefit of heterogeneous match factor. Eur. J. Oper. Res. 261, 1052–1065 (2017)CrossRefGoogle Scholar
  8. 8.
    Amin, I., Adil, M., Rehman, S.U., Ahmad, I.: Simulation of truck-shovel operations using simio. Int. J. Econ. Environ. Geol. 8, 55–58 (2019)Google Scholar
  9. 9.
    Zeng, W.: A simulation model for truck-shovel operation (2018)Google Scholar
  10. 10.
    Ozdemir, B., Kumral, M.: Simulation-based optimization of truck-shovel material handling systems in multi-pit surface mines. Simul. Model. Pract. Theory 95, 36–48 (2019)CrossRefGoogle Scholar
  11. 11.
    Shishvan, M., Benndorf, J.: Simulation-based optimization approach for material dispatching in continuous mining systems. Eur. J. Oper. Res. 275, 1108–1125 (2019)CrossRefGoogle Scholar
  12. 12.
    Yuriy, G., Vayenas, N.: Discrete-event simulation of mine equipment systems combined with a reliability assessment model based on genetic algorithms. Int. J. Min. Reclam. Environ. 22, 70–83 (2008)CrossRefGoogle Scholar
  13. 13.
    Upadhyay, S.P., Askari-Nasab, H.: Simulation and optimization approach for uncertainty-based short-term planning in open pit mines. Int. J. Min. Sci. Technol. 28, 153–166 (2018)CrossRefGoogle Scholar
  14. 14.
    Rist, K.: The solution of a transportation problem by use of a Monte Carlo technique. In: Proceedings of the 1st International Symposium on Computer Application in Mining (APCOM-I), p. L2. Tucson University of Arizona (1961)Google Scholar
  15. 15.
    Deutsch, M., González, E., Williams, M.: Using simulation to quantify uncertainty in ultimate-pit limits and inform infrastructure placement. Min. Eng. 67, 49–55 (2015)CrossRefGoogle Scholar
  16. 16.
    Kumral, M., Sari, Y.A.: Simulation-based mine extraction sequencing with chance constrained risk tolerance. Simulation 93, 527–539 (2017)CrossRefGoogle Scholar
  17. 17.
    Blom, M., Pearce, A.R., Stuckey, P.J.: Short-term planning for open pit mines: a review. Int. J. Min. Reclam. Environ. 33, 318–339 (2019).  https://doi.org/10.1080/17480930.2018.1448248CrossRefGoogle Scholar
  18. 18.
    White, J.W., Olson, J.P., Vohnout, S.I.: On improving truck/shovel productivity in open pit mines. CIM Bull. 86, 43–49 (1993)Google Scholar
  19. 19.
    Suglo, R.S., Al-Hassan, S.: Use of simulation techniques in determining the fleet requirements of an open pit mine. Ghana Min. J. 9 (2007)Google Scholar
  20. 20.
    Ortiz, C.E.A., Curi, A., Campos, P.H.: The use of simulation in fleet selection and equipment sizing in mining. In: Mine Planning and Equipment Selection, pp. 869–877. Springer (2014)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • S. Upadhyay
    • 1
  • M. Tabesh
    • 2
  • M. Badiozamani
    • 3
    Email author
  • H. Askari-Nasab
    • 3
  1. 1.Canadian Natural Resources LimitedCalgaryCanada
  2. 2.Teck Resources LimitedVancouverCanada
  3. 3.School of Mining and Petroleum EngineeringUniversity of AlbertaEdmontonCanada

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