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Selecting the best mining method using analytical and numerical methods

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Abstract

Selection of the most optimal method of mining in the stage of designing the mine is considered to be an important and sensitive issue as far as designing the system of exploitation of a mine is concerned. This selection is based on geological, geotechnical, geographical, economic, social and political studies, etc. recognizing all of the factors that impact the method selection and determining the size of effect of each of these factors is not easily possible. The purpose of selecting the optimum extraction method in the first stage of designing a mine is to select a method that is as compatible as possible with the storage conditions and external factors such as economy, the budget that has been assigned to this project, and political, social and local conditions. In this respect, the researcher developed numerical and analytical methods for selecting a method for the extraction of mineral resources. Numerical methods are based on scoring parameters that are indicative of the condition of mineral resources. On the other hand, the analytical methods have utilized the decision-making methods in management sciences. The parameters that affected the decision making associated with the extraction method were not precise and they can be put in fuzzy sets. In this article, the shortcomings and defects of old quantitative numerical methods, such as UBS and Nicholas method, have been reviewed and using fuzzy AHP and fuzzy TOPSIS methods, which are multi-criteria analytical methods, the best method of extraction of copper from Qaleh Zari copper mine was selected.

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References

  • Adler, L., & Thompson, S. D. (1992). Mining method classification systems, SME Mining Engineering Hand-Book (pp. 531–537). Society for Mining Engineering, Metallurgy and Exploration, Inc.

    Google Scholar 

  • Afradi, A., Alavi, I., & Moslemi, M. (2020). Selecting the most suitable method for extracting construction materials in Iran through the fuzzy multi-attribute decision-making model. Journal of the Institution of Engineers (india): Series D. https://doi.org/10.1007/s40033-020-00239-w

    Article  Google Scholar 

  • Alavi, I., & Alinejad, R. H. (2011a). Comparison of fuzzy AHP and fuzzy TOPSIS methods for plant species selection (case study: Reclamation plan of Sungun Copper Mine; Iran). Australian Journal of Basic and Applied Sciences, 5(12), 1104–1113.

    Google Scholar 

  • Alavi, I., Alinejad, R. H., & Sadegh Zadeh, M. (2011b). Prioritizing crescive plant species in Choghart Iron Mine Desert Region (used method: Fuzzy AHP). Australian Journal of Basic and Applied Sciences, 5(12), 1075–1078.

    Google Scholar 

  • Alpay, S., & Yavuz, M. (2009). Underground mining method selection by decision making tools. Tunnelling and Underground Space Technology, 24(2), 173–184.

    Article  Google Scholar 

  • Balusa, B. C., & Singam, J. (2017). Underground mining method selection using WPM and PROMETHEE. Journal of the Institution of Engineers (india) Series D, 99, 1–7.

    Google Scholar 

  • Bitarafan, M. R., & Ataei, M. (2004). Mining method selection by multiple criteria decision making tools. Journal of the South African Institute of Mining and Metallurgy, 104(9), 493–498.

    Google Scholar 

  • Boshkov, S. H., & Wright, F. D. (1973). Basic and parametric criteria in the selection, design and development of underground mining systems. In A. B. Cummins & I. A. Given (Eds.), SME mining engineering handbook (Vol. 1, pp. 12.2-12.13). New York: SME-AIME.

    Google Scholar 

  • Buckley, J. J. (1985). Fuzzy hierarchical analysis. Fuzzy Sets and System, 17(3), 233–247. https://doi.org/10.1016/0165-0114(85)90090-9

    Article  Google Scholar 

  • Chang, D. Y. (1996). Applications of the extent analysis method on fuzzy AHP. European Journal of Operational Research, 95, 649–655. https://doi.org/10.1016/0377-2217(95)00300-2

    Article  Google Scholar 

  • Chen, C. T. (2000). Extensions of the TOPSIS for group decision making under fuzzy environment. Fuzzy Sets and Systems, 114, 1–9. https://doi.org/10.1016/S0165-0114(97)00377-1

    Article  Google Scholar 

  • Chu, M.-T., Shyu, J., Tzeng, G.-H., & Khosla, R. (2007). Comparison among three analytical methods for knowledge communities’ group-decision analysis. Expert Systems with Applications, 33(4), 1011–1024. https://doi.org/10.1016/j.eswa.2006.08.026

    Article  Google Scholar 

  • Chu, T. C. (2002). Selecting plant location via a fuzzy TOPSIS approach. The International Journal of Advanced Manufacturing Technology, 20, 859–864. https://doi.org/10.1007/s001700200227

    Article  Google Scholar 

  • Hartman, H. L. (1987). Introductory mining engineering. Wiley.

    Google Scholar 

  • Hwang, C. L., & Yoon, K. (1981). Multiple attribute decision making methods and applications. Springer.

    Book  Google Scholar 

  • Kabassi, K., & Virvou, M. (2004). A technique for preference ordering for advice generation in an intelligent help system. In Proceeding of the 2004 IEEE International Conference on System. https://doi.org/10.1109/ICSMC.2004.1400857.

  • Kacprzyk, J. (1986). Group decision making with a fuzzy linguistic majority. Fuzzy Sets and System, 18(2), 105–118. https://doi.org/10.1016/0165-0114(86)90014-X

    Article  Google Scholar 

  • Karadogan, A., Bascetin, A., Kahriman, A., & Gorgun, S. (2001). A new approach in selection of underground mining method. In Int. Conf. Modern Management Mine Producing Geol. Env. Protection, Varna, Bulgaria, June 3–9, 2001. pp. 171–184.

  • Kaufmann, A., & Gupta, M. M. (1985). Introduction to fuzzy arithmetic: Theory and applications. Van Nostrand Reinhold Co.

    Google Scholar 

  • Khalou Kakayi, R., Atayi, M., Javanshir, M., & Jahanshahi, H. (2006). Mining method selection for Ghale-Zari copper deposit in Birjand with the use of analytical hierarchy process. International Journal of Industrial Engineering and Production Management (IJIE), 17(3), 37–45.

    Google Scholar 

  • Laubscher, D. H. (1981). Selection of mass underground mining methods. In D. Stewart (Ed.), Design and operation of caving and sublevel stoping mines (pp. 23–38). SME-AIME.

    Google Scholar 

  • Miller, D. (2003). Indigenous copper mining and smelting in pre-colonial southern Africa. In Craddock, P. & Lang, J. (Eds.) Mining and metal production through the ages. London: British Museum, pp 101–110

  • Miller, T. L., Pakalnis, R., & Poulin, R. (1995). UBC mining method selection. University of British Columbia.

    Google Scholar 

  • Mohammadian, M. (2017). Modelling, control and prediction using hierarchical fuzzy logic systems: Design and development. The International Journal of Fuzzy System Applications (IJFSA), 6(3), 105–123.

    Article  Google Scholar 

  • Morrison, R. G. K. (1976). A philosophy of ground control (pp. 125–159). McGill University.

    Google Scholar 

  • Nicholas, D. E. (1981). Method selection—A numerical approach. In D. Stewart (Ed.), Design and operation of caving and sublevel stoping mines, Chap. 4 (pp. 39–53). SME-AIME.

    Google Scholar 

  • Nicholas, D. E. (1992). Selection method. In H. L. Hartman (Ed.), SME mining engineering handbook (2nd ed., pp. 2090–2106). Society for Mining Engineering, Metallurgy and Exploration Inc.

    Google Scholar 

  • Saaty, T.L. (1980) The Analytic Hierarchy Process. McGraw-Hill, New York

  • Saaty, T. L. (2001). Decision making for leaders: The analytic hierarchy process for decisions in a complex world. RWS Publications.

    Google Scholar 

  • Wang, W., Liu, X., Ma, Y., et al. (2021). A new approach for occupational risk evaluation of natural gas pipeline construction with extended cumulative prospect theory. The International Journal of Fuzzy System Applications (IJFSA). https://doi.org/10.1007/s40815-020-01038-x

    Article  Google Scholar 

  • Yang, F., Mu, N., Liao, X., et al. (2021). EA-HUFIM: Optimization for fuzzy-based high-utility itemsets mining. The International Journal of Fuzzy System Applications (IJFSA). https://doi.org/10.1007/s40815-020-01003-8

    Article  Google Scholar 

  • Yazdi, M., Khan, F., Abbassi, R., & Rusli, R. (2020). Improved DEMATEL methodology for effective safety management decision-making. Safety Science, 127, 104705. https://doi.org/10.1016/j.ssci.2020.104705

    Article  Google Scholar 

  • Yen, J., & Langari, R. (1999). Fuzzy logic: Intelligence, control, and information. (Vol. 1). Prentice Hall.

    Google Scholar 

  • Zadeh, L. A. (1965a). Fuzzy sets. Information and Control, 8(3), 338–353. https://doi.org/10.1016/S0019-9958(65)90241-X

    Article  Google Scholar 

  • Zhang, H. J., Zhou, Y., & Gan, Q. H. (2019). An extended PROMETHEE-II-based risk prioritization method for equipment failures in the geothermal power plant. The International Journal of Fuzzy System Applications (IJFSA), 21(8), 2490–2509. https://doi.org/10.1007/s40815-019-00679-x

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Mining and Geology, Qaemshahr Branch, Islamic Azad University, Qaemshahr, Iran

    Alireza Afradi & Iraj Alavi

  2. Department of Environmental Engineering, Qaemshahr Branch, Islamic Azad University, Qaemshahr, Iran

    Mahdieh Moslemi

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  1. Alireza Afradi
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  2. Iraj Alavi
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  3. Mahdieh Moslemi
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Correspondence to Alireza Afradi.

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Communicated by Mauro Cesar Geraldes

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Afradi, A., Alavi, I. & Moslemi, M. Selecting the best mining method using analytical and numerical methods. J. Sediment. Environ. 6, 403–415 (2021). https://doi.org/10.1007/s43217-021-00063-6

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  • DOI: https://doi.org/10.1007/s43217-021-00063-6

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