Ranking criteria used for underground mining method selection applying Z-numbers Theory

Document Type : Research Article

Authors

Department of Mining Engineering, Amirkabir University of Technology, Tehran, Iran

Abstract

Due to its nature, mining operations are associated with many uncertainties. The effective factors in selecting the appropriate mining method in underground mines are also associated with uncertainties. The uncertainty associated with these parameters can cause various life-threatening/mortal and financial risks. Considering the risk and uncertainty related to these parameters, ranking and determining their importance, not only helps to choose the best (the safest and the most profitable) mining method before starting the mining process, but also to design a better and safer mine and reducing subsequent risks. Fuzzy parameters are generally estimated through expert knowledge, but the degree of confidence in the opinion of different experts is different and the uncertainty and difference in the reliability of their opinion cannot be ignored. In this research, Z-numbers Theory was used to solve the mentioned challenge. To conduct the present study, first the influencing factors in the selection of underground mining methods were studied and classified into 4 main groups of criteria, 13 sub-criteria 1 and 78 sub-criteria 2. Then, the Z-numbers theory was used to rank and determine their importance. After calculating the final weight of each parameter, in order to check the validity and accuracy of the findings, the results were compared with the parameters considered for choosing the underground mining method in Angouran lead and zinc mine. The results show that in each group of parameters, the more weighted factors (the results of the present research) match the parameters related to choosing the mining method in Angouran mine.

Keywords

Main Subjects

References

[1] S. Gupta, U. Kumar, An analytical hierarchy process (AHP)-guided decision model for underground mining method selection, International journal of mining, reclamation and environment, 26(4) (2012) 324-336.
[2] F.S. Namin, K. Shahriar, A. Bascetin, S. Ghodsypour, Practical applications from decision-making techniques for selection of suitable mining method in Iran, Gospodarka Surowcami Mineralnymi, 25 (2009) 57-77.
[3] S. Alpay, M. Yavuz, A decision support system for underground mining method selection, in:  International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, Springer, 2007, pp. 334-343.
[4] B.C. Balusa, J. Singam, Underground mining method selection using WPM and PROMETHEE, Journal of the Institution of Engineers (India): Series D, 99(1) (2018) 165-171.
[5] Z. Fu, X. Wu, H. Liao, F. Herrera, Underground mining method selection with the hesitant fuzzy linguistic gained and lost dominance score method, IEEE Access, 6 (2018) 66442-66458.
[6] B.C. Balusa, A.K. Gorai, Sensitivity analysis of fuzzy-analytic hierarchical process (FAHP) decision-making model in selection of underground metal mining method, Journal of Sustainable Mining, 18(1) (2019) 8-17.
[7] S. Bajić, D. Bajić, B. Gluščević, V. Ristić Vakanjac, Application of fuzzy analytic hierarchy process to underground mining method selection, Symmetry, 12(2) (2020) 192.
[8] O. Ghazdali, J. Moustadraf, T. Tagma, B. Alabjah, F. Amraoui, Study and evaluation of the stability of underground mining method used in shallow-dip vein deposits hosted in poor quality rock, Mining of Mineral Deposits, 15(3) (2021) 31-38.
[9] M.A. Ali, J.-G. Kim, Selection mining methods via multiple criteria decision analysis using TOPSIS and modification of the UBC method, Journal of Sustainable Mining, 20 (2021).
[10] F.S. Namin, A. Ghadi, F. Saki, A literature review of Multi Criteria Decision-Making (MCDM) towards mining method selection (MMS), Resources Policy, 77 (2022) 102676.
[11] B. Kang, D. Wei, Y. Li, Y. Deng, Decision making using Z-numbers under uncertain environment, Journal of computational Information systems, 8(7) (2012) 2807-2814.
[12] B. Kang, D. Wei, Y. Li, Y. Deng, A method of converting Z-number to classical fuzzy number, Journal of Information &computational Science, 9(3) (2012) 703-709.
[13] L.A. Zadeh, A note on Z-numbers, Information Sciences, 181(14) (2011) 2923-2932.
[14] S. Heidarzadeh, A. Saeidi, A. Rouleau, Use of probabilistic numerical modeling to evaluate the effect of geomechanical parameter variability on the probability of open-stope failure: a case Study of the Niobec Mine, Quebec (Canada), Rock Mechanics and Rock Engineering, 53(3) (2020) 1411-1431.
[15] A. Azadeh, M. Saberi, N.Z. Atashbar, E. Chang, P. Pazhoheshfar, Z-AHP: A Z-number extension of fuzzy analytical hierarchy process, in:  2013 7th IEEE International Conference on Digital Ecosystems and Technologies (DEST), IEEE, 2013, pp. 141-147.
[16] D. Bogdanovic, D. Nikolic, I. Ilic, Mining method selection by integrated AHP and PROMETHEE method, Anais da Academia Brasileira de Ciências, 84 (2012) 219-233.
[17] H. Karimnia, H. Bagloo, Optimum mining method selection using fuzzy analytical hierarchy process–Qapiliq salt mine, Iran, International Journal of Mining Science and Technology, 25(2) (2015) 225-230.
[18] S. Alpay, M. Yavuz, Underground mining method selection by decision making tools, Tunnelling and Underground Space Technology, 24(2) (2009) 173-184.
[19] M. Ataei, M. Jamshidi, F. Sereshki, S. Jalali, Mining method selection by AHP approach, Journal of the Southern African Institute of Mining and Metallurgy, 108(12) (2008) 741-749.
[20] M. Ataei, H. Shahsavany, R. Mikaeil, Monte Carlo Analytic Hierarchy Process (MAHP) approach to selection of optimum mining method, International Journal of Mining Science and Technology, 23(4) (2013) 573-578.
[21] B.C. Balusa, A.K. Gorai, A comparative study of various multi-criteria decision-making models in underground mining method selection, Journal of The Institution of Engineers (India): Series D, 100(1) (2019) 105-121.
[22] H. Dehghani, A. Siami, P. Haghi, A new model for mining method selection based on grey and TODIM methods, Journal of Mining and Environment, 8(1) (2017) 49-60.
[23] M. Iphar, S. Alpay, A mobile application based on multi-criteria decision-making methods for underground mining method selection, International Journal of Mining, Reclamation and Environment, 33(7) (2019) 480-504.
[24] A. Karadogan, A. Kahriman, U. Ozer, Application of fuzzy set theory in the selection of underground mining method, Journal of the Southern African Institute of Mining and Metallurgy, 108(2) (2008) 73-79.
[25] R. Mikaeil, M.Z. Naghadehi, M. Ataei, R. Khalokakaie, A decision support system using fuzzy analytical hierarchy process (FAHP) and TOPSIS approaches for selection of the optimum underground mining method, Archives of Mining Sciences, 54(2) (2009) 341-368.
[26] M.Z. Naghadehi, R. Mikaeil, M. Ataei, The application of fuzzy analytic hierarchy process (FAHP) approach to selection of optimum underground mining method for Jajarm Bauxite Mine, Iran, Expert Systems with Applications, 36(4) (2009) 8218-8226.
[27] G. Popovic, B. Djordjevic, D. Milanovic, Multiple criteria approach in the mining method selection, Industrija, 47(4) (2019).
[28] M. Yavuz, The application of the analytic hierarchy process (AHP) and Yager’s method in underground mining method selection problem, International Journal of Mining, Reclamation and Environment, 29(6) (2015) 453-475.
[29] A. Yazdani-Chamzini, S. Haji Yakchali, E. Kazimieras Zavadskas, Using a integrated MCDM model for mining method selection in presence of uncertainty, Economic research-Ekonomska istraživanja, 25(4) (2012) 869-904.
[30] S.M. Lavasani, A. Zendegani, M. Celik, An extension to Fuzzy Fault Tree Analysis (FFTA) application in petrochemical process industry, Process Safety and Environmental Protection, 93 (2015) 75-88.
[31] L.M. MIRI, J. Wang, Z. Yang, J. Finlay, Application of fuzzy fault tree analysis on oil and gas offshore pipelines,  (2011).
[32] V. Renjith, G. Madhu, V.L.G. Nayagam, A. Bhasi, Two-dimensional fuzzy fault tree analysis for chlorine release from a chlor-alkali industry using expert elicitation, Journal of hazardous materials, 183(1-3) (2010) 103-110.
[33] C.-L.H. Shu-Jen Chen, Fuzzy Multiple Attribute Decision Making, Springer Berlin, Heidelberg, Berlin, 1992.
[34] A. Mottahedi, M. Ataei, Fuzzy fault tree analysis for coal burst occurrence probability in underground coal mining, Tunnelling and Underground Space Technology, 83 (2019) 165-174.
[35] R.T. Clemen, R.L. Winkler, Combining probability distributions from experts in risk analysis, Risk analysis, 19(2) (1999) 187-203.
[36] K. Consultant, Final technical report of Angouran mine planning and design (the sulphur section of the mine), 2008.
Amirkabir Journal of Civil Engineering
Volume 54, Issue 12
March 2023
Pages 4639-4656

Files

Share

How to cite

Statistics