پیوندهای مفید
آمار
| تعداد نشریات | 8 |
| تعداد شمارهها | 440 |
| تعداد مقالات | 5,680 |
| تعداد مشاهده مقاله | 7,821,035 |
| تعداد دریافت فایل اصل مقاله | 6,392,481 |
کاربرد تئوری اعداد در رتبهبندی معیارهای تاثیرگذار در انتخاب روشهای استخراج زیرزمینی | ||
| نشریه مهندسی عمران امیرکبیر | ||
| مقاله 10، دوره 54، شماره 12، اسفند 1401، صفحه 4639-4656 اصل مقاله (1.94 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22060/ceej.2022.20885.7562 | ||
| نویسندگان | ||
| زینب جهانبانی؛ مجید عطائی پور* ؛ علی مرتضوی | ||
| دانشکده مهندسی معدن، دانشگاه صنعتی امیرکبیر، تهران، ایران | ||
| چکیده | ||
| عملیات معدن کاری با توجه به ماهیت آن با عدم قطعیت های زیادی همراه است و فاکتورهای موثر در انتخاب روش مناسب استخراج برای معادن زیرزمینی نیز با عدم قطعیت همراه هستند. عدم قطعیت همراه با این پارامترها می تواند سبب به وجود آمدن ریسک های مختلف جانی و مالی شود. در نظر گرفتن ریسک و عدم قطعیت موجود در پارامترها، رتبه بندی و تعیین میزان اهمیت آنها، علاوه بر کمک به انتخاب بهترین (ایمن ترین و سودآورترین) روش استخراج قبل از شروع فرآیند معدن کاری، می تواند به طراحی بهتر و ایمن تر معدن نیز کمک کرده و باعث کاهش ریسک های متعاقب شود. برآورد پارامترهای فازی عموما بر اساس دانش خبرگان صورت می گیرد، اما میزان اطمینان به نظر کارشناسان مختلف تفاوت دارد و نمیتوان عدم قطعیت و تفاوت در اعتبار نظر آنان را نادیده گرفت. برای حل چالش ذکر شده، در این تحقیق از تئوری اعداد Z استفاده شد. برای انجام مطالعه حاضر، ابتدا فاکتورهای موثر در انتخاب روش های استخراج زیرزمینی مطالعه و در 4 گروه اصلی، 13 زیرمعیار-۱ و ۷۸ زیرمعیار-۲ دسته بندی شدند. سپس به منظور بررسی و تعیین میزان اهمیت آنها از تئوری اعداد Z استفاده شد. پس از محاسبه وزن نهایی هر پارامتر، به منظور بررسی اعتبار و سنجش صحت یافته ها، نتایج حاصل از مطالعه با پارامترهای در نظر گرفته شده برای انتخاب روش استخراج زیرزمینی در معدن سرب و روی انگوران مقایسه شد. بررسی ها نشان میدهد که در هر گروه از پارامترها، فاکتورهایی که وزن بیشتری را دارا بودند (نتایج حاصل از تحقیق حاضر)، با پارامترهای در نظر گرفته شده برای انتخاب روش استخراج در معدن انگوران مطابقت دارند. | ||
| کلیدواژهها | ||
| معیارهای تاثیرگذار؛ انتخاب روش استخراج زیرزمینی؛ عدم قطعیت؛ اعداد فازی؛ تئوری اعداد Z | ||
| موضوعات | ||
| استخراج معدن | ||
| عنوان مقاله [English] | ||
| Ranking criteria used for underground mining method selection applying Z-numbers Theory | ||
| نویسندگان [English] | ||
| Zeinab Jahanbani؛ Majid Ataee-pour؛ Ali Mortazavi | ||
| Department of Mining Engineering, Amirkabir University of Technology, Tehran, Iran | ||
| چکیده [English] | ||
| 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. | ||
| کلیدواژهها [English] | ||
| Influencing criteria, Underground mining method selection, Uncertainty, Fuzzy numbers, Z-numbers theory | ||
| مراجع | ||
|
[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. | ||
|
آمار تعداد مشاهده مقاله: 1,022 تعداد دریافت فایل اصل مقاله: 1,254 |
||