Mining Method Selection (MMS) is the first and the most critical problem in mine design, and it depends on some parameters such as geometric and geological features and economic factors.
The ultimate goals of mining method selection are maximizing profit, enhancing mining recovery rate and providing a safe mining environment.
Selection of an appropriate mining method is a complex task that requires consideration of many technical, economic, social, and historical factors.
Pak Myong Chun, a section head at the Faculty of Mining Engineering, determined the factors affecting MMS with the help of some mining experts, and selected the most suitable mining method using the hesitant fuzzy group decision-making (HFGDM) and technique for order performance by similarity to the ideal solution (TOPSIS). These factors included type of deposit, slope of deposit, thickness of orebody, depth below the surface, grade distribution, hanging wall Rock Mass Rating (RMR), footwall RMR, ore body RMR, recovery, capital cost, mining cost, annual productivity, and environmental impact.
Firstly, he proposed a group decision-making (GDM) method to determine the weights of several attributes based on the score function with decision-makers’ weights, in which the n-dimensional hesitant fuzzy environment takes the form of hesitant fuzzy sets (HFS). Then, he calculated the weights of these factors using the HFGDM method. He compared seven mining methods for an apatite mine to select the optimal mining method using the TOPSIS method.
The results showed that the sub-level stoping method with priority of 0.811 3 was the best for the studied mine.
For more information, please refer to his paper “Suitable Mining Method Selection using HFGDM-TOPSIS Method: a Case Study of an Apatite Mine” in “Journal of Mining and Environment” (EI).
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