Application of an integrated decision-making approach based on FDAHP and PROMETHEE for selection of optimal coal seam for mechanization; A case study of the Tazareh coal mine complex, Iran

Document Type: Research Paper

Authors

1 School of Mining, College of Engineering, University of Tehran, Iran

2 Faculty of Mining and Metallurgy, Yazd University, Yazd, Iran

3 School of Mining, College of Engineering, University of Tehran, Tehran, Iran

Abstract

Increasing the production rate and minimizing the related costs, while optimizing the safety measures, are nowadays’ most important tasks in the mining industry. To these ends, mechanization of mines could be applied, which can result in significant cost reductions and higher levels of profitability for underground mines. The potential of a coal mine mechanization depends on some important factors such as seam inclination and thickness, geological disturbances, seam floor and roof conditions. Mechanization of underground mines requires substantial investments. Therefore, thorough inspection of pertaining aspects is of highest importance before a final decision. The main aim of this study is to develop a new approach to rank the mechanization potential of different coal seams in the Tazareh coal mine complex based on multi-criteria decision-making methods. In fact, a decision-making approach is an effective tool for dealing with complex decision-making processes, and the obtained results may aid the decision maker to determine the priorities and make the best decision. To this end, an integrated Fuzzy Delphi Analytical Hierarchy Process (FDAHP) - PROMETHEE method was utilized to rank coal stopes from the best to the worst. Among different coal seams, K19 was selected as the optimal alternative for mechanization of the Tazareh coal mine complex. In addition, in order to investigate the effects of the pertaining factors on the final decision, a sensitivity analysis was performed. The results obtained from sensitivity analysis showed that K19 with 71.4% of votes had the highest potential for mechanization.

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