Determinants of prioritised influencing factors on coal spontaneous combustion propensity – A Fuzzy-Delphi-geometric mean analytic hierarchy process

Abstract

One of the most common hazards in coal mines is spontaneous combustion, a significant cause of coal loss in underground mines. Specifying a suitable approach for the determinants of prioritized influencing factors on coal spontaneous combustion propensity is challenging. There needs to be more research that directly addresses the uncertainty of human judgement in assessing the factors influencing coal propensity to self-combustion. This is the first attempt to address the aforesaid problem by ranking the factors influencing spontaneous coal combustion. The ranking was performed using the Fuzzy Delphi Analytic Hierarchy Process (FDAHP) with two variants of the Geometric Mean Method (GMM), which enabled the analysis of both quantitative and qualitative data related to the rating of each factor influencing spontaneous combustion risk and the relative importance of each factor related to coal seam characteristics, geological aspects, mining practices, and environmental aspects. The results show that ‘GMM as a whole’ is a comparatively best-aggregating approach to the AHP process. The presented case study of using this methodology in underground mine conditions confirms its efficacy in assessing susceptibility to coal spontaneous combustion. This mainly concerns the pace and reliability of the obtained results and the determination of risk evaluation system based on intrinsic and extrinsic factors influencing susceptibility to spontaneous combustion of coal. Finally, this systematic technique was validated in the research area by utilizing spontaneous combustion incident that occurred in Jharia and Raniganj coalfields, which revealed rather excellent concordance in Jharia and Raniganj coalfields. The study can be used in mine planning to develop engineering data to select the operational parameter and prevent the initiation of spontaneous heating by identifying intrinsic factors and optimizing extrinsic factors.