Risk assessment of gas explosion in coal mines based on fuzzy AHP and bayesian network

Abstract

Gas explosion is one of the most deadly hazards in underground coal mining. Risk assessment has played an effective role in avoiding gas explosions and revising coal mine regulations. However, the traditional methods are deficient in quantitative evaluation, dynamic control and dealing with uncertainty. In this paper, a method of quantitative assessment the risk of gas explosion in underground coal mine using Bayesian network was proposed. A fuzzy analytic hierarchy process (FAHP) method based on subjective and objective information of experts was developed in the process of fuzzification. Through the Bayesian inference, the probability of occurrence of potential risk events and the probability distribution of risk factors can be calculated in real time according to on prior knowledge and evidence updating. Meanwhile, the most likely potential causes of accidents can be determined. A sensitivity analysis technique was utilized to investigate the contribution rate of each risk factor to a risk event, so as to determine the most critical risk factor. Taking Babao Coal Mine in China as the case, this study conducted a gas explosion risk assessment. The results show that the mothed of fuzzy AHP and Bayesian Network is feasible and applicable. It can be used as a decision-making tool to prevent coal mine gas explosions and provide decision makers with a technical guide for managing the coal mine gas explosion risk.