Quantitative risk assessment of coal mine gas explosion based on a Bayesian network and computational fluid dynamics

Abstract

A gas explosion is one of the most serious coal mine accidents. The complexity and dynamics of gas explosion accidents, resulting from both multiple risk factors and time-dependent physical parameters, necessitates the use of risk assessment techniques to analyze the accident. A quantitative risk assessment method is proposed for coal mine gas explosions based on a Bayesian Network (BN) and Computational Fluid Dynamics (CFD). The results show that, in addition to the problems of “low vigilance” and “substandard safety education”, the “irrational design of mine gas extraction” has become another major risk factor at the new stage of development of mine excavation technology. Three typical gas explosion areas are selected to analyze the possible consequences of an accident. Based on the probabilistic risk assessment model, time-dependent physical parameters such as accident conditions, location and time are analyzed. The probability of occurrence of time-dependent risk factors is quantified to calculate the risk values of the consequences of explosion accidents corresponding to different times. The proposed method helps to improve the calculation accuracy of updating the risk probability value over time and provides theoretical support for the risk analysis of coal mine gas explosion accidents.