Risk Assessment of Methane Gas Explosions in Underground Coal Mines Using Bayesian Belief Networks

Abstract

Currently, numerous accidents occur during underground coal mining, and the safety management standards of coal mines remain inadequate. Methane gas explosions are among the most severe and catastrophic incidents in underground coal mines. The sudden release and explosion of coal gas during underground mining can cause significant damage to ventilation systems, equipment, and miners. Despite the availability of control and prevention technologies, the frequency of sudden coal and gas releases tends to increase with greater mining depth and operational progress. This paper aims to identify the main causes of gas explosions and to develop a probabilistic risk assessment model for gas explosions during underground coal mining. To achieve this, the key factors contributing to methane explosions during mining operations were identified, and the relationships among these factors were established. Subsequently, by constructing a network of events leading to accidents and employing Bayesian belief networks, the overall risk of explosion was evaluated. The proposed model was applied and analyzed in a case study of the Parvadeh Tabas Coal Mine in Iran. The results indicate that the mining environment and ventilation equipment are the most critical factors for gas accumulation, while fire and electrical sparks are the primary contributors to gas ignition. Additionally, the application of the proposed model to assess explosion risk in the Tabas Coal Mine revealed that ventilation equipment and machinery have the highest risk priority numbers for gas accumulation and gas ignition, respectively.