Risk assessment of gas explosionin coal mines based on fuzzy Bayesian network

In order to quantify the risk of coal mine gas explosion and solve the deficiency problem in dealing with uncertainty in risk analysis, a risk assessment method of gas explosion using the Bayesian network and fuzzy set theory is proposed. Firstly, the main factors influencing gas explosion are identified based on expertise, and the topology models of gas explosion, gas overrun, and ignition source are built respectively. At the same time, the prior and conditional probabilities of risk factors are evaluated by triangular fuzzy number based on fuzzy set theory. Then, the probability of gas explosion is calculated using forward causal inference, and the formation mechanism of gas explosion is analyzed with reverse diagnosis inference, so as to identify the most likely risk factors quickly. Finally, the key risk factors affecting gas explosion are found with sensitivity analysis based on the Bayesian importance analysis. The case study shows that the probability of gas explosion in the working face of a mine in Jilin Province is 5.5%, which is a small probability event. However, when the underground production conditions change, especially when multiple risk factors occur at the same time, the risk level of gas explosion increase significantly. Through causal inference, the authors can determine whether the risk level of gas explosion is within the acceptable range. The posterior probability of ventilation resistance and ventilation failure exceed 15% by reverse diagnosis inference. Gas explosion is sensitive to these two risk factors and an attention should be paid to the important role of mine ventilation system in mine production unit. At the same time, electric spark and coal spontaneous combustion in ignition source are the key risk factors of gas explosion based on sensitivity analysis. The assessment method can provide a technical guidance for decision makers to effectively manage the risk of gas explosion in coal mine.