Gas Overrun Risk Assessment in Coal Mining Face Based on Fuzzy Bayesian Network

Abstract

A risk assessment technology based on a fuzzy Bayesian network is proposed to improve the degree of gas control management in the coal mining face and avoid gas overrun. Five characteristics of the coal mining working face, i.e., geological structure, ventilation conditions, gas extraction, mining activities, and coal mine management affecting the coal mining working face, were examined for 17 risk variables and a gas overrun assessment model was created. A priori information and sample data indicate that there is a 3% chance of gas overflow. The reverse reasoning test found that the main reasons for gas overrun were unreported gas anomalies, ventilation modes, gas content in coal seams, goaf extraction volume, and coal mining rate in coal mining face. The research results show that the approach can assess the risk of gas overflow in coal mining face.