Development and Application of Coal and Gas Outburst Monitoring and Early Warning System in Coal Mines

Abstract

 In view of the technical challenges faced by Pingmei No.13 Mine, such as frequent coal and gas outburst disasters, insufficient accuracy, and poor timeliness of traditional prediction methods, a coal and gas outburst monitoring and early warning system integrating multi-source data acquisition, intelligent algorithm analysis, and hierarchical early warning and response has been developed by considering the mine's geological conditions and actual production conditions. This system adopts a hierarchical and distributed technical architecture, deeply integrating key influencing factors including gas geology, gas emission, daily prediction, outburst prevention measures, and mining disturbances. Based on big data mining and machine learning algorithms, an intelligent early warning model is constructed. A multi-level early warning mechanism coupling state early warning and trend early warning is innovatively established, and efficient interaction and display of early warning information are achieved through visualization technology. Field application results show that the system achieves an average state early warning accuracy of 95.94% and an average trend early warning accuracy of 97.09%. It significantly enhances the refinement and intelligence level of mine outburst prevention management, effectively reduces the risk of coal and gas outburst accidents, and provides reliable technical support for the safe production of high-gas outburst mines.