Research Progress on Gas Concentration Prediction Methods in Coal Mine Working Faces

Abstract

Gas concentration prediction in coal mine working faces is a crucial aspect of ensuring safe coal mine production. This paper systematically reviews the current research status of gas concentration prediction methods in coal mine working faces, encompassing traditional prediction methods, artificial intelligence-based prediction methods, multi-source data fusion prediction methods, and prediction methods for special environments. It analyzes the advantages, disadvantages, and applicable scenarios of each method, pointing out existing issues in current research, such as insufficient data quality and quantity, weak model generalization ability, and high real-time requirements. Finally, it provides an outlook on future research directions, suggesting the strengthening of interdisciplinary integration, the development of new sensor technologies, and the construction of intelligent prediction platforms to enhance the accuracy and reliability of gas concentration prediction and provide robust safeguards for safe coal mine production.