Advancements in machine learning techniques for coal and gas outburst prediction in underground mines

Abstract

Coal and gas outbursts are a major cause of fatalities in underground coal mines and pose a threat to coal power generation worldwide. Among the current mitigation efforts include monitoring methane gas levels using sensors, employing geophysical surveys to identify geological structures and zones prone to outbursts, and using empirical modeling for outburst predictions. However, in the wake of industry 4.0 technologies, several studies have been conducted on applying artificial intelligence methods to predict outbursts. The proposed models and their results vary significantly in the literature. This study reviews the application of machine learning (ML) to predict coal and gas outbursts in underground mines using a mixed-method approach. Most of the available literature, with a focus on ML applications in coal and gas outburst prediction, was investigated in China. Findings indicate that researchers proposed diverse ML models mostly combined with different optimization algorithms, including particle swarm optimization (PSO), genetic algorithm (GA), rough set (RS), and fruit fly optimization algorithm (IFOA) for outburst prediction. The number and type of input parameters used for prediction differed significantly, with initial gas velocity being the most dominant parameter for gas outbursts, and coal seam depth as the dominant parameter for coal outbursts. The datasets for training and testing the proposed ML models in the literature varied significantly but were mostly insufficient - which questions the reliability of some of the applied ML models. Future research should investigate the effect of data size and input parameters on coal and gas outburst prediction.