Use of machine learning to detect dangerous level of coal mine methane (CMM) concentrations during underground mining operations

Abstract

Underground coal mining is considered to be a highly dangerous activity and has been responsible for large amounts of accidents, causing the death of many mine workers. One of the factors responsible for the fatal aspect of underground coal mining is the presence and accumulation of toxic gases during underground mining operations. This paper focused its investigation specifically on coal mine methane (CMM), which is released as a result of the extraction of coal and the disturbance inflicted to surrounding rock formations during deep mining operations. Methane is considered a highly dangerous gas as it holds the capacity to cause explosions due to its highly inflammable nature. It can also displace oxygen, which eventually leads to asphyxiation. This research was based on the use of machine learning models to successfully predict dangerous concentrations of methane over the authorized threshold. Those predictions were made from a dataset containing information on the temperature, airflow, humidity, pressure and methane concentration in an underground coal mine. The temperature, airflow, humidity and pressure measurements were recorded by a series of sensors, namely anemometers and component sensors THP2/93. Three machine learning classification models were implemented and compared, with the objective to find the best model to predict and detect dangerous levels of coal mine methane. The models that were investigated included naïve Bayes, logistic regression and artificial neural networks (ANNs). This paper concludes with an engineering decision matrix that illustrates the precision of these models in predicting and detecting dangerous levels of methane concentrations in underground mines. Furthermore, recommendations for capacity improvement towards successfully predicting and detecting dangerous levels of coal mine methane from an artificial intelligence’s perspective are provided.