IoT-Enabled Methane Monitoring and LSTM-Based Forecasting System for Enhanced Safety in Underground Coal Mining

Ensuring safety in the mining industry is a critical concern for a nation's industrial advancement. Industry 4.0, characterized by the integration of advanced technologies, is at the forefront of efforts to enhance mining practices. Coal seams contain a range of hydrocarbon gases, predominantly methane, which is released in significant quantities during mining operations. Effectively mitigating methane emissions is imperative. The inclusion of methane forecasting allows for the early identification of potential methane emissions, hence results in significance enhancement in mine safety. The research work is focused on real-time remote monitoring and cloud-based forecasting of methane levels in underground coal mines. An Industrial Internet of Things (IIoT) device is developed for data acquisition in underground coal mines, capturing essential parameters such as methane concentration, temperature, and humidity. The collected data are utilized to train LSTM based multivariate forecasting model. The trained model is subsequently deployed in the cloud. The experiment is performed in a mine of Eastern Coalfields Limited, India. After the deployment of the proposed model, the developed IIoT device transmits real-time data, obtained from the mine, to the cloud. Based on the real time data, our model conducts methane forecasting and communicates results back to the IIoT device. The device issues immediate alerts when methane levels surpass predefined thresholds. This ensures enhanced safety in mining operations by providing warnings for both current and forecasted methane concentrations. The forecasted methane concentrations, along with real-time data, are accessible through mobile applications and a web-based dashboard. The accuracy of the proposed model is measured by mean absolute error, mean absolute percentage error and root mean square error, which demonstrate values of 156.95 ppm, 4.23% and 191.53 ppm respectively. A comparative study is performed where our model is evaluated against the multivariate Multilayer Perceptron (MLP), Vector autoregression (VAR) and Auto-Regressive Integrated Moving Average (ARIMA) models. The comparative study demonstrates that our developed model outperforms the others, showing superior results.