Explosions in underground mining due to methane gas have been the leading cause of incidents and fatalities in the mining industry. The main objective of this research is to develop a forecasting methodology for methane gas emissions based on time series analysis. Methane time series data were retrieved from atmospheric monitoring systems (AMS) of three underground coal mines in the United States. The AMS data were preprocessed and statistically evaluated to explore the potential autocorrelation of methane gas. It was concluded that the autoregressive integrated moving average (ARIMA) time series model used in the one-step-ahead forecasting mode provides accurate estimates that match the increase or decrease of the methane gas emission data.
Full-text paper:
Mining, Metallurgy & Exploration (2022) 39:1961–1982, https://doi.org/10.1007/s42461-022-00654-5
