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Doctor of Philosophy (PhD)
Methane gas management continues to be a challenge concerning underground coal mine safety and productivity worldwide despite the extraordinary effort of the mining industry, governmental agencies, and academia to develop new technologies to monitor and control methane gas emissions more efficiently. The risk of hazardous methane gas concentrations in underground environments cannot be underestimated. Statistical data for the last 100 years indicate that around 80% of the accidents and 90% of the fatalities in the underground coal mining industry in the US were related to methane gas explosions.
Modern underground mine operations monitor and evaluate atmospheric parameters such as barometric pressure, temperature, gas concentrations, and ventilation parameters (e.g., fan performance and airflow) by means of Automated Atmospheric Monitoring Systems, which use sensors that collect a massive amount of data implemented by mine operators to make decisions concerning mine safety and operate ventilation systems more effectively. In addition, however, some of these data can be statistically studied to develop forecast models to help improve the safety and health parameters of underground coal mining operations.
The research presented in this dissertation investigates potential correlations between methane gas concentrations and independent variables such as barometric pressure and coal production rate to build reliable forecasting models capable of predicting future concentrations of methane gas, mainly based on time series data collected by the Atmospheric Monitoring System of three active underground coal mining operations in the eastern US and weather data retrieved from public weather stations in the proximity of the case studies. The mine and weather data were stored and pre-processed using an Atmospheric Monitoring Analysis and Database Management system explicitly designed to manage Atmospheric Monitoring Systems data. Furthermore, various statistical techniques were implemented to assess the potential association (e.g., autocorrelation and cross-correlation) between methane gas concentration time series and the independent variables. Such associations were employed to develop univariate and multivariate forecasting models for methane gas emissions in underground coal mines. Finally, the optimal model is selected using the Akaike Information Criterion, and the results obtained from the different forecast approaches (univariate and multivariate) are compared using cross-validation metrics to determine the best model.
It was concluded that the ARIMA, VAR, and ARIMAX methane gas forecasting methodologies proposed in this research can accurately predict methane gas concentrations in underground coal mines operations. The methane gas forecasted from the models matched the validation data consistently, and their linear correlation was positive and strong in most cases. In addition, the 95% confidence interval consistently captured the forecast and validation data.
KEYWORDS: Underground coal mining industry, methane gas, time series analysis, univariate and multivariate forecasting.
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This study was sponsored by the Alpha Foundation for the Improvement of Mine Safety and Health, Inc. (ALPHA FOUNDATION), contract number AFCTG20-103. The views, opinions, and recommendations expressed herein are solely those of the authors and do not imply any endorsement by the ALPHA FOUNDATION, its Directors, and staff.
Diaz, Juan, "DEVELOPMENT OF UNIVARIATE AND MULTIVARIATE FORECASTING MODELS FOR METHANE GAS EMISSIONS IN UNDERGROUND COAL MINES" (2022). Theses and Dissertations--Mining Engineering. 70.