Abstract
Mining operations provide the coal required to satisfy more than 36% of the electricity demand worldwide. Coal mining releases methane gas which constitutes a significant risk for the safety of coal miners working underground. Therefore, early warning of rising methane gas concentrations is critical to preventing accidents and loss of life. The prediction of methane concentration is complicated by its dependence on many factors and the presence of stochastic fluctuations. This paper introduces a new forecasting approach for methane gas emissions in underground coal mines. The proposed approach employs univariate and multivariate time series forecasting methods. Multivariate methods incorporate barometric pressure as a predictor of gas concentration. The data used herein were collected from the Atmospheric Monitoring Systems of three active underground coal mines in the eastern USA. The performance of three time series methods is compared: the univariate autoregressive integrated moving average (ARIMA), the multivariate vector autoregressive (VAR), and ARIMA with exogenous inputs (ARIMAX). The optimal model per method (ARIMA, VAR, ARIMAX) is selected based on the Akaike Information Criterion. The forecasting performance is assessed using cross-validation to determine the best overall model. It is concluded that all three methods can, in most cases, satisfactorily predict methane gas concentrations in underground coal mines.
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The data used in this paper have been provided by mining companies and they will not be shared with third parties due to non-disclosure agreement.
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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.
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Diaz, J., Agioutantis, Z., Hristopulos, D.T. et al. Forecasting of methane gas in underground coal mines: univariate versus multivariate time series modeling. Stoch Environ Res Risk Assess 37, 2099–2115 (2023). https://doi.org/10.1007/s00477-023-02382-8
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DOI: https://doi.org/10.1007/s00477-023-02382-8