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Methane Concentration Prediction Method Based on Deep Learning and Classical Time Series Analysis

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  • Xiangrui Meng

    (School of Economics and Management, Anhui University of Science & Technology, Huainan 232000, China
    State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines, Anhui University of Science and Technology, Huainan 232000, China)

  • Haoqian Chang

    (School of Economics and Management, Anhui University of Science & Technology, Huainan 232000, China)

  • Xiangqian Wang

    (School of Economics and Management, Anhui University of Science & Technology, Huainan 232000, China)

Abstract

Methane is one of the most dangerous gases encountered in the mining industry. During mining operations, methane can be broadly classified into three states: mining excavation, stoppage safety check, and abnormal methane concentration, which is usually a precursor to a gas accident, such as a coal and gas outburst. Consequently, it is vital to accurately predict methane concentrations. Herein, we apply three deep learning methods—a recurrent neural network (RNN), long short-term memory (LSTM), and a gated recurrent unit (GRU)—to the problem of methane concentration prediction and evaluate their efficacy. In addition, we propose a novel prediction method that combines classical time series analysis with these deep learning models. The results revealed that GRU has the least root mean square error (RMSE) loss of the three models. The RMSE loss can be further reduced by approximately 35% by using the proposed combined approach, and the models are also less likely to result in overfitting. Therefore, combining deep learning methods with classical time series analysis can provide accurate methane concentration prediction and improve mining safety.

Suggested Citation

  • Xiangrui Meng & Haoqian Chang & Xiangqian Wang, 2022. "Methane Concentration Prediction Method Based on Deep Learning and Classical Time Series Analysis," Energies, MDPI, vol. 15(6), pages 1-15, March.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:6:p:2262-:d:775223
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    References listed on IDEAS

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    1. Wu Xiang & Qian Jian-sheng & Huang Cheng-hua & Zhang Li, 2014. "Short-Term Coalmine Gas Concentration Prediction Based on Wavelet Transform and Extreme Learning Machine," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-8, July.
    2. Shuang Song & Shugang Li & Tianjun Zhang & Li Ma & Shaobo Pan & Lu Gao, 2021. "Research on a Multi-Parameter Fusion Prediction Model of Pressure Relief Gas Concentration Based on RNN," Energies, MDPI, vol. 14(5), pages 1-18, March.
    3. Tianjun Zhang & Shuang Song & Shugang Li & Li Ma & Shaobo Pan & Liyun Han, 2019. "Research on Gas Concentration Prediction Models Based on LSTM Multidimensional Time Series," Energies, MDPI, vol. 12(1), pages 1-15, January.
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    Cited by:

    1. Sergey Zhironkin & Elena Dotsenko, 2023. "Review of Transition from Mining 4.0 to 5.0 in Fossil Energy Sources Production," Energies, MDPI, vol. 16(15), pages 1-35, August.
    2. Olga Zhironkina & Sergey Zhironkin, 2023. "Technological and Intellectual Transition to Mining 4.0: A Review," Energies, MDPI, vol. 16(3), pages 1-37, February.

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