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Coalbed methane concentration prediction and early-warning in fully mechanized mining face based on deep learning

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  • Wen, Hu
  • Yan, Li
  • Jin, Yongfei
  • Wang, Zhipeng
  • Guo, Jun
  • Deng, Jun

Abstract

Coalbed methane (CBM) disasters are a major safety problem in coal mining. CBM concentration prediction and early-warning technology play a vital role in the prevention and control of CBM disasters. Traditional prediction methods have some shortcomings such as unreasonable data analysis, inability to predict in real time, long prediction time, and overfitting. This study uses a large amount of coal mine CBM data and develops a fast and high-precision CBM concentration prediction method based on deep learning theory, which can be used for CBM disaster early-warning. The proposed method has the following advantages: (1) it combines three exponential smoothing methods, the autoregressive model, wavelet domain denoising method, and principal component analysis. This method optimizes data with outliers, missing values, and noise. (2) Particle swarm optimization and genetic algorithm are used to optimize the network parameters of the gated recurrent unit. The application and verification of the optimized model show that the running time and accuracy are significantly improved. (3) By combining the optimized prediction model with Spark streaming, an early-warning system is developed, which can complete the efficient early-warning of CBM concentration within 8 s. The proposed method provides decision-making support for mine safety and CBM disaster prevention and control.

Suggested Citation

  • Wen, Hu & Yan, Li & Jin, Yongfei & Wang, Zhipeng & Guo, Jun & Deng, Jun, 2023. "Coalbed methane concentration prediction and early-warning in fully mechanized mining face based on deep learning," Energy, Elsevier, vol. 264(C).
    • Handle: RePEc:eee:energy:v:264:y:2023:i:c:s0360544222030948
    DOI: 10.1016/j.energy.2022.126208
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    1. Yongkang Yang & Qiaoyi Du & Chenlong Wang & Yu Bai, 2020. "Research on the Method of Methane Emission Prediction Using Improved Grey Radial Basis Function Neural Network Model," Energies, MDPI, vol. 13(22), pages 1-15, November.
    2. Zhou, Shangyong & Gao, Jiancun & Luo, Zhenmin & Hu, Shoutao & Wang, Le & Wang, Tao, 2022. "Role of ferromagnetic metal velvet and DC magnetic field on the explosion of a C3H8/air mixture-effect on reaction mechanism," Energy, Elsevier, vol. 239(PC).
    3. Magdalena Tutak & Jarosław Brodny, 2019. "Forecasting Methane Emissions from Hard Coal Mines Including the Methane Drainage Process," Energies, MDPI, vol. 12(20), pages 1-28, October.
    4. Guo, Zixi & Zhao, Jinzhou & You, Zhenjiang & Li, Yongming & Zhang, Shu & Chen, Yiyu, 2021. "Prediction of coalbed methane production based on deep learning," Energy, Elsevier, vol. 230(C).
    5. Yan, Li & Wen, Hu & Liu, Wenyong & Jin, Yongfei & Liu, Yin & Li, Chuansheng, 2022. "Adiabatic spontaneous coal combustion period derived from the thermal effect of spontaneous combustion," Energy, Elsevier, vol. 239(PB).
    6. 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.
    7. Liang Qiao & Doudou Liu & Xueliang Yuan & Qingsong Wang & Qiao Ma, 2020. "Generation and Prediction of Construction and Demolition Waste Using Exponential Smoothing Method: A Case Study of Shandong Province, China," Sustainability, MDPI, vol. 12(12), pages 1-12, June.
    8. Cheng Zhai & Xianwei Xiang & Jizhao Xu & Shiliang Wu, 2016. "The characteristics and main influencing factors affecting coal and gas outbursts in Chinese Pingdingshan mining region," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 82(1), pages 507-530, May.
    9. Zhang, Yuanbo & Zhang, Yutao & Li, Yaqing & Shi, Xueqiang & Che, Bo, 2022. "Determination of ignition temperature and kinetics and thermodynamics analysis of high-volatile coal based on differential derivative thermogravimetry," Energy, Elsevier, vol. 240(C).
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    1. Zhou, Aitao & Li, Jingwen & Gong, Weili & Wang, Kai & Du, Changang, 2023. "Theoretical and numerical study on the contribution of multi-hole arrangement to coalbed methane extraction," Energy, Elsevier, vol. 284(C).
    2. Dong, Fangying & Yin, Huiyong & Cheng, Wenju & Zhang, Chao & Zhang, Danyang & Ding, Haixiao & Lu, Chang & Wang, Yin, 2024. "Quantitative prediction model and prewarning system of water yield capacity (WYC) from coal seam roof based on deep learning and joint advanced detection," Energy, Elsevier, vol. 290(C).

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