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Research on Intelligent Comprehensive Evaluation of Coal Seam Impact Risk Based on BP Neural Network Model

Author

Listed:
  • Kexue Zhang

    (Hebei Key Laboratory of Mine Intelligent Unmanned Mining Technology, North China Institute of Science and Technology, Beijing 101601, China
    State Key Laboratory for Geomechanics and Deep Underground Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China
    Institute of Intelligent Unmanned Mining, North China Institute of Science and Technology, Beijing 101601, China
    State Key Laboratory of Coal Resources and Mine Safety, China University of Mining & Technology (Beijing), Beijing 100083, China)

  • Junao Zhu

    (Hebei Key Laboratory of Mine Intelligent Unmanned Mining Technology, North China Institute of Science and Technology, Beijing 101601, China
    Institute of Intelligent Unmanned Mining, North China Institute of Science and Technology, Beijing 101601, China)

  • Manchao He

    (State Key Laboratory for Geomechanics and Deep Underground Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China)

  • Yaodong Jiang

    (State Key Laboratory for Geomechanics and Deep Underground Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China
    State Key Laboratory of Coal Resources and Mine Safety, China University of Mining & Technology (Beijing), Beijing 100083, China)

  • Chun Zhu

    (School of Earth Sciences and Engineering, Hohai University, Nanjing 210098, China)

  • Dong Li

    (Hebei Key Laboratory of Mine Intelligent Unmanned Mining Technology, North China Institute of Science and Technology, Beijing 101601, China
    Institute of Intelligent Unmanned Mining, North China Institute of Science and Technology, Beijing 101601, China
    School of Mine Safety, North China Institute of Science and Technology, Beijing 101601, China)

  • Lei Kang

    (Hebei Key Laboratory of Mine Intelligent Unmanned Mining Technology, North China Institute of Science and Technology, Beijing 101601, China
    Institute of Intelligent Unmanned Mining, North China Institute of Science and Technology, Beijing 101601, China)

  • Jiandong Sun

    (Hebei Key Laboratory of Mine Intelligent Unmanned Mining Technology, North China Institute of Science and Technology, Beijing 101601, China
    Institute of Intelligent Unmanned Mining, North China Institute of Science and Technology, Beijing 101601, China
    School of Mine Safety, North China Institute of Science and Technology, Beijing 101601, China)

  • Zhiheng Chen

    (Hebei Key Laboratory of Mine Intelligent Unmanned Mining Technology, North China Institute of Science and Technology, Beijing 101601, China
    Institute of Intelligent Unmanned Mining, North China Institute of Science and Technology, Beijing 101601, China
    School of Mine Safety, North China Institute of Science and Technology, Beijing 101601, China)

  • Xiaoling Wang

    (Hebei Key Laboratory of Mine Intelligent Unmanned Mining Technology, North China Institute of Science and Technology, Beijing 101601, China
    Institute of Intelligent Unmanned Mining, North China Institute of Science and Technology, Beijing 101601, China)

  • Haijiang Yang

    (Hebei Key Laboratory of Mine Intelligent Unmanned Mining Technology, North China Institute of Science and Technology, Beijing 101601, China
    Institute of Intelligent Unmanned Mining, North China Institute of Science and Technology, Beijing 101601, China)

  • Yongwei Wu

    (Hebei Key Laboratory of Mine Intelligent Unmanned Mining Technology, North China Institute of Science and Technology, Beijing 101601, China
    Institute of Intelligent Unmanned Mining, North China Institute of Science and Technology, Beijing 101601, China)

  • Xingcheng Yan

    (Hebei Key Laboratory of Mine Intelligent Unmanned Mining Technology, North China Institute of Science and Technology, Beijing 101601, China
    Institute of Intelligent Unmanned Mining, North China Institute of Science and Technology, Beijing 101601, China)

Abstract

Coal seam impact risk assessment is the premise of coal mine safety, which can reduce the occurrence of underground impact pressure accidents and directly affect the safety, coal production, economic and social benefits of coal mining enterprises. In order to evaluate the impact risk of coal seams more reasonably and comprehensively, and consider the weights of different influencing factors on the impact risk of coal seams, the neural network model is proposed to evaluate the impact risk of coal seams. Mining depth, impact tendency, geological structure and mining technology are selected as the influencing factors of coal seam impact risk. Each influencing factor contains different evaluation indices, a total of 18. The 18 evaluation indices and the impact risk level are normalized and quantified. The BP neural network model for evaluating coal seam impact risk level is established, and the impact risk of 2-1 coal seams in a mine in Inner Mongolia is comprehensively evaluated and analyzed in this study. The results show that the BP neural network model can represent coal seam impact risk level well. The application of the BP neural network model to evaluate coal seam impact risk level has the characteristics of high precision, fast calculation speed and less artificial calculation, which provides an efficient and convenient method for the evaluation of coal seam impact risk.

Suggested Citation

  • Kexue Zhang & Junao Zhu & Manchao He & Yaodong Jiang & Chun Zhu & Dong Li & Lei Kang & Jiandong Sun & Zhiheng Chen & Xiaoling Wang & Haijiang Yang & Yongwei Wu & Xingcheng Yan, 2022. "Research on Intelligent Comprehensive Evaluation of Coal Seam Impact Risk Based on BP Neural Network Model," Energies, MDPI, vol. 15(9), pages 1-14, April.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:9:p:3292-:d:806639
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    References listed on IDEAS

    as
    1. Kexue Zhang & Lei Kang & Xuexi Chen & Manchao He & Chun Zhu & Dong Li, 2022. "A Review of Intelligent Unmanned Mining Current Situation and Development Trend," Energies, MDPI, vol. 15(2), pages 1-19, 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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