LAPSE:2023.13643
Published Article
LAPSE:2023.13643
Research on Intelligent Comprehensive Evaluation of Coal Seam Impact Risk Based on BP Neural Network Model
March 1, 2023
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.
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.
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Keywords
BP neural network, coal bump, coal seam, comprehensive evaluation, impact risk, intelligent
Suggested Citation
Zhang K, Zhu J, He M, Jiang Y, Zhu C, Li D, Kang L, Sun J, Chen Z, Wang X, Yang H, Wu Y, Yan X. Research on Intelligent Comprehensive Evaluation of Coal Seam Impact Risk Based on BP Neural Network Model. (2023). LAPSE:2023.13643
Author Affiliations
Zhang K: 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 [ORCID]
Zhu J: 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
He M: State Key Laboratory for Geomechanics and Deep Underground Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China
Jiang Y: 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),
Zhu C: School of Earth Sciences and Engineering, Hohai University, Nanjing 210098, China
Li D: 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;
Kang L: 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 [ORCID]
Sun J: 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;
Chen Z: 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;
Wang X: 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
Yang H: 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
Wu Y: 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
Yan X: 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
Zhu J: 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
He M: State Key Laboratory for Geomechanics and Deep Underground Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China
Jiang Y: 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),
Zhu C: School of Earth Sciences and Engineering, Hohai University, Nanjing 210098, China
Li D: 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;
Kang L: 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 [ORCID]
Sun J: 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;
Chen Z: 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;
Wang X: 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
Yang H: 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
Wu Y: 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
Yan X: 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
Journal Name
Energies
Volume
15
Issue
9
First Page
3292
Year
2022
Publication Date
2022-04-30
ISSN
1996-1073
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PII: en15093292, Publication Type: Journal Article
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