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Prevention and control strategy of coal mine water inrush accident based on case-driven and Bow-tie-Bayesian model

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  • Tong, Xin
  • Zheng, Xuezhao
  • Jin, Yongfei
  • Dong, Beibei
  • Liu, Qingyun
  • Li, Yuan

Abstract

Preventing and controlling coal mine water inrush accidents is a prerequisite for ensuring safe mining and a stable energy supply. In order to solve the problems of accident chain uncertainty reasoning and a priori information ambiguity in the risk analysis, the macroscopic causative mechanism was explored based on 154 cases, a causative system was constructed by using the Bow-tie model and the topological network, and a fuzzy Bayesian risk assessment model was developed. Through causal reasoning, diagnostic reasoning, and sensitivity analysis, the accident probability was determined, and causal mechanism was revealed. The case study showed that the accident probability in a mine working face is 0.84 %; the probabilities of accident induced by the four combined paths are more than 900 % higher than that of the normal situation, and paths are the main object of accident prevention. The accident development dynamic rules under the safety barriers was revealed. Finally, the model validation was carried out with the Zhaojin coal mine water inrush accident as a sample, and the result showed that the accident probability is 9 %, and the critical causal paths were basically consistent with the accident investigation. The method can provide technical support for decision-makers to effectively prevent accidents.

Suggested Citation

  • Tong, Xin & Zheng, Xuezhao & Jin, Yongfei & Dong, Beibei & Liu, Qingyun & Li, Yuan, 2025. "Prevention and control strategy of coal mine water inrush accident based on case-driven and Bow-tie-Bayesian model," Energy, Elsevier, vol. 320(C).
    • Handle: RePEc:eee:energy:v:320:y:2025:i:c:s0360544225009545
    DOI: 10.1016/j.energy.2025.135312
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    References listed on IDEAS

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    1. Zhang, Yan & Wang, Yu-Hao & Zhao, Xu & Tong, Rui-Peng, 2023. "Dynamic probabilistic risk assessment of emergency response for intelligent coal mining face system, case study: Gas overrun scenario," Resources Policy, Elsevier, vol. 85(PB).
    2. Mkrtchyan, L. & Podofillini, L. & Dang, V.N., 2016. "Methods for building Conditional Probability Tables of Bayesian Belief Networks from limited judgment: An evaluation for Human Reliability Application," Reliability Engineering and System Safety, Elsevier, vol. 151(C), pages 93-112.
    3. 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).
    4. Li, Weijun & Sun, Qiqi & Zhang, Jiwang & Zhang, Laibin, 2024. "Quantitative risk assessment of industrial hot work using Adaptive Bow Tie and Petri Nets," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    5. Guo, Yong & Yang, Fuqiang, 2023. "Mining safety research in China: Understanding safety research trends and future demands for sustainable mining industry," Resources Policy, Elsevier, vol. 83(C).
    Full references (including those not matched with items on IDEAS)

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