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Dynamic probabilistic risk assessment of emergency response for intelligent coal mining face system, case study: Gas overrun scenario

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  • Zhang, Yan
  • Wang, Yu-Hao
  • Zhao, Xu
  • Tong, Rui-Peng

Abstract

Intelligent coal mining has become an inevitable development trend of the coal industry in the era of artificial intelligence. Emergency response level is particularly important for preventing accidents and reducing accident losses in intelligent coal mine (ICM) operations, which urgently needs an accurate risk assessment method to assess the risk of ICM emergency response. Combining the ICM system structure and emergency response process (ERP) of human-system interaction based on the information, decision, and action in crew context (IDAC) model, this study proposes a dynamic probabilistic risk assessment (PRA) method based on dynamic Bayesian network (DBN), mainly including the event sequence diagram (ESD) from the cognitive perspective, human-system concurrent task analysis (CoTA) from success perspective and fault tree analysis (FTA) model from failure perspective, dependency model of performance influencing factor (PIF) from organizational impact perspective, and the “ESD-FTA-PIFs” coupled BN model. To address the uncertainty of risk factors (e.g., human factors and system equipment) and achieve quantitative analysis, the coupled BN model is mapped to the DBN model to perform dynamic PRA combining with existing databases, fuzzy set theory, Dempster-Shafer (D-S) evidence theory, and time-variant model. Finally, the practicality and advantages of the proposed methodology are demonstrated by a real case study in the gas overrun scenario. The results show that the DBN model can perform dynamic inference analysis to achieve the risk prediction of event and system state compared with the static BN model. The comprehensive integrated DBN model of “ESD-FTA-PIFs” not only enables bidirectional risk analysis of emergency response event, but also enables the systematic traceability of risk factors. The proposed methodology can be a useful tool to assess the dynamic risk of ICM emergency response and decision-making, and it can provide scientific guidance for risk assessment during emergency response of other safety-critical industries.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:jrpoli:v:85:y:2023:i:pb:s0301420723007067
    DOI: 10.1016/j.resourpol.2023.103995
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    References listed on IDEAS

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    1. Zhang, Yan & Wang, Si-Xia & Yao, Jian-Ting & Tong, Rui-Peng, 2023. "The impact of behavior safety management system on coal mine work safety: A system dynamics model of quadripartite evolutionary game," Resources Policy, Elsevier, vol. 82(C).
    2. Pence, Justin & Sakurahara, Tatsuya & Zhu, Xuefeng & Mohaghegh, Zahra & Ertem, Mehmet & Ostroff, Cheri & Kee, Ernie, 2019. "Data-theoretic methodology and computational platform to quantify organizational factors in socio-technical risk analysis," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 240-260.
    3. Ekanem, Nsimah J. & Mosleh, Ali & Shen, Song-Hua, 2016. "Phoenix – A model-based Human Reliability Analysis methodology: Qualitative Analysis Procedure," Reliability Engineering and System Safety, Elsevier, vol. 145(C), pages 301-315.
    4. Poormirzaee, Rashed & Hosseini, Shahab & Taghizadeh, Rahim, 2022. "Smart mining policy: Integrating fuzzy-VIKOR technique and the Z-number concept to implement industry 4.0 strategies in mining engineering," Resources Policy, Elsevier, vol. 77(C).
    5. Liu, Quanlong & Dou, Fenfen & Meng, Xianfei, 2021. "Building risk precontrol management systems for safety in China's underground coal mines," Resources Policy, Elsevier, vol. 74(C).
    6. Nawrocki, Tomasz Leszek & Jonek-Kowalska, Izabela, 2016. "Assessing operational risk in coal mining enterprises – Internal, industrial and international perspectives," Resources Policy, Elsevier, vol. 48(C), pages 50-67.
    7. Ramos, M.A. & Thieme, Christoph A. & Utne, Ingrid B. & Mosleh, A., 2020. "Human-system concurrent task analysis for maritime autonomous surface ship operation and safety," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    8. Chang, Chia-Hsun & Kontovas, Christos & Yu, Qing & Yang, Zaili, 2021. "Risk assessment of the operations of maritime autonomous surface ships," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    9. 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.
    10. Pandya, Dhruv & Podofillini, Luca & Emert, Frank & Lomax, Antony J. & Dang, Vinh N. & Sansavini, Giovanni, 2020. "Quantification of a human reliability analysis method for radiotherapy applications based on expert judgment aggregation," Reliability Engineering and System Safety, Elsevier, vol. 194(C).
    11. Chang, Y.H.J. & Mosleh, A., 2007. "Cognitive modeling and dynamic probabilistic simulation of operating crew response to complex system accidents," Reliability Engineering and System Safety, Elsevier, vol. 92(8), pages 1076-1101.
    12. Laumann, Karin & Rasmussen, Martin, 2016. "Suggested improvements to the definitions of Standardized Plant Analysis of Risk-Human Reliability Analysis (SPAR-H) performance shaping factors, their levels and multipliers and the nominal tasks," Reliability Engineering and System Safety, Elsevier, vol. 145(C), pages 287-300.
    13. Linhui Sun & Zigu Guo & Xiaofang Yuan & Xinping Wang & Chang Su & Jiali Jiang & Xun Li, 2022. "An Investigation of the Effects of Brain Fatigue on the Sustained Attention of Intelligent Coal Mine VDT Operators," IJERPH, MDPI, vol. 19(17), pages 1-22, September.
    14. Thieme, Christoph A. & Mosleh, Ali & Utne, Ingrid B. & Hegde, Jeevith, 2020. "Incorporating software failure in risk analysis––Part 2: Risk modeling process and case study," Reliability Engineering and System Safety, Elsevier, vol. 198(C).
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