Quantitative assessment rules and models for dynamic disaster risk in high-density gas gathering stations: Practical application in a largest CBM gathering station

Qi Jing, Lin Yu, Fengyi Lan and Yuntao Li

Reliability Engineering and System Safety, 2024, vol. 252, issue C

Abstract: Coalbed Methane (CBM) gathering stations involve multiple engineering disciplines, with a concentration of pressure vessels and strong production continuity. Once accidents occur, they can easily result in severe casualties, property losses, and environmental pollution. Therefore, prior to the large-scale promotion and urban application of CBM, this paper proposes a dynamic quantitative risk assessment model for the operational and task-related risks in CBM gathering stations. Firstly, various methods are employed for risk factor identification. Then, a dynamic Bayesian network (DBN) model is constructed to assess the dynamic operational risks and task-related risks of CBM gathering stations under different maintenance conditions. This model provides the initial probability of operational risk occurrence at stations, predicts changes in the probability of equipment multi-state operational risks under different maintenance conditions over 10 years, and forecasts the probability of task-related risks during operations. Finally, a combined weighting model is established, incorporating instance information to comprehensively evaluate safety management and derive safety management correction coefficients. These coefficients are integrated with the probabilities of station operation and task-related risk occurrence to ultimately determine the likelihood of operational risk occurrence during station production, completing the dynamic quantitative assessment of operational risks in CBM gathering stations.

Keywords: Coalbed methane station; Dynamic Bayesian network; Data mining; Risk-based inspection technology; Risk assessment (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:252:y:2024:i:c:s0951832024005258

DOI: 10.1016/j.ress.2024.110453

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