An event-driven probabilistic methodology for modeling the spatial-temporal evolution of natural hazard-induced domino chain in chemical industrial parks

Journal article (2022)

Authors

Jinkun Men South China University of Technology, Guangdong Provincial Science and Technology Collaborative Innovation Center for Work Safety
Guohua Chen South China University of Technology, Guangdong Provincial Science and Technology Collaborative Innovation Center for Work Safety
Yunfeng Yang Guangdong Provincial Science and Technology Collaborative Innovation Center for Work Safety, South China University of Technology
G.L.L.M.E. Reniers Katholieke Universiteit Leuven, Safety and Security Science - TPM , Universiteit Antwerpen
Research Group
Safety and Security Science (TPM) (TU Delft)
Copyright: © 2022 Jinkun Men, Guohua Chen, Yunfeng Yang, G.L.L.M.E. Reniers
DOI
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https://doi.org/10.1016/j.ress.2022.108723
Dynamic risk analysis Chemical industrial park Disaster chain evolution system Event-driven probabilistic methodology Natural hazard-induced domino chain
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Published Date

2022

Language

English

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Faculty

Technology, Policy and Management

Department

Values Technology and Innovation

Research Group

Safety and Security Science

Abstract

Natural hazards may rapidly lead to a massive domino chain in chemical industrial parks (CIPs). This work develops a high-efficiency and systematic analytical framework that is applicable to a broad range of uncertain and time-varying factors related to the evolution process of natural hazard-induced domino chain (NHDC). Specifically, the evolution mechanism of NHDC is revealed from a macro-systemic perspective. An event-driven disaster chain evolution system is developed, of which the system state transition is formulated by a Markov decision process and a temporal-difference learning algorithm. A system dynamic risk model is proposed to analyze the dynamic risk associated with NHDC. An earthquake-induced Na-tech scenario is adopted to demonstrate the methodology. Computational results indicate that the proposed methodology is competitive in simulating large-scale system state transition spaces. The involvement of natural hazards would lead to a more complex and severe evolution pattern. Five distinctive stages of the whole NHDC were identified. We found that the value of system dynamic risk is likely to surge in the deterioration stage. Our methodology can dynamically identify the critical system temporal intervals and units at each evolution stage, which has the potential to support the prevention and mitigation of such catastrophic chain events.