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A dynamic multi-agent approach for modeling the evolution of multi-hazard accident scenarios in chemical plants

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  • Chen, Chao
  • Reniers, Genserik
  • Khakzad, Nima

Abstract

In the chemical industry, multi-hazard (toxic, flammable, and explosive) materials such as acrylonitrile are stored, transported, and processed in large quantities. A release of multi-hazard materials can simultaneously or sequentially lead to acute toxicity, fire and explosion. The spatial-temporal evolution of hazards may also result in cascading effects. In this study, a dynamic methodology called “Dynamic Graph Monte Carlo†(DGMC) is developed to model the evolution of multi-hazard accident scenarios and assess the vulnerability of humans and installations exposed to such hazards. In the DGMC model, chemical plants are modeled as a multi-agent system with three kinds of agents: hazardous installations, ignition sources, and humans while considering the uncertainties and interdependencies among the agents and their impacts on the evolution of hazards and possible escalation effects. A case study is analyzed using the DGMC methodology, demonstrating that the risk can be underestimated if the spatial-temporal evolution of multi-hazard scenarios is neglected. Vapor cloud explosion (VCEs) may lead to more severe damage than fire, and the safety distances which are implemented only based on fire hazards are not sufficient to prevent from the damage of VCEs.

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  • Chen, Chao & Reniers, Genserik & Khakzad, Nima, 2021. "A dynamic multi-agent approach for modeling the evolution of multi-hazard accident scenarios in chemical plants," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    • Handle: RePEc:eee:reensy:v:207:y:2021:i:c:s0951832020308401
    DOI: 10.1016/j.ress.2020.107349
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    References listed on IDEAS

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    3. Lan, Meng & Gardoni, Paolo & Qin, Rongshui & Zhang, Xiao & Zhu, Jiping & Lo, Siuming, 2022. "Modeling NaTech-related domino effects in process clusters: A network-based approach," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    4. Mocellin, Paolo & Pilenghi, Lisa, 2023. "Semi-quantitative approach to prioritize risk in industrial chemical plants aggregating safety, economics and ageing: A case study," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    5. Gholamizadeh, Kamran & Zarei, Esmaeil & Yazdi, Mohammad & Ramezanifar, Ehsan & Aliabadi, Mostafa Mirzaei, 2024. "A hybrid model for dynamic analysis of domino effects in chemical process industries," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    6. Li, Qilin & Wang, Yang & Chen, Wensu & Li, Ling & Hao, Hong, 2024. "Machine learning prediction of BLEVE loading with graph neural networks," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    7. Mingfei Hu & Xinyi Hu & Zhenzhou Deng & Bing Tu, 2022. "Fault Diagnosis of Tennessee Eastman Process with XGB-AVSSA-KELM Algorithm," Energies, MDPI, vol. 15(9), pages 1-25, April.
    8. Li, Xiaofeng & Chen, Guohua & Amyotte, Paul & Khan, Faisal & Alauddin, Mohammad, 2023. "Vulnerability assessment of storage tanks exposed to simultaneous fire and explosion hazards," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    9. Ding, Long & Khan, Faisal & Ji, Jie, 2022. "A novel vulnerability model considering synergistic effect of fire and overpressure in chemical processing facilities," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    10. Men, Jinkun & Chen, Guohua & Yang, Yunfeng & Reniers, Genserik, 2022. "An event-driven probabilistic methodology for modeling the spatial-temporal evolution of natural hazard-induced domino chain in chemical industrial parks," Reliability Engineering and System Safety, Elsevier, vol. 226(C).

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