Sustainable Risk Management Framework for Petroleum Storage Facilities: Integrating Bow-Tie Analysis and Dynamic Bayesian Networks

Dingding Yang, Kexin Xing, Lidong Pan, Ning Lu and Jingxiao Yu ()
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Dingding Yang: National & Local Joint Engineering Research Center of Harbor Oil & Gas Storage and Transportation Technology, Zhejiang Key Laboratory of Pollution Control for Port-Petrochemical Industry, Zhejiang Ocean University, Zhoushan 316022, China
Kexin Xing: School of Naval Architecture and Maritime, Zhejiang Ocean University, Zhoushan 316022, China
Lidong Pan: Zhejiang Academy of Special Equipment Science, Hangzhou 310020, China
Ning Lu: Sinochem Zhoushan Dangerous Chemical Emergency Rescue Base Co., Ltd., Zhoushan 316022, China
Jingxiao Yu: National & Local Joint Engineering Research Center of Harbor Oil & Gas Storage and Transportation Technology, Zhejiang Key Laboratory of Pollution Control for Port-Petrochemical Industry, Zhejiang Ocean University, Zhoushan 316022, China

Sustainability, 2025, vol. 17, issue 6, 1-17

Abstract: Petroleum storage and transport systems necessitate robust safety measures to mitigate oil spill risks threatening marine ecosystems and sustainable development through ecological and socioeconomic safeguards. We aimed to gain a deeper understanding of the evolution patterns of accidents and effectively mitigate risks. An improved risk assessment method that combines the Bow-Tie (BT) theory and Dynamic Bayesian theory was applied to evaluate the safety risks of petroleum storage and transportation facilities. Additionally, a scenario modeling approach was utilized to construct a model of the event chain resulting from accidents, facilitating quantitative analysis and risk prediction. By constructing an accident chain based on fault trees, the BT model was converted into a Bayesian Network (BN) model. A Dynamic Bayesian Network (DBN) model was established by incorporating time series parameters into the static Bayesian model, enabling the dynamic risk assessment of an oil storage and transportation base in the Zhoushan archipelago. This study quantitatively analyzes the dynamic risk propagation process of storage tank leakage, establishing time-dependent risk probability profiles. The results demonstrate an initial leakage probability of 0.015, with risk magnitude doubling for the temporal progression and concurrent probabilistic escalation of secondary hazards, including fire or explosion scenarios. A novel risk transition framework for the consequences of petrochemical leaks has been developed, providing a predictive paradigm for risk evolution trajectories and offering critical theoretical and practical references for emergency response optimization.

Keywords: dynamic Bayesian networks; sustainable risk assessment; oil storage and transportation base (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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