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A novel dynamic risk assessment method for the petrochemical industry using bow-tie analysis and Bayesian network analysis method based on the methodological framework of ARAMIS project

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  • Wu, Xingguang
  • Huang, Huirong
  • Xie, Jianyu
  • Lu, Meixing
  • Wang, Shaobo
  • Li, Wang
  • Huang, Yixuan
  • Yu, Weichao
  • Sun, Xiaobo

Abstract

In order to obtain the actual risk level and assess the performance of the safety barriers, bow-tie (BT) approach proposed by ARAMIS project is applied to accident scenarios identification and risk analysis, but this approach has limitations in dynamic risk assessment due to its static nature. This study takes the floating roof tank as the research object, and further proposes a dynamic risk assessment method based on the methodology proposed by the ARAMIS project. The BT model of the major leak of external floating roof tank was established by comprehensive consideration of the barrier functions and the logical relationship between causes and events. Furthermore, The BT to Bayesian network (BN) mapping algorithm was proposed to characterize system dynamics and uncertainty. The analysis results show that not only the dynamic assessment of the risk level for safety barriers in different states can be implemented, but also whether the current overall safety protection capability of the system is sufficient can be judged. The proposed method and findings can help managers identify safety barriers that play a key role in various accident risks, and provide effective support for risk management decision-making and implementation of preventive strategies.

Suggested Citation

  • Wu, Xingguang & Huang, Huirong & Xie, Jianyu & Lu, Meixing & Wang, Shaobo & Li, Wang & Huang, Yixuan & Yu, Weichao & Sun, Xiaobo, 2023. "A novel dynamic risk assessment method for the petrochemical industry using bow-tie analysis and Bayesian network analysis method based on the methodological framework of ARAMIS project," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    • Handle: RePEc:eee:reensy:v:237:y:2023:i:c:s0951832023003113
    DOI: 10.1016/j.ress.2023.109397
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    3. Huang, Tudi & Xiahou, Tangfan & Mi, Jinhua & Chen, Hong & Huang, Hong-Zhong & Liu, Yu, 2024. "Merging multi-level evidential observations for dynamic reliability assessment of hierarchical multi-state systems: A dynamic Bayesian network approach," Reliability Engineering and System Safety, Elsevier, vol. 249(C).
    4. Deng, Wanyi & Ma, Xiaoxue & Qiao, Weiliang, 2024. "A novel methodology to quantify the impact of safety barriers on maritime operational risk based on a probabilistic network," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    5. Xie, Shuyi & Huang, Zimeng & Wu, Gang & Luo, Jinheng & Li, Lifeng & Ma, Weifeng & Wang, Bohong, 2024. "Combining precursor and Cloud Leaky noisy-OR logic gate Bayesian network for dynamic probability analysis of major accidents in the oil depots," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    6. Lilli, Giordano & Sanavia, Matteo & Oboe, Roberto & Vianello, Chiara & Manzolaro, Mattia & De Ruvo, Pasquale Luca & Andrighetto, Alberto, 2024. "A semi-quantitative risk assessment of remote handling operations on the SPES Front-End based on HAZOP-LOPA," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    7. Men, Jinkun & Chen, Guohua & Reniers, Genserik, 2024. "Pareto-based design optimization of chemical tank farm using a trade-off between domino effects related and land resource utilization efficiency," Reliability Engineering and System Safety, Elsevier, vol. 249(C).

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