A novel risk evaluation method for fire and explosion accidents in oil depots using bow-tie analysis and risk matrix analysis method based on cloud model theory

Due to the catastrophic impact of Fire and Explosion Accidents in Oil Depots (FEAOD), the prevention and control of such accidents is the most critical part of risk management. In this study, a Bow-tie (BT) model was built for FEAOD, while a quantitative risk assessment and consequence assessment was performed in conjunction with the risk matrix. Then, due to the uncertainty and ambiguity of the probability data of Basic Events (BEs) during the expert elicitation process, this study creatively proposed a Cloud-Analytic Hierarchy Process (Cloud-AHP) algorithm and Group Cloud Decision-Making (GCDM) algorithm based on the Fuzzy Cloud Membership Function (FCMF). Finally, combined with the probability estimation algorithm and the sensitivity analysis, a novel risk quantitative assessment algorithm for a BT model was developed based on Cloud Model (CM) theory. The discretion, ambiguity, and randomness were considered during the evaluation process, and a more scientific assessment fusion result of Decision makers (DMs) was obtained, while the Delphi iteration process further reduced personal errors. A case study of an oil depot in Dalian was investigated. The results showed that the proposed method more accurately identified the weak links in the safety system, providing a theoretical basis for the risk prevention and control.

Automated summary

This study focuses on the prevention and control of Fire and Explosion Accidents in Oil Depots (FEAOD) by building a Bow-tie (BT) model and proposing a novel risk quantitative assessment algorithm based on Cloud Model (CM) theory. Through a case study, it was found that the proposed method can more accurately identify the weak links in the safety system, providing a theoretical basis for risk prevention and control.