Quantitative risk assessment of leakage accident of crude oil storage tank based on fuzzy Bayesian network and improved AHP

Abstract

Leakage accidents of crude oil storage tanks (LACOST) occasionally occur during the production and storage processes of the petroleum and chemical industry, significantly impacting lives, the environment, and private property. To enhance the risk assessment of LACOST, our study sought to construct a fuzzy Bayesian network (FBN) through expert evaluation based on an improved analytic hierarchy process (AHP). Subsequently, the societal risk of LACOST was analyzed in conjunction with the surrounding population density. Applying the proposed method to a crude oil storage depot in China revealed that incorporating the improved AHP significantly enhances the FBN's risk assessment capability, leading to more accurate predictions of LACOST likelihood. Furthermore, the importance of basic events was assessed, thereby effectively and reliably identifying critical events of LACOST. The rationality of the layout of buildings and population density in the oil depot was assessed through societal risk analysis. Collectively, our findings demonstrated that the proposed method can effectively identify changes in both LACOST probabilities and consequences, enabling decision-makers to optimize risk management strategies and achieve efficient resource allocation.