A dynamic risk assessment model based on multidimensional and quantitative inference theory

Abstract

With the continuous increase in petroleum consumption and the trend towards more refined management, crude oil storage tanks are playing an increasingly crucial role in the petrochemical industry. However, the corrosion issue of crude oil storage tanks has consistently been a major factor limiting their service life, and leaks in crude oil tanks are often attributed to corrosion of the tank bottom. Due to the high cost and complexity associated with tank opening inspections, in this paper, directly observable information and physical quantities are utilized as indirect evidence, combined with Bayesian network, to indirectly determine the probability of tank corrosion. This approach allows for selective internal tank inspections, enabling effective risk assessment while also facilitating cost savings. Some parameters in the Bayesian network model are obtained through a probability estimation model combining expert experience and fuzzy set theory. Finally, the feasibility of the method is demonstrated by citing examples. The current model can offer a relatively reliable reference for evaluating the risks associated with tanks and their ancillary facilities, while also reducing the costs of tank inspections to some extent.