Application of Bayesian network to safety assessment of chemical plants during fire-induced domino effects

Abstract

The propagation of fire-induced domino effects in chemical plants largely depends on the presence of fire protection barriers. Aside from internal safety distances, which are usually considered as a component of an inherently safer design, engineering passive and active safety barriers are widely employed to prevent or delay the initiation or propagation of domino effects. In case of engineering safety barriers, however, the performance of each barrier plays a key role in a successful prevention or mitigation of domino effects. In the present study, we have developed a methodology based on Bayesian network to evaluate the effect of engineering safety barriers on fire-induced domino effects in chemical plants. The developed methodology takes into account both the failure probability and the efficiency of fire protection barriers while modeling the escalation of domino effects, all in a single Bayesian network. The application of the methodology is demonstrated using an industrial case study.