Domino effect analysis of dust explosions using Bayesian networks

Abstract

In most dust explosion accidents, a series of explosions consisting of a primary (dust) explosion and one or more subsequent secondary dust explosion(s) has been reported. Such chain of dust explosions can be referred to as a dust explosion domino effect (DEDE). DEDEs are capable of causing severe onsite and offsite damages to human, assets, and the environment, thus requiring a detailed understanding of the causes, consequences, probabilities, and escalation mechanisms thereof to prevent and mitigate the potential damages. In this research, we have developed a methodology for the probability estimation of DEDEs based on Bayesian network. The application and efficacy of the methodology have been demonstrated via a real-world case study. The results illustrate that the developed methodology can effectively portend the propagation of DEDEs while calculating the respective probabilities.