In most dust explosion accidents, a series of explosions consisting of a primary (dust) explo-sion 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 environ-ment, 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 demon-strated 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.
© 2016 The Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.