Combining uncertainty reasoning and deterministic modeling for risk analysis of fire-induced domino effects

Abstract

A fire-induced domino effect can be considered as a combination of an uncertain event and deterministic events, the occurrence of primary fire accidents can be considered as an uncertain event, and the occurrence of secondary fire accidents could be considered as deterministic events since accident escalation is controlled by fire heat radiation dominantly. Uncertainty reasoning approaches apply to assess fire accident probabilities before a primary accident occurs, while deterministic modeling approaches apply to model domino evolution process after a primary accident occurs. Therefore, uncertainty reasoning approaches and deterministic modeling approaches are complementary and can be combined to perform a comprehensive risk analysis of domino effects. This paper proposes a framework combining uncertainty reasoning approach and deterministic modeling approach to assess the fire accident probability before a primary accident occurs and to model the domino evolution process after a primary accident occurs, respectively. Dynamic Bayesian network (DBN) is used to assess fire accident probabilities of units within a chemical plant. After a primary accident occurs, the fire synergistic effect model (FSEM) is used to model the domino effect evolution process for different primary accident scenarios. This study demonstrated that combining uncertainty reasoning and deterministic modeling can deliver a macro image of the comprehensive risk of chemical plants both before and after a primary accident occurs.