A time-dependent probabilistic model for fire accident analysis. (January 2020)

• Record Type: Journal Article
• Authors: Yang, Ruochen; Khan, Faisal; Taleb-Berrouane, Mohammed; Kong, Depeng
• Is Part Of: Fire safety journal. Volume 111(2020)
• Publication Date: 2020-01
• Subjects: Fire accident -- Accident modeling -- Fire risk assessment -- Fire probability modeling -- Floating LNG; Fire prevention -- Periodicals; Incendies -- Prévention -- Recherche -- Périodiques; Fire prevention -- Research; Periodicals; 628.92205
• DOI: 10.1016/j.firesaf.2019.102891
• Languages: English
• ISSNs: 0379-7112

Abstract

Abstract: Fire is among the most common and devastating accidents in the hydrocarbon production and processing industry. Many efforts have been dedicated to assessing fire accident likelihood; however, most of these studies considered fire probability as spatially distributed, ignoring the time dependence of the fire accident scenario. In this study, a robust and practical model is proposed to analyze fire accident probability in a congested and complex processing area. This model integrates a conditional probability approach – the Bayesian network (BN) - with a time-dependent scenario evolution approach, Stochastic Petri Nets (SPN). The computational fluid dynamics (CFD) tool is used to estimate the time-dependent scenario consequences. The outcome of the model is fire probability as a function of time and location caused by a specific leak rate and leak duration. A case study of fire probability analysis in a Floating Liquified Natural Gas facility (FLNG) is presented. This study demonstrates the importance of the temporal dependency of the fire scenario and the proposed model can serve as the required tool for time-dependent fire probability analysis, further safety measures' application and system optimization.