Application of Fuzzy Bayesian Network in Dynamic Risk Analysis of Explosion in Process Industries

Background: Fire and explosion in process industries can have catastrophic consequences. The frequency of these accidents has led safety experts to underscore the importance of conducting thorough risk analysis studies to implement effective control measures. Materials and Methods: A tank gas leak was initially selected as a scenario for probable explosion risk assessment. The bowtie technique was utilized to analyze the potential causes and consequences of the selected incident. A fuzzy logic approach was used to quantify the probability of essential events, and the Bayesian network was employed for dynamic risk analysis. Results: Using the bowtie method, 24 fundamental causes were identified for tank gas leaks (main scenario). Moreover, 4 safety barriers against the prevention of the selected scenario were identified, and evaluation of the success and failure of these safety barriers led to the identification of 5 potential consequences. According to the Bayesian network and fuzzy analysis, the inappropriate installation was the most influential event, with a near miss identified as the most likely consequence of the central event. Discussion: Combining fuzzy theory and Bayesian networks allows fuzzy numbers to reduce uncertainty in basic events and safety barriers and leverages deductive and inductive reasoning. Conclusion: The results demonstrate that applying fuzzy logic and Bayesian networks could solve the uncertainty and static nature of traditional quantitative risk analysis studies.

Application of Fuzzy Bayesian Network in Dynamic Risk Analysis of Explosion in Process Industries Keywords:

Dynamic risk analysis , Fuzzy Bayesian network , Explosion , Process industries