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Integration of fuzzy reliability analysis and consequence simulation to conduct risk assessment
Institution:1. College of Safety Science and Engineering, Nanjing Tech University, Nanjing, Jiangsu, 210009, China;2. Jiangsu Key Laboratory of Hazardous Chemicals Safety and Control, Nanjing, Jiangsu, 210009, China;1. Université de Toulouse, INSA, UPS, Mines d’Albi, ISAE, ICA (Institut Clément Ader), 135 Avenue de Rangueil, Cedex, 31077, Toulouse, France;2. Defence Technology Institute, 47/433 Moo 3, Ban Mai, Pak Kret, Nonthaburi, 11120, Thailand;3. Faculty of Engineering, Burapha University, 169 Long-Hard Bangsaen Road, Chonburi, 20131, Thailand;1. College of Ocean Science and Engineering, Shanghai Maritime University, Shanghai, 201306, China;2. Anyang Fire and Rescue Detachment, Anyang, 455000, China;1. School of Safety Engineering, Beijing Institute of Petrochemical Technology, Beijing, 102617, China;2. Research Institute of Macro-Safety Science, University of Science and Technology Beijing, Beijing, 10083, China;3. NHC Key Laboratory for Engineering Control of Dust Hazard, University of Science and Technology Beijing, Beijing, 100083, China
Abstract:Losses of containment within the natural gas network, located in most populated areas, could cause environmental damage, injuries, or even death. Accordingly, it is pivotal to adopt proper approaches to assess and mitigate the risk arising from potential losses. Within this context, it is required to exploit solid reliability and consequence analysis techniques. To this end, this paper presents a methodology established on the integration of a Fuzzy Bayesian Network and consequence simulation. The Bayesian Network is more flexible and realistic than classic approaches because it can consider conditional probabilities and prior information. Furthermore, Leaky Noisy-OR Gates are exploited to allow an easier filling of the Conditional Probability Tables. This task is performed through expert elicitation, adopting Intuitionistic Fuzzy Set Theory and Similarity Aggregation Method. Finally, the severity analysis is performed via a software, named Safeti, which provides an accurate evaluation of the consequences. To show the applicability of the framework, a pressure regulator of a Natural Gas Regulating and Metering Station is considered as case study. The proposed approach can assist asset managers in evaluating the risk arising from the operations, and, accordingly, it can guide them in making maintenance-related decisions to assure the safety of the operations.
Keywords:Risk-based framework  Bayesian network  Fuzzy set theory  Natural gas distribution system  Quantitative risk analysis
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