In this paper, a risk assessment methodology is presented to identify, analyze, and evaluate the risk associated with different consequences of a liquefied natural gas (LNG) process facility from west Algeria. Bow‐tie analysis (BT) is used to identify the possible causes of hazardous events and the dangerous outcomes and consequences resulting from the failure of the safety barriers. Moreover, the bow‐tie is mapped into Bayesian networks (BNs) to overcome some limitation of dependencies and updating in the events. Finally, the risk‐reducing measures implemented in the process are examined using BNs. The performances of the safety barriers are investigated considering the occurrence of fire and explosion in the facility. BN uses diagnostic (posterior) and predictive (prior) analysis to calculate the probabilities of the components and to predict the occurrence frequency of the consequences and the effectiveness of safety barriers. The results from the BN model indicate that pool fire is the most likely scenario that potentially causes damage to the LNG process.
Fault tree analysis (FTA) is one of the most prominent techniques used in risk analysis, this method aimed to identify how component failures lead to system failure using logical gates (i.e. AND, OR gates). However, some limitations appear on FTA due to its static structure. Bayesian networks (BNs) have become a popular technique used in reliability analysis; it represents a set of random variables and their conditional dependencies. This paper discusses the advantages of Bayesian networks over fault tree in reliability and safety analysis. Also, it shows the ability of BN to update probabilities, to represent multi-state variables, dependent failures, and common cause failure. An example taken from the literature is used to illustrate the application and compare the results of both fault tree and bayesian networks techniques.
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