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<p>This paper’s purpose is to present a risk assessment approach for analysing the LNG (Liquefied Natural Gas) release in onshore terminals during the loading process. This research adopted the fuzzy Bayesian network (FBN) procedure that predicts the likelihood of an LNG release and its consequences using a BN model combined with an FST (Fuzzy Set Theory), it calculates the percentage of major critical causes of LNG release in the loading process more accurately. First, the FST employing an enhanced SAM (Similarity Aggregation Method) to handle incertitude and incline the outcomes to the more credible experts is applied to obtain the probabilities of source causes. Then, the Bayesian Network (BN) is used to predict the possibility of LNG release and perform an accurate identification of its causes and consequences. The procedure is demonstrated with a case study of Algeria’s LNG marine terminal. The results of this paper are the quantification of the risk of the top event, consequences, and update the prior risk occurrence possibility based on new evidence. In addition, sensitivity analysis has been established to validate the risk model. Overall, the methodology will be effective in helping safety professionals and decision-makers support safety management in the LNG loading process.</p>

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An enhanced fuzzy Bayesian network probabilistic model for LNG release during loading operations in an onshore terminal

  • 期刊论文

  • 发表日期：

    2025年7月30日

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  • Sarra Chebli,
  • Cherif Tolba,
  • Youcef Zennir,
  • Yassine Messaadia,
  • Mohamed Benghanem

摘要

This paper’s purpose is to present a risk assessment approach for analysing the LNG (Liquefied Natural Gas) release in onshore terminals during the loading process. This research adopted the fuzzy Bayesian network (FBN) procedure that predicts the likelihood of an LNG release and its consequences using a BN model combined with an FST (Fuzzy Set Theory), it calculates the percentage of major critical causes of LNG release in the loading process more accurately. First, the FST employing an enhanced SAM (Similarity Aggregation Method) to handle incertitude and incline the outcomes to the more credible experts is applied to obtain the probabilities of source causes. Then, the Bayesian Network (BN) is used to predict the possibility of LNG release and perform an accurate identification of its causes and consequences. The procedure is demonstrated with a case study of Algeria’s LNG marine terminal. The results of this paper are the quantification of the risk of the top event, consequences, and update the prior risk occurrence possibility based on new evidence. In addition, sensitivity analysis has been established to validate the risk model. Overall, the methodology will be effective in helping safety professionals and decision-makers support safety management in the LNG loading process.