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    A Fuzzy Bayesian Network–Based Method for Evaluating the Leakage Risk of STS LNG Bunkering

    Source: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering:;2024:;Volume ( 010 ):;issue: 001::page 04023066-1
    Author:
    Wenfen Zhang
    ,
    Yang Yu
    ,
    Bing Wu
    ,
    Chengpeng Wan
    ,
    Niu Yu
    ,
    Yateng Song
    DOI: 10.1061/AJRUA6.RUENG-1204
    Publisher: ASCE
    Abstract: As a promising clean fuel, the liquefied natural gas (LNG) has been considered as the alternative energy for ships and the LNG-fueled vessels has sharply increased in recent years. However, the leakage of LNG has become a serious challenge for the wide application of LNG-fueled vessels, especially during ship-to-ship (STS) bunkering. This study analyzes leakage accidents during STS LNG bunkering, considering its operation process, and develops a fuzzy Bayesian network model to evaluate the probability of LNG leakage during STS LNG bunkering. Sensitivity analysis is carried out to identify the critical risk factors. Moreover, the study simulates the consequence of LNG leakage using the Fire Dynamics Simulator (FDS) software and derives the F-N curve to determine the risk levels of LNG leakage considering the thermal radiation criteria. A case study of LNG-fueled vessel in Ningbo is conducted to verify the proposed method. The results show that the probability of LNG leakage and fire accidents during STS bunkering is 0.93% and 1.3829×10−4 respectively, which is located in the as low as reasonably practicable (ALARP) zone. Afterwards, it is found that uneven force on the ropes and fenders are the crucial factor influencing LNG bunkering. The simulation experiments demonstrate that the high-risk areas of STS LNG bunkering are 4 to 16 m away from the LNG-fueled ship. This study analyzes leakage accidents during ship-to-ship (STS) liquefied natural gas (LNG) bunkering, and develops a fuzzy Bayesian network model to evaluate the probability of LNG leakage during STS LNG bunkering. A case study of LNG-fueled vessel named XINAO in Ningbo is conducted to verify the proposed method. Moreover, the study simulates the consequence of LNG leakage using the Fire Dynamics Simulator (FDS) software and derives the frequency versus number of fatalities (F-N) curve to determine the risk levels of LNG leakage considering the thermal radiation criteria. The results show that the probability of LNG leakage and fire accidents during STS bunkering is 0.93% and 1.3829×10−4 respectively, which is located in the as low as reasonably practical (ALARP) zone. Then, it is found that uneven force on the ropes and fenders are the crucial factor during STS LNG bunkering. The simulation experiments demonstrate that the high-risk areas of STS LNG bunkering are 4–16 m away from the LNG-fueled ship. This paper delivers a remarkable research work providing rule-makers with an insight into the safety management during STS LNG bunkering.
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      A Fuzzy Bayesian Network–Based Method for Evaluating the Leakage Risk of STS LNG Bunkering

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    • ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering

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    contributor authorWenfen Zhang
    contributor authorYang Yu
    contributor authorBing Wu
    contributor authorChengpeng Wan
    contributor authorNiu Yu
    contributor authorYateng Song
    date accessioned2024-04-27T22:44:53Z
    date available2024-04-27T22:44:53Z
    date issued2024/03/01
    identifier other10.1061-AJRUA6.RUENG-1204.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4297400
    description abstractAs a promising clean fuel, the liquefied natural gas (LNG) has been considered as the alternative energy for ships and the LNG-fueled vessels has sharply increased in recent years. However, the leakage of LNG has become a serious challenge for the wide application of LNG-fueled vessels, especially during ship-to-ship (STS) bunkering. This study analyzes leakage accidents during STS LNG bunkering, considering its operation process, and develops a fuzzy Bayesian network model to evaluate the probability of LNG leakage during STS LNG bunkering. Sensitivity analysis is carried out to identify the critical risk factors. Moreover, the study simulates the consequence of LNG leakage using the Fire Dynamics Simulator (FDS) software and derives the F-N curve to determine the risk levels of LNG leakage considering the thermal radiation criteria. A case study of LNG-fueled vessel in Ningbo is conducted to verify the proposed method. The results show that the probability of LNG leakage and fire accidents during STS bunkering is 0.93% and 1.3829×10−4 respectively, which is located in the as low as reasonably practicable (ALARP) zone. Afterwards, it is found that uneven force on the ropes and fenders are the crucial factor influencing LNG bunkering. The simulation experiments demonstrate that the high-risk areas of STS LNG bunkering are 4 to 16 m away from the LNG-fueled ship. This study analyzes leakage accidents during ship-to-ship (STS) liquefied natural gas (LNG) bunkering, and develops a fuzzy Bayesian network model to evaluate the probability of LNG leakage during STS LNG bunkering. A case study of LNG-fueled vessel named XINAO in Ningbo is conducted to verify the proposed method. Moreover, the study simulates the consequence of LNG leakage using the Fire Dynamics Simulator (FDS) software and derives the frequency versus number of fatalities (F-N) curve to determine the risk levels of LNG leakage considering the thermal radiation criteria. The results show that the probability of LNG leakage and fire accidents during STS bunkering is 0.93% and 1.3829×10−4 respectively, which is located in the as low as reasonably practical (ALARP) zone. Then, it is found that uneven force on the ropes and fenders are the crucial factor during STS LNG bunkering. The simulation experiments demonstrate that the high-risk areas of STS LNG bunkering are 4–16 m away from the LNG-fueled ship. This paper delivers a remarkable research work providing rule-makers with an insight into the safety management during STS LNG bunkering.
    publisherASCE
    titleA Fuzzy Bayesian Network–Based Method for Evaluating the Leakage Risk of STS LNG Bunkering
    typeJournal Article
    journal volume10
    journal issue1
    journal titleASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
    identifier doi10.1061/AJRUA6.RUENG-1204
    journal fristpage04023066-1
    journal lastpage04023066-16
    page16
    treeASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering:;2024:;Volume ( 010 ):;issue: 001
    contenttypeFulltext
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