{"title":"基于贝叶斯网络的LNG加注SIMOP动态定量风险评估","authors":"Hongjun Fan, Hossein Enshaei, Shantha Gamini Jayasinghe","doi":"10.1016/j.joes.2022.03.004","DOIUrl":null,"url":null,"abstract":"<div><p>Liquified natural gas (LNG) bunkering simultaneous operations (SIMOPs) refers to the operations (such as cargo operations, port activities and ship maintenance) occurring around LNG bunkering. SIMOPs pose new risks to LNG bunkering, because the operations are dynamically interlocked in which the occurrence probabilities of potential consequences change at different times due to commencement or completion of specific SIMOP events. However, traditional static risk assessment approaches are not able to take the dynamic nature of these new risks into account. This article proposes a dynamic quantitative risk assessment (DQRA) methodology based on the Bayesian network (BN) to develop better understanding of dynamic risks of LNG bunkering SIMOPs. The methodology is demonstrated and evaluated through a truck-to-ship LNG bunkering case study. The results and discussion of the case study validate the utility of the proposed methodology and demonstrate that BNs are efficient in performing the probability calculations and are flexible in conducting causal diagnosis. The main innovation of this work is realizing the quantification of risks at different times, which reflects the most essential time-changing characteristics of risks associated with LNG bunkering SIMOPs.</p></div>","PeriodicalId":48514,"journal":{"name":"Journal of Ocean Engineering and Science","volume":null,"pages":null},"PeriodicalIF":13.0000,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Dynamic quantitative risk assessment of LNG bunkering SIMOPs based on Bayesian network\",\"authors\":\"Hongjun Fan, Hossein Enshaei, Shantha Gamini Jayasinghe\",\"doi\":\"10.1016/j.joes.2022.03.004\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Liquified natural gas (LNG) bunkering simultaneous operations (SIMOPs) refers to the operations (such as cargo operations, port activities and ship maintenance) occurring around LNG bunkering. SIMOPs pose new risks to LNG bunkering, because the operations are dynamically interlocked in which the occurrence probabilities of potential consequences change at different times due to commencement or completion of specific SIMOP events. However, traditional static risk assessment approaches are not able to take the dynamic nature of these new risks into account. This article proposes a dynamic quantitative risk assessment (DQRA) methodology based on the Bayesian network (BN) to develop better understanding of dynamic risks of LNG bunkering SIMOPs. The methodology is demonstrated and evaluated through a truck-to-ship LNG bunkering case study. The results and discussion of the case study validate the utility of the proposed methodology and demonstrate that BNs are efficient in performing the probability calculations and are flexible in conducting causal diagnosis. The main innovation of this work is realizing the quantification of risks at different times, which reflects the most essential time-changing characteristics of risks associated with LNG bunkering SIMOPs.</p></div>\",\"PeriodicalId\":48514,\"journal\":{\"name\":\"Journal of Ocean Engineering and Science\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":13.0000,\"publicationDate\":\"2023-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Ocean Engineering and Science\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2468013322000535\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, MARINE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Ocean Engineering and Science","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2468013322000535","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MARINE","Score":null,"Total":0}
Dynamic quantitative risk assessment of LNG bunkering SIMOPs based on Bayesian network
Liquified natural gas (LNG) bunkering simultaneous operations (SIMOPs) refers to the operations (such as cargo operations, port activities and ship maintenance) occurring around LNG bunkering. SIMOPs pose new risks to LNG bunkering, because the operations are dynamically interlocked in which the occurrence probabilities of potential consequences change at different times due to commencement or completion of specific SIMOP events. However, traditional static risk assessment approaches are not able to take the dynamic nature of these new risks into account. This article proposes a dynamic quantitative risk assessment (DQRA) methodology based on the Bayesian network (BN) to develop better understanding of dynamic risks of LNG bunkering SIMOPs. The methodology is demonstrated and evaluated through a truck-to-ship LNG bunkering case study. The results and discussion of the case study validate the utility of the proposed methodology and demonstrate that BNs are efficient in performing the probability calculations and are flexible in conducting causal diagnosis. The main innovation of this work is realizing the quantification of risks at different times, which reflects the most essential time-changing characteristics of risks associated with LNG bunkering SIMOPs.
期刊介绍:
The Journal of Ocean Engineering and Science (JOES) serves as a platform for disseminating original research and advancements in the realm of ocean engineering and science.
JOES encourages the submission of papers covering various aspects of ocean engineering and science.