Formation of Dataset for Fuzzy Quantitative Risk Assessment of LNG Bunkering SIMOPs

Hongjun Fan, Hossein Enshaei and Shantha Gamini Jayasinghe
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Hongjun Fan: Australian Maritime College (AMC), College of Sciences and Engineering, University of Tasmania, Launceston, TAS 7248, Australia
Hossein Enshaei: Australian Maritime College (AMC), College of Sciences and Engineering, University of Tasmania, Launceston, TAS 7248, Australia
Shantha Gamini Jayasinghe: Australian Maritime College (AMC), College of Sciences and Engineering, University of Tasmania, Launceston, TAS 7248, Australia

Data, 2022, vol. 7, issue 5, 1-13

Abstract: New international regulations aimed at decarbonizing maritime transportation are positively contributing to attention being paid to the use of liquefied natural gas (LNG) as a ship fuel. Scaling up LNG-fueled ships is highly dependent on safe bunkering operations, particularly during simultaneous operations (SIMOPs); therefore, performing a quantitative risk assessment (QRA) is either mandated or highly recommended, and a dynamic quantitative risk assessment (DQRA) has been developed to make up for the deficiencies of the traditional QRA. The QRA and DQRA are both data-driven processes, and so far, the data of occurrence rates (ORs) of basic events (BEs) in LNG bunkering SIMOPs are unavailable. To fill this gap, this study identified a total of 41 BEs and employed the online questionnaire method, the fuzzy set theory, and the Onisawa function to the investigation of the fuzzy ORs for the identified BEs. Purposive sampling was applied when selecting experts in the process of online data collection. The closed-ended structured questionnaire garnered responses from 137 experts from the industry and academia. The questionnaire, the raw data and obtained ORs, and the process of data analysis are presented in this data descriptor. The obtained data can be used directly in QRAs and DQRAs. This dataset is first of its kind and could be expanded further for research in the field of risk assessment of LNG bunkering.

Keywords: maritime; decarbonization; LNG bunkering; simultaneous operations (SIMOPs); quantitative risk assessment (QRA); data (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2022
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