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Formation of dataset for fuzzy Quantitative Risk Assessment of LNG Bunkering SIMOPs

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Fan, H, Enshaei, H ORCID: 0000-0002-5649-7015 and Jayasinghe, SG ORCID: 0000-0002-3304-9455 2022 , 'Formation of dataset for fuzzy Quantitative Risk Assessment of LNG Bunkering SIMOPs' , Data, vol. 7, no. 5 , pp. 1-13 , doi: 10.3390/data7050060.

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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.

Item Type: Article
Authors/Creators:Fan, H and Enshaei, H and Jayasinghe, SG
Keywords: maritime, decarbonization, LNGbunkering, simultaneous operations (SIMOPs), quantitative risk assessment (QRA), data
Journal or Publication Title: Data
Publisher: MDPI AG
ISSN: 2306-5729
DOI / ID Number: 10.3390/data7050060
Copyright Information:

Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons 4.0 International (CC BY 4.0) license (https://creativecommons.org/licenses/by/4.0/).

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