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Human error probability assessment for LNG Bunkering based on Fuzzy Bayesian Network-CREAM Model

Citation

Fan, H and Enshaei, H and Jayasinghe, SG, Human error probability assessment for LNG Bunkering based on Fuzzy Bayesian Network-CREAM Model, Journal of Marine Science and Engineering, 10, (3) Article 333. ISSN 2077-1312 (2022) [Refereed Article]


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DOI: doi:10.3390/jmse10030333

Abstract

Liquified natural gas (LNG) as a marine fuel has gained momentum as the maritime industry moves towards a sustainable future. Since unwanted LNG release may lead to severe consequences, performing quantitative risk assessment (QRA) for LNG bunkering operations has become mandatory according to some regulations. Human error is a main contributor to the risks, and the human error probabilities (HEPs) are essential for inclusion in a QRA. However, HEPs data are unavailable in the LNG bunkering industry so far. Therefore, this study attempts to infer HEPs through on-site safety philosophical factors (SPFs). The cognitive reliability and error analysis method (CREAM) was adopted as a basic model and modified to make it suitable for HEP assessment in LNG bunkering. Nine common performance condition (CPC) indicators were identified based on the fuzzy ranking of 23 SPF indicators (SPFIs). A Bayesian network (BN) was built to simulate the occurrence probabilities of different contextual control modes (COCOMs), and a conditional probability table (CPT) for the COCOM node with 19,683 possible combinations in the BN was developed according to the CREAM’s COCOM matrix. The prior probabilities of CPCs were evaluated using the fuzzy set theory (FST) based on data acquired from an online questionnaire survey. The results showed that the prior HEP for LNG bunkering is 0.009841. This value can be updated based on the re-evaluation of on-site SPFIs for a specific LNG bunkering project to capture the dynamics of HEP. The main innovation of this work is realizing the efficient quantification of HEP for LNG bunkering operations by using the proposed fuzzy BN-CREAM model.

Item Details

Item Type:Refereed Article
Keywords:maritime, LNG bunkering, quantitative risk assessment, human error, Bayesian network, CREAM, fuzzy set
Research Division:Engineering
Research Group:Maritime engineering
Research Field:Marine engineering
Objective Division:Transport
Objective Group:Water transport
Objective Field:International sea transport of liquefied gas
UTAS Author:Fan, H ( Hongjun Fan)
UTAS Author:Enshaei, H (Dr Hossein Enshaei)
UTAS Author:Jayasinghe, SG (Dr Shantha Jayasinghe Arachchillage)
ID Code:150549
Year Published:2022
Web of Science® Times Cited:1
Deposited By:Seafaring and Maritime Operations
Deposited On:2022-06-20
Last Modified:2022-06-20
Downloads:0

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