Human Error Probability Assessment for LNG Bunkering Based on Fuzzy Bayesian Network-CREAM Model
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-02-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/10/3/333 |
| _version_ | 1797470273336770560 |
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| author | Hongjun Fan Hossein Enshaei Shantha Gamini Jayasinghe |
| author_facet | Hongjun Fan Hossein Enshaei Shantha Gamini Jayasinghe |
| author_sort | Hongjun Fan |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T19:35:19Z |
| format | Article |
| id | doaj.art-2643ec1ac6794dfbb7533e94a686325b |
| institution | Directory Open Access Journal |
| issn | 2077-1312 |
| language | English |
| last_indexed | 2024-03-09T19:35:19Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj.art-2643ec1ac6794dfbb7533e94a686325b2023-11-24T01:56:49ZengMDPI AGJournal of Marine Science and Engineering2077-13122022-02-0110333310.3390/jmse10030333Human Error Probability Assessment for LNG Bunkering Based on Fuzzy Bayesian Network-CREAM ModelHongjun Fan0Hossein Enshaei1Shantha Gamini Jayasinghe2Australian Maritime College (AMC), College of Sciences and Engineering, University of Tasmania, Launceston, TAS 7248, AustraliaAustralian Maritime College (AMC), College of Sciences and Engineering, University of Tasmania, Launceston, TAS 7248, AustraliaAustralian Maritime College (AMC), College of Sciences and Engineering, University of Tasmania, Launceston, TAS 7248, AustraliaLiquified 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.https://www.mdpi.com/2077-1312/10/3/333maritimeLNG bunkeringquantitative risk assessmenthuman errorBayesian networkCREAM |
| spellingShingle | Hongjun Fan Hossein Enshaei Shantha Gamini Jayasinghe Human Error Probability Assessment for LNG Bunkering Based on Fuzzy Bayesian Network-CREAM Model Journal of Marine Science and Engineering maritime LNG bunkering quantitative risk assessment human error Bayesian network CREAM |
| title | Human Error Probability Assessment for LNG Bunkering Based on Fuzzy Bayesian Network-CREAM Model |
| title_full | Human Error Probability Assessment for LNG Bunkering Based on Fuzzy Bayesian Network-CREAM Model |
| title_fullStr | Human Error Probability Assessment for LNG Bunkering Based on Fuzzy Bayesian Network-CREAM Model |
| title_full_unstemmed | Human Error Probability Assessment for LNG Bunkering Based on Fuzzy Bayesian Network-CREAM Model |
| title_short | Human Error Probability Assessment for LNG Bunkering Based on Fuzzy Bayesian Network-CREAM Model |
| title_sort | human error probability assessment for lng bunkering based on fuzzy bayesian network cream model |
| topic | maritime LNG bunkering quantitative risk assessment human error Bayesian network CREAM |
| url | https://www.mdpi.com/2077-1312/10/3/333 |
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