A new approach for quantitative risk assessment of gas explosions on FPSO. (15th September 2022)
- Record Type:
- Journal Article
- Title:
- A new approach for quantitative risk assessment of gas explosions on FPSO. (15th September 2022)
- Main Title:
- A new approach for quantitative risk assessment of gas explosions on FPSO
- Authors:
- Fang, Han
Xue, Hongxiang
Tang, Wenyong - Abstract:
- Abstract: Gas explosions on floating, production, storage and off-loading units (FPSO) cause catastrophic consequences. It is vital to perform an accurate risk assessment for preventing these hazards. This paper presents a comprehensive quantitative risk assessment approach for evaluating the whole gas explosion process from initial leakage to final explosion. A stochastic sampling method is proposed to determine the explosion scenarios by using historical statistics data. Secondly three operational barriers: gas detection, emergency shutdown and ignition prevention are arranged to prevent the development from leakage to explosion. The sequential Bayesian Network (BN) predictive model is constructed to describe the hazard development by considering Human and organizational factors (HOFs). As low as reasonably practicable (ALARP) principle is performed to assess the explosion risks. The proposed approach has been applied to a case study focusing on estimating the risk levels for humans and constructions. It turns out that HOFs have significant effect on the normal functions for operational barriers, if ignoring the HOFs effect the accident risks will be underestimated. This approach is especially good at updating the results whenever new data becomes available. Several safety measures can be recommended based on the diagnostic analysis of BN. Highlights: Proposing a new method for obtaining hazard scenarios based on stochastic sampling. Establishing a Bayesian Network toAbstract: Gas explosions on floating, production, storage and off-loading units (FPSO) cause catastrophic consequences. It is vital to perform an accurate risk assessment for preventing these hazards. This paper presents a comprehensive quantitative risk assessment approach for evaluating the whole gas explosion process from initial leakage to final explosion. A stochastic sampling method is proposed to determine the explosion scenarios by using historical statistics data. Secondly three operational barriers: gas detection, emergency shutdown and ignition prevention are arranged to prevent the development from leakage to explosion. The sequential Bayesian Network (BN) predictive model is constructed to describe the hazard development by considering Human and organizational factors (HOFs). As low as reasonably practicable (ALARP) principle is performed to assess the explosion risks. The proposed approach has been applied to a case study focusing on estimating the risk levels for humans and constructions. It turns out that HOFs have significant effect on the normal functions for operational barriers, if ignoring the HOFs effect the accident risks will be underestimated. This approach is especially good at updating the results whenever new data becomes available. Several safety measures can be recommended based on the diagnostic analysis of BN. Highlights: Proposing a new method for obtaining hazard scenarios based on stochastic sampling. Establishing a Bayesian Network to calculate hazard probability by considering multi-state variables and Fuzzy set theory. The most effect factor for causing gas explosion hazards are analyzed by probability updating function of BN. … (more)
- Is Part Of:
- Ocean engineering. Volume 260(2022)
- Journal:
- Ocean engineering
- Issue:
- Volume 260(2022)
- Issue Display:
- Volume 260, Issue 2022 (2022)
- Year:
- 2022
- Volume:
- 260
- Issue:
- 2022
- Issue Sort Value:
- 2022-0260-2022-0000
- Page Start:
- Page End:
- Publication Date:
- 2022-09-15
- Subjects:
- Explosion -- Quantitative risk assessment -- Bayesian network -- Human and organizational factor -- FPSO
Ocean engineering -- Periodicals
Ocean engineering
Periodicals
620.4162 - Journal URLs:
- http://www.sciencedirect.com/science/journal/00298018 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.oceaneng.2022.112006 ↗
- Languages:
- English
- ISSNs:
- 0029-8018
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 6231.280000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 23981.xml