Dynamic Bayesian network based approach for risk analysis of hydrogen generation unit leakage. (8th October 2019)
- Record Type:
- Journal Article
- Title:
- Dynamic Bayesian network based approach for risk analysis of hydrogen generation unit leakage. (8th October 2019)
- Main Title:
- Dynamic Bayesian network based approach for risk analysis of hydrogen generation unit leakage
- Authors:
- Chang, Yuanjiang
Zhang, Changshuai
Shi, Jihao
Li, Jiayi
Zhang, Shenyan
Chen, Guoming - Abstract:
- Abstract: Hydrogen leakage is a crucial risk for the hydrogen generation unit, which would lead to the potential fire and explosion accidents. Hydrogen leakage risk analysis is the essential alternative to ensure the safety of the hydrogen generation process. This paper presents a dynamic risk analysis methodology regarding the hydrogen leakage in the hydrogen generation unit by using the dynamic Bayesian network, which is employed to address the potential uncertainty and dynamic nature underlying the leakage risk of the hydrogen generation unit. A case study of hydrogen generation unit is carried out to demonstrate the applicability and advantage of the proposed methodology. Results indicate that the leakage probability of hydrogen generation unit can be significantly decreased within one year through equipment repair. Furthermore, the failure and repair rates of overflow alarm and pressure sensor are the most contributory factors to the hydrogen generation unit leakage. Finally, some active mitigative suggestions are presented to further reduce the leakage risk of the hydrogen generation unit. Highlights: A DBN-based methodology for dynamic risk prediction of hydrogen generation unit leakage was proposed. The dynamic risk of hydrogen generation unitleakage and accident consequences were achieved. The most contributory factors leading to leakage failure of hydrogen generation unit were identified. Active measures to mitigate the leakage failure risk in the process ofAbstract: Hydrogen leakage is a crucial risk for the hydrogen generation unit, which would lead to the potential fire and explosion accidents. Hydrogen leakage risk analysis is the essential alternative to ensure the safety of the hydrogen generation process. This paper presents a dynamic risk analysis methodology regarding the hydrogen leakage in the hydrogen generation unit by using the dynamic Bayesian network, which is employed to address the potential uncertainty and dynamic nature underlying the leakage risk of the hydrogen generation unit. A case study of hydrogen generation unit is carried out to demonstrate the applicability and advantage of the proposed methodology. Results indicate that the leakage probability of hydrogen generation unit can be significantly decreased within one year through equipment repair. Furthermore, the failure and repair rates of overflow alarm and pressure sensor are the most contributory factors to the hydrogen generation unit leakage. Finally, some active mitigative suggestions are presented to further reduce the leakage risk of the hydrogen generation unit. Highlights: A DBN-based methodology for dynamic risk prediction of hydrogen generation unit leakage was proposed. The dynamic risk of hydrogen generation unitleakage and accident consequences were achieved. The most contributory factors leading to leakage failure of hydrogen generation unit were identified. Active measures to mitigate the leakage failure risk in the process of hydrogen generation were presented. … (more)
- Is Part Of:
- International journal of hydrogen energy. Volume 44:Number 48(2019)
- Journal:
- International journal of hydrogen energy
- Issue:
- Volume 44:Number 48(2019)
- Issue Display:
- Volume 44, Issue 48 (2019)
- Year:
- 2019
- Volume:
- 44
- Issue:
- 48
- Issue Sort Value:
- 2019-0044-0048-0000
- Page Start:
- 26665
- Page End:
- 26678
- Publication Date:
- 2019-10-08
- Subjects:
- Dynamic Bayesian network -- Hydrogen generation unit -- Leakage failure -- Risk analysis
Hydrogen as fuel -- Periodicals
Hydrogène (Combustible) -- Périodiques
Hydrogen as fuel
Periodicals
665.81 - Journal URLs:
- http://www.sciencedirect.com/science/journal/03603199 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.ijhydene.2019.08.065 ↗
- Languages:
- English
- ISSNs:
- 0360-3199
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 4542.290000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 12027.xml