Risk analysis using object-oriented Bayesian network: a case study of ammonia leakage of refrigeration system

Dheyaa A. Khudhur, Tuan Amran Tuan Abdullah and Norafneeza Norazahar

International Journal of Reliability and Safety, 2025, vol. 19, issue 2, 107-131

Abstract: The increasing complexity of refrigeration systems has introduced major concerns into industrial safety and assets. This paper aims to develop a risk analysis framework for an ammonia refrigeration system using Object-Oriented Bayesian Network (OOBN). The failure causes of ammonia leakage are identified through a historical review of past accidents over a ten-year period and the Fault Tree (FT) is then constructed. Failure probabilities are quantified using objective data sources (plant-specific accident records) for known failure rates and subjective data sources (expert judgments and fuzzy set theory) for uncertain ones. The OOBN model is employed to analyse and evaluate the leakage risk. The results revealed that valve seal failures and flange breakages are critical factors in ammonia leakage, necessitating top priority in risk management. Moreover, the developed framework provides the decision-makers a robust tool for implementing safety measures to prevent and mitigate ammonia leakage incidents effectively.

Keywords: ammonia; Bayesian network; object-oriented Bayesian network; refrigeration; risk assessment. (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=145522 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijrsaf:v:19:y:2025:i:2:p:107-131

Access Statistics for this article

More articles in International Journal of Reliability and Safety from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().