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Reliability engineering for safety prognostic using bayesian approach: a case study of a green hydrogen prototype.

Khatima Chafaa1, Islam Hadj Mohamed Guetarni2, Nassima Aissani2

  • 1Laboratory of Industrial Safety Engineering and Sustainable Development, Institute of Maintenance and Industrial Safety, University of Oran 2 Mohamed Ben Ahmed, Oran, Algeria. chafaa.khatima@univ-oran2.dz.

Environmental Science and Pollution Research International
|October 18, 2025
PubMed
Summary

This study enhances green hydrogen production safety by analyzing a prototype system. Fault Tree Analysis and Bayesian networks identified critical components like the switch and regulator, improving maintenance and reliability for a sustainable energy future.

Keywords:
Bayesian networkHydrogenInferencesProductionPrototypeThe fault tree

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Area of Science:

  • Renewable Energy Systems
  • Reliability Engineering
  • Risk Assessment

Background:

  • Hydrogen is a key future fuel, but its systems require rigorous reliability and safety studies.
  • Current research in hydrogen system reliability is limited, hindering the safe scaling of this technology.
  • Improving the safety and efficiency of green hydrogen production is crucial for energy transition.

Purpose of the Study:

  • To conduct a risk analysis of a green hydrogen production prototype system.
  • To develop a tool for safety prognosis and optimize maintenance strategies.
  • To identify critical components impacting the overall safety and reliability of the system.

Main Methods:

  • Fault Tree Analysis (FTA) and Bayesian network (BN) inferences were employed for risk assessment.
  • Probabilistic techniques were compared to identify system vulnerabilities and their safety impacts.
  • Quantitative importance analysis (Marginal Importance Factor, Critical Importance Factor) was used to rank component criticality.

Main Results:

  • System unavailability was calculated as 0.3783 (FTA) and 0.37575 (BN), showing method agreement.
  • The switch, 12V regulator, and electrolyzer were identified as the most critical components affecting reliability.
  • Solar panels, humans, and anemometers showed zero impact, suggesting high reliability or areas for modeling refinement.

Conclusions:

  • Combining multiple probabilistic approaches provides a comprehensive safety assessment for hydrogen systems.
  • The study pinpoints critical components requiring prioritized maintenance, enhancing overall system reliability.
  • Findings support the development of robust safety protocols for scaling hydrogen technologies and advancing renewable energy solutions.