Improving Safety and Efficiency in Green Hydrogen Production

Strategic Insights -

Investigations into the reliability of hydrogen systems are crucial in addressing the energy challenges of the future. Researchers at the University of Oran 2 have conducted a case study on a green hydrogen prototype, employing both Fault Tree Analysis and Bayesian network inferences to identify critical components and optimize maintenance strategies. The study has provided valuable insights into advancing research and facilitating the transition to hydrogen as a renewable energy source.

Key Takeaways:

  • The study aimed to improve the safety and efficiency of green hydrogen production by conducting a risk analysis of a prototype system and providing a tool for safety prognosis.
  • Both Fault Tree Analysis and Bayesian network inferences were employed to identify critical components and optimize maintenance strategies, demonstrating an agreement between both methods.
  • The unavailability of the system was calculated to be 0.3783 using Fault Tree Analysis and 0.37575 using the Bayesian network.
  • Quantitative importance analysis revealed that the switch had a Marginal Importance Factor (MIF) of 0.938644, and Critical Importance Factor (CIF) of 0.837, while the electrolyzer had a MIF of 0.63046, and CIF of 0.0165.
  • Several other parts showed moderate importance, while some had zero-impact factors, such as solar panels, humans, and anemometer, suggesting high reliability or modeling refinements.
  • The study highlighted the effectiveness of combining multiple probabilistic approaches for comprehensive safety assessment and pinpointed the components most in need of maintenance.

Statistics:

  • The unavailability of the system was calculated to be 0.3783 using Fault Tree Analysis and 0.37575 using the Bayesian network.
  • The switch had a Marginal Importance Factor (MIF) of 0.938644, and Critical Importance Factor (CIF) of 0.837.
  • The electrolyzer had a Marginal Importance Factor (MIF) of 0.63046, and Critical Importance Factor (CIF) of 0.0165.

Sources:

  • Environmental Science and Pollution Research, 2025.
  • Springer Heidelberg, Tiergartenstrasse 17, D-69121 Heidelberg, Germany.
  • University of Oran 2 Mohamed Ben Ahmed, Oran, Algeria.