Analysis of system resilience in escalation scenarios involving LH2 bunkering operations

In the context of global energy transition and decarbonization efforts, resilience emerges as a critical factor in ensuring the reliability and adaptability of industrial infrastructure systems. This paper introduces a novel model rooted in Dynamic Bayesian Networks (DBNs) for the quantitative assessment of the resilience of engineered systems in the event of escalation scenarios triggered by domino effect. The model is integrated into a systematic, step-by-step procedure capable of evaluating the ability of complex systems to recover functionality from subsequent disruptions occurring at different times throughout the operational lifecycle. Leveraging DBNs, the methodology captures the dynamic interactions and feedback among subsystems or components, overcoming the limitations associated with conventional methods. The innovative methodology has been applied to a case study involving a liquid hydrogen (LH2) bunkering system, illustrating its effectiveness in assessing resilience amidst evolving accident scenarios. The results demonstrate the significant impact of escalation scenarios on system resilience and underscore the importance of proper implementation and management of safety measures and mitigation strategies. The proposed approach provides a valuable insight into system performance and empowers proactive risk management in the face of escalation scenarios, ensuring the continued operation and success of industrial operations in an uncertain and interconnected reality.