HySchool Webinar #15: Ingrid Marie Stuen (UiB) & Abhishek Subedi (NTNU)

Ingrid Marie Stuen
PhD-candidate at UiB

Detecting Leaks in Hydrogen Gas Grids Using Pressure and Mass Balance

Hydrogen is expected to play a significant role in the future energy sector, increasing the need for efficient and reliable transportation of hydrogen between production sites and end-users. Pipelines are considered a cost-effective option for mid- to long-distance transport. In hydrogen pipeline networks, early and accurate leak detection is critical for economic efficiency, environmental protection, and safety. While hydrogen is not a greenhouse gas, it has significant global warming potential through indirect effects, and emissions must be minimized. 

This study explores how fiscal measurements from gas sales and purchases can be used to detect leaks in hydrogen pipeline networks. Two approaches are investigated: mass balance and pressure point analysis, capturing different signatures of leakage. Emphasis is placed on quantifying detection limits and assessing the conditions under which small leaks can be reliably identified. The results provide insight into the potential of measurement-based monitoring strategies as a complementary tool for improving the safety and efficiency of future hydrogen infrastructure.

Abhishek Subedi
PhD-candidate at NTNU

AI-based Exploration of Socio-Technical Safety Barrier Dynamics in Hydrogen Systems

This presentation introduces quantitative framework for analyzing socio-technical safety barriers in complex hydrogen systems. Building on the premise that safety barriers should be modeled as dynamic socio-technical systems rather than static components, the study integrates system dynamics modeling with an AI- driven system identification approach (SINDy). Using a hydrogen refueling station maintenance procedure as a case study, synthetic datasets are generated to investigate the interactions among human, organizational, and technical elements.

The results demonstrate how governing equations for system behavior can be identified from limited and partially observed data, enabling both interpretable and predictive representations of safety barrier dynamics. The work highlights the context dependent and dynamic role of safety barriers in shaping operational safety and overall system performance, leveraging AI- based system identification techniques. Overall, the study contributes to advancement of quantitative methodologies for socio-technical safety barrier analysis and facilitates a systematic approach to manage emerging risks in complex systems.

Microsoft Teams

Join: https://teams.microsoft.com/meet/369745767799389?p=5fWQGMdppnfiHgi5f2 (external link)
Meeting-ID: 369 745 767 799 389 
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