Real-time quantitative risk analysis and routing optimization of gaseous hydrogen tube trailer transport: A Bayesian network and Dijkstra algorithm combining approach

Abstract

With increasing global energy demand, the volume and scale of hydrogen energy transportation are also on the rise. Gaseous hydrogen tube trailers, as the most flexible mode of hydrogen transport, present risk characteristics such as dynamism, suddenness, and potential for severe consequences. However, current methods for real-time risk assessment and route optimization during transportation are limited, lacking in both efficiency and accuracy. To address this issue, we propose a comprehensive and novel real-time risk assessment and path optimization system for gaseous hydrogen tube trailer transport. The System Theory Process Analysis (STPA) method is employed to identify risk factors associated with this transport modality. We combine a Bayesian network model with real-time observational data to derive dynamic failure probabilities for various routes. The potential consequences of transportation accidents are calculated using a computational model and computational fluid dynamics (CFD). By analyzing population density along the routes, we estimate the number of fatalities resulting from accidents, leading to a dynamic assessment of accident consequences. Finally, we consider societal impacts, economic costs, time costs, carbon emissions, the proximity to environmentally sensitive areas, and locations prone to fire and explosion hazards to select the optimal transportation route using Dijkstra's algorithm. The findings of this research will provide valuable insights for the safe management and sustainable development of gaseous hydrogen tube trailer transport.