Dynamic risk analysis of bunkering operations based on system dynamics simulation and Bayesian network

• A dynamic risk assessment method involving system dynamics simulation and Bayesian network is proposed • The system dynamics model enables dynamic probability prediction under the coupling mechanism of faults • A Bayesian network-based consequence model is established to assess the effect of safety barriers • A case study demonstrates the effectiveness of the proposed methodology for methanol bunkering under SIMOPs • The societal risks associated with methanol leakage are assessed across varying population densities This paper proposes a dynamic risk assessment method based on system dynamics (SD) simulation to deal with both the complexity of systems involving simultaneous operations (SIMOPs) and their dynamic evolution over time. A fault tree model is constructed and transformed into an SD model comprising four feedback loops, enabling dynamic probability prediction under coupling mechanisms while accounting for each loss of containment (LOC). Critical hazard factors are identified and ranked based on mutual information (MI), allowing prioritization of safety interventions. Consequence-probability modelling is carried out by establishing a Bayesian network (BN)-based event tree considering safety barriers (SBs). The severity of potential consequences is evaluated using fire modelling in ALOHA. Individual and societal risk values are subsequently calculated, followed by the development of risk mitigation measures at a bunkering station in the Port of Shanghai, China, as a practical case study. The proposed mitigation strategies effectively reduce leakage probability and associated risks. The proposed framework effectively characterizes the temporal evolution of risk, provides enhanced representation of time-dependent risk dynamics compared with traditional static quantitative risk assessment models, and offers a transferable basis for alternative marine fuels and port operations. To the best of our knowledge, the combination of bow-tie analysis, dynamic simulation, and Bayesian networks for temporal methanol bunkering risk assessment remains unexplored in the current academic literature.