A dynamic decision framework for improving tank fire emergency decision-making using influence diagrams and micro-economics

Abstract

This study proposes a dynamic decision-making framework for fire emergency management in hazardous chemical storage tank areas, based on Bayesian networks and influence diagrams. The framework comprises two core components: the generation of initial response strategies and the evaluation of them. By analyzing the thermal radiation coupling effect between tanks, this study introduces the closeness centrality metric to identify key tank nodes, for which multiple emergency response strategies are further formulated. To evaluate these emergency response strategies, this study employs influence diagrams to evaluate the utility of each strategy and proposes a simplified utility function. The study further evaluates the impact of domino effects by dynamically updating the influence diagram, recalculating the closeness centrality, and adjusting the key nodes based on the assumption that a fire spreads to adjacent tanks. A case study illustrates that, across a range of firefighting and cooling efficiency assumptions, the framework successfully adapts to changing scenarios, optimizing strategies to improve the timeliness and accuracy of emergency responses. This provides a viable optimization method for complex tank fire emergency decision-making.