Abstract
The performance of fire protection measures plays a key role in the prevention and mitigation of fire escalation (fire domino effect) in process plants. In addition to passive and active safety measures, the intervention of firefighting teams can have a great impact on fire propagation. In the present study, we have demonstrated an application of dynamic Bayesian network to modeling and safety assessment of fire domino effect in oil terminals while considering the effect of safety measures in place. The results of the developed dynamic Bayesian network-prior and posterior probabilities-have been combined with information theory, in the form of mutual information, to identify optimal firefighting strategies, especially when the number of fire trucks is not sufficient to handle all the vessels in danger.
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