A hybrid model for dynamic analysis of domino effects in chemical process industries

Due to potential domino effect incidents, chemical tank farms pose a significant threat to human life and nearby infrastructure. However, existing approaches for modeling these effects have limitations in accounting for the system's state over time and complex parameter interactions. This study develops a hybrid model for dynamically analyzing domino effects in chemical tank farms to address these limitations. The proposed model integrates Bowtie (BT) for cause-consequence analysis and utilizes the set pair analysis (SPA) method to handle uncertainty. The model actively investigates domino effects by incorporating multi-agent dynamic analysis and simulation providing quantitative and visual representations of consequences, including equipment damage and potential human losses. A case study on methanol tanks demonstrates the model's effectiveness in assessing and managing domino effects. The findings highlight the superior performance of the proposed approach compared to previous models used in dynamic domino effect analysis. In summary, this hybrid model offers a comprehensive and practical approach for assessing and managing the risks associated with domino effects in chemical tanks by integrating BT, SPA, multi-agent dynamic analysis, and simulation.