Domino effects are a type of catastrophic event that can occur when multiple process units or equipment fail simultaneously, often triggered by a single initiating cause. These events can have devastating consequences for people, the environment, and the economy. In this article, we will review various approaches to modeling and managing domino effects in the process industries.

Domino effects are typically caused by overpressure, fire, or other initiating causes that trigger a chain reaction of subsequent failures. The quantitative assessment of these events is crucial for identifying potential risks and developing effective strategies for prevention and mitigation. Cozzani and Salzano (2004) proposed a methodology for assessing the domino effects caused by overpressure, which involves simulating various scenarios and evaluating the consequences.

In recent years, there has been an increasing interest in using agent-based modeling and simulation to assess domino effects. Zhang et al. (2018) developed a model called DAMS, which uses agents to represent different process units or equipment and simulates their behavior during a domino effect. This approach allows for the assessment of various scenarios and the evaluation of different mitigation strategies.

Fire is another common initiating cause of domino effects. Chen et al. (2018) proposed an innovative methodology for quickly modeling the spatial-temporal evolution of domino accidents triggered by fire. This involves using a combination of simulation models and data analytics to predict the spread of fire and evaluate the effectiveness of different firefighting strategies.

Cost-benefit management is another important aspect of managing domino effects. Chen et al. (2020) developed an approach for quickly modeling the cost-benefit trade-offs associated with intentional domino effects in chemical industrial areas. This involves using a combination of simulation models and economic analysis to evaluate the potential consequences of different scenarios.

Event sequence diagrams are also useful for evaluating emergency response actions during fire-induced domino effects. Zhou et al. (2016) proposed an approach that uses event sequence diagrams to model the sequence of events leading up to a domino effect and evaluates the effectiveness of different emergency response strategies.

The analysis of domino accidents triggered by vapor cloud explosions is another important area of research. Salzano and Cozzani (2005) developed a methodology for analyzing these types of events, which involves simulating various scenarios and evaluating the consequences.

In addition to these approaches, there have been several studies on the classification and discussion of methods for modeling and managing domino effects in the process industries. Chen et al. (2020) proposed a thorough classification and discussion of these approaches, which includes simulation-based methods, agent-based models, and cost-benefit analysis.

Finally, Khan and Abbasi (2004) developed a model for domino effect analysis in chemical process industries. This involves using a combination of simulation models and data analytics to predict the spread of fire and evaluate the effectiveness of different firefighting strategies., domino effects are a type of catastrophic event that can have devastating consequences for people, the environment, and the economy. Various approaches have been proposed for modeling and managing these events, including simulation-based methods, agent-based models, cost-benefit analysis, and event sequence diagrams. These approaches can be used to identify potential risks, develop effective strategies for prevention and mitigation, and evaluate the effectiveness of different emergency response actions.

References:

Chen, C., Reniers, Genserik, Zhang, L. (2020). A thorough classification and discussion of approaches for modeling and managing domino effects in the process industries. Safety Science, 125, 104618.

Cozzani, V., Salzano, E. (2004). The quantitative assessment of domino effects caused by overpressure. Journal of Hazardous Materials, 107(3), 67–80.

Khakzad, Nima, Landucci, Gabriele, Reniers, Genserik. (2017). Application of dynamic Bayesian network to performance assessment of fire protection systems during domino effects. Reliability Engineering & System Safety, 167, 232–247.

Khan, Faisal I., Abbasi, S. A. (2004). Models for domino effect analysis in chemical process industries. Process Safety Progress, 17(2), 107–123.

Landucci, Gabriele, Argenti, Francesca, Tugnoli, Alessandro, Cozzani, Valerio. (2015). Quantitative assessment of safety barrier performance in the prevention of domino scenarios triggered by fire. Reliability Engineering & System Safety, 143, 30–43.

Pavlova, Yulia, Reniers, Genserik. (2011). A sequential-move game for enhancing safety and security cooperation within chemical clusters. Journal of Hazardous Materials, 186(1), 401–406.

Reniers, Genserik. (2010). An external domino effects investment approach to improve cross-plant safety within chemical industries. Reliability Engineering & System Safety, 95(3), 253–261.

Salzano, E., Cozzani, V. (2005). The analysis of domino accidents triggered by vapor cloud explosions. Journal of Loss Prevention in the Process Industries, 18(2), 137–147.

Zhang, L., Wang, Y., Liu, X. (2018). A simulation-based approach for evaluating the effectiveness of different emergency response strategies during fire-induced domino effects. Safety Science, 102, 123–135.

Zhou, J., Reniers, Genserik, Zhang, L. (2016). An event sequence diagram-based approach for evaluating the effectiveness of different emergency response strategies during fire-induced domino effects. Journal of Loss Prevention in the Process Industries, 46, 231–242.