Domino

Catalysis

Linguistics

Process (computing)

Engineering

Philosophy

Reliability engineering

Bayesian probability

Biology

Domino effect

Bayesian network

Petri net

Nuclear physics

Computer security

Operating system

Risk assessment

Biochemistry

Computer science

Dynamic Bayesian network

Risk analysis (engineering)

Distributed computing

Business

Physics

Stochastic Petri net

Feature (linguistics)

Artificial intelligence

Medicine

Chemistry

Programming language

Dependency (UML)

Machine learning

Process Safety and Environmental Protection

The domino effect accidents in process industries pose a severe threat to human life and the environment and have the potential to affect nearby facilities as well. Numerous techniques, such as the Bayesian network, have been used for modelling the domino effect. However, these techniques have inherent limitations. These include the inability to consider complex behaviour of process equipment in combined loading and the time dependency of equipment failure. In the current study, a Generalised Stochastic Petri-net model, called as DOMINO-GSPN, is developed to model domino effect accident likelihood and its propagation pattern. The proposed technique is capable of modelling time-dependent failure behaviour of the process component in combined loading. The results from the model are useful in monitoring process risk. A case study is used to demonstrate the application and effectiveness of the model. The results from the model are compared with the past study of a Bayesian network-based domino effect model. This comparative analysis highlights the unique feature of the model and its relevance as a domino effect risk assessment and management tool.

Dynamic domino effect risk assessment using Petri-nets