Process systems are prone to devastating accidents as they deal with hazardous materials at high temperatures and high pressure. Process plants are also characterized as complex systems as dense clusters of pipes and equipment make it more likely that a primary accident escalates into secondary accidents, leading to a chain of accidents or so-called domino effect. Thus, the implementation of preventive and mitigative safety measures is crucial to maintain the level of risk below the acceptance criteria. To this end, accident scenario modeling and quantitative risk analysis have widely been used to explore the root causes and possible consequences of an accident. Using these methods, not only can the possible risk of an accident be estimated, but the effect of relevant safety measures can be investigated.
The main focus of Accident Modeling and Risk Analysis is on the research and development of methodologies for risk analysis and safety assessment of complex systems to address the needs of the industry in oil and gas and process facilities.
Current Research Areas
- Bayesian data analysis and predictions
- Application of accident precursor data in probability estimation and dependency analysis
- Application of physical reliability models and monitoring in dynamic risk analysis
- Bayesian networks
- Rare accident risk analysis