Process (computing)

Engineering

Reliability engineering

Bayesian probability

Process safety

Bayesian network

Computer security

Work in process

Operating system

Risk assessment

Data mining

Computer science

Risk analysis (engineering)

Simulation

Fuzzy logic

Inference

Operations management

Artificial intelligence

Medicine

Fault tree analysis

Uncertainty analysis

Machine learning

Bayesian inference

Journal of Loss Prevention in the Process Industries

Quantitative risk assessment (QRA) has played an effective role in improving safety of process systems during the last decades. However, QRA conventional techniques such as fault tree and bow-tie diagram suffer from drawbacks as being static and ineffective in handling uncertainty, which hamper their application to risk analysis of process systems. Bayesian network (BN) has well proven as a flexible and robust technique in accident modeling and risk assessment of engineering systems. Despite its merits, conventional applications of BN have been criticized for the utilization of crisp probabilities in assessing uncertainty. The present study is aimed at alleviating this drawback by developing a Fuzzy Bayesian Network (FBN) methodology to deal more effectively with uncertainty. Using expert elicitation and fuzzy theory to determine probabilities, FBN employs the same reasoning and inference algorithms of conventional BN for predictive analysis and probability updating. A comparison between the results of FBN and BN, especially in critically analysis of root events, shows the outperformance of FBN in providing more detailed, transparent and realistic results.

Safety analysis of process systems using Fuzzy Bayesian Network (FBN)