Data-Driven Model Reduction, Scientific Frontiers, and Applications ()
- Texas A&M University
- College Station, TX
- Joe C. Richardson Petroleum Engineering Building (RICH) 910
- Pallavi Kumari, Artie McFerrin Department of Chemical Engineering
- Risk Analysis of Rare Events Through Bi-Directionality in Fault and Event Trees
- Pallavi Kumari
- M. Nazmul Karim
Risk assessment of rare but catastrophic events in process industry deals with challenges of data scarcity and uncertainty estimation . Data scarcity and uncertainty both are prevalent with operator responses as well as process data . Difficulty presented by sparse data for accounting uncertainty in failure probability of control layers can be solved by Bayesian model. Also, it is assumed that data collected for risk assessment comes from strictly consistent operating conditions. It leads to unaccountability of uncertainty associated with source-to-source variability in data sources for process data as well as operator responses . This work implements bi-directionality in fault and event trees and estimating uncertainty due to source-to-source variability of data sources has not gained much attention for chemical process industry. For that it uses
- R. He et al. , A Quantitative Risk Analysis Model Considering Uncertain Information, Process Safety and Environmental Protection 118 (), 361–370.
- N. Khakzad, F. Khan, and P. Amyotte, Dynamic Safety Analysis of Process Systems by Mapping Bow-Tie Into Bayesian Network, Process Safety and Environmental Protection 91(1) (), 46–53.
- H. Yu et al., A Flexible Hierarchical Bayesian Modeling Technique for Risk Analysis of Major Accidents, Risk Analysis 37(9) (), 1668–1682.