TY - JOUR
T1 - Quantitative resilience assessment of chemical process systems using functional resonance analysis method and Dynamic Bayesian network
AU - Zinetullina, Altyngul
AU - Yang, Ming
AU - Khakzad, Nima
AU - Golman, Boris
AU - Li, Xinhong
PY - 2021
Y1 - 2021
N2 - The emergent hazards of chemical process systems cannot be wholly identified and are highly uncertain due to the complicated technical-human-organizational interactions. Under uncertain and unpredictable circumstances, resilience becomes an essential property of a chemical process system that helps it better adapt to disruptions and restore from surprising damages. The resilience assessment needs to be enhanced to identify the accident's root causes on the level of technical-human-organizational interactions, and development of the specific resilience attributes to withstand or recover from the disruptions. The outcomes of resilience assessment are valuable to identify potential design or operational improvements to ensure complex process system functionality and safety. The current study integrates the Functional Resonance Analysis Method and dynamic Bayesian Network for quantitative resilience assessment. The method is demonstrated through a two-phase separator of an acid gas sweetening unit. Aspen Hysys simulator is applied to estimate the failure probabilities needed in the resilience assessment model. The study provides a useful tool for rigorous quantitative resilience analysis of complex process systems on the level of technical-human-organizational interactions.
AB - The emergent hazards of chemical process systems cannot be wholly identified and are highly uncertain due to the complicated technical-human-organizational interactions. Under uncertain and unpredictable circumstances, resilience becomes an essential property of a chemical process system that helps it better adapt to disruptions and restore from surprising damages. The resilience assessment needs to be enhanced to identify the accident's root causes on the level of technical-human-organizational interactions, and development of the specific resilience attributes to withstand or recover from the disruptions. The outcomes of resilience assessment are valuable to identify potential design or operational improvements to ensure complex process system functionality and safety. The current study integrates the Functional Resonance Analysis Method and dynamic Bayesian Network for quantitative resilience assessment. The method is demonstrated through a two-phase separator of an acid gas sweetening unit. Aspen Hysys simulator is applied to estimate the failure probabilities needed in the resilience assessment model. The study provides a useful tool for rigorous quantitative resilience analysis of complex process systems on the level of technical-human-organizational interactions.
KW - Chemical process systems
KW - Dynamic Bayesian network
KW - FRAM
KW - Resilience assessment
UR - http://www.scopus.com/inward/record.url?scp=85090853659&partnerID=8YFLogxK
U2 - 10.1016/j.ress.2020.107232
DO - 10.1016/j.ress.2020.107232
M3 - Article
AN - SCOPUS:85090853659
SN - 0951-8320
VL - 205
JO - Reliability Engineering and System Safety
JF - Reliability Engineering and System Safety
M1 - 107232
ER -