Development of a FRAM-based framework to identify hazards in a complex system. (January 2020)

• Record Type: Journal Article
• Authors: Yu, Mengxi; Quddus, Noor; Kravaris, Costas; Mannan, M. Sam
• Is Part Of: Journal of loss prevention in the process industries. Volume 63(2020)
• Publication Date: 2020-01
• Subjects: Hazard identification -- Socio-technical system -- Hybrid simulator -- FRAM -- Association rules; Chemical industries -- Safety measures -- Periodicals; 660.2804
• DOI: 10.1016/j.jlp.2019.103994
• Languages: English
• ISSNs: 0950-4230

Abstract

Abstract: System-based approaches such as Functional Resonance Analysis Model (FRAM) are developed to model the complex interactions of system variables and their performance variabilities that may lead to a hazardous scenario in a complex system. However, they have limitations to be applied in process industries for hazard identification since they are heavily based on qualitative analysis and expert elicitations. To overcome the limitations of the system-based hazard identification, the study developed a FRAM-based framework to integrate a human performance model, an equipment performance model, and a first-principle based chemical process model into a hybrid simulator, which will be able to aid hazard analysis in the process industries. The simulator is capable of simulating the performance variabilities of the functions through the aggregation of mathematical models within a complex system, which can be used to simulate potential hazard situations and identify the corresponding interactions. Interaction analysis is conducted by applying association rule mining to the simulated data. The impact of the interactions among upstream functions on the performance of downstream functions can be identified by interpreting the rules, whose antecedents contain upstream functions and consequents contain downstream functions. Highlights: A FRAM based framework is used to identify hazards of a complex system. Hybrid simulator models the interactions of different functions. Performance variabilities of functions are aggregated by mathematical models. Interactions leading to hazardous scenario identified by association rule mining.