An integrated approach to quantitative resilience assessment in process systems

Journal article (2024)

Authors

H. Sun Safety and Security Science - TPM , China University of Petroleum (East China), Anhui University of Technology
M. Yang Safety and Security Science - TPM
Haiqing Wang China University of Petroleum (East China)
Research Group
Safety and Security Science (TPM) (TU Delft)
Copyright: © 2024 H. Sun, M. Yang, Haiqing Wang
DOI
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https://doi.org/10.1016/j.ress.2023.109878
Process safety Human-technical systems, FRAM, resilience
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Published Date

2024

Language

English

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Faculty

Technology, Policy and Management

Department

Values Technology and Innovation

Research Group

Safety and Security Science

Abstract

Chemical process systems are becoming more automated and complex, which leads to increased interaction and interdependence between the human and technical elements of process systems. This urges the need for updating the safety assessment method by treating “safety” as an emergent property of a system. Uncertainty comes together with complexity. To enhance system ability of dealing with uncertain disruptions, this paper proposes a quantitative resilience assessment method by modeling the failure propagation (initiated by a disruption) across the functional units of a system. The Functional Resonance Analysis Method (FRAM) is utilized to model the system operation to represent the relationship among its function units and to consider the interactions among human-technical factors. Then, a Cascading Failure Propagation Model (CFPM) is developed to quantify the fault propagation process and reflect the system functionality changes over time for resilience assessment. The proposed method is applied to a propane-feeding control system. The results show that it can help practitioners understand the process of fault propagation and risk increase, identify potential ways to design a more resilient system to respond to uncertain disruptions/attacks, and provide a real-time dynamic resilience profile to support decision-making.