An integrated approach to quantitative resilience assessment in process systems

Journal Article (2024)
Author(s)

Hao Sun (TU Delft - Safety and Security Science, China University of Petroleum (East China), Anhui University of Technology)

Ming Yang (TU Delft - Safety and Security Science)

Haiqing Wang (China University of Petroleum (East China))

DOI related publication
https://doi.org/10.1016/j.ress.2023.109878 Final published version
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Publication Year
2024
Language
English
Bibliographical Note
Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
Journal title
Reliability Engineering and System Safety
Volume number
243
Article number
109878
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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.

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