A simulation-based approach for resilience assessment of process system

A case of LNG terminal system

Journal Article (2024)
Author(s)

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

Ming Yang (University of Tasmania, TU Delft - Safety and Security Science)

Enrico Zio (Mines Paris – PSL, Politecnico di Milano)

Xinhong Li (Xi'an University of Architecture and Technology)

Xiaofei Lin (Anhui University of Technology)

Xinjie Huang (Anhui University of Technology)

Qun Wu (Anhui University)

Research Group
Safety and Security Science
DOI related publication
https://doi.org/10.1016/j.ress.2024.110207
More Info
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Publication Year
2024
Language
English
Research Group
Safety and Security Science
Volume number
249
Article number
110207
Downloads counter
353
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Abstract

System resilience denotes the capacity to uphold desired system performance in the face of disruptions. Evaluating the resilience of a process system necessitates a thorough consideration of the intricate interplay between its components and the pivotal role of process parameters in reflecting the repercussions of disruptions on the system. This paper introduces an integrated methodology that takes into account component interactions and leverages process data for the resilience assessment of a process system. The proposed methodology comprises four key components: system structure analysis, disruption impacts analysis, process simulation, and resilience assessment. Firstly, the system structure is meticulously scrutinized using a P-graph model. This analysis encompasses the assessment of the significance and interplay of components, as well as the evaluation of how component failures affect the system's overall processes. Secondly, a Markov model is devised to examine the state transition process of components and quantifies the maintenance time needed for failed components. Subsequently, a simulation model is formulated to acquire real-time process parameters in the presence of disruptive events. Finally, the system's performance response function (PRF) is derived from the normalization of these process parameters. Building upon this foundation, a resilience assessment is conducted with a focus on the PRF. To illustrate the effectiveness of this methodology, an LNG terminal system is employed as an exemplar.

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