Abstract
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.
Highlights
The technology is moving forward, and the process safety measures are increasingly becoming automated to facilitate the safe environment in the chemical process plants
To the authors’ knowledge, this paper will, for the first time, integrate Functional resonance analysis method (FRAM) and Dynamic Bayesian network (DBN) to facilitate the assessment of the ability of a chemical process system, its operators, and associated organization to respond to unprecedented disruption, recover from unexpected damages, and learn from those events
The probabilities of failures (PoF) are obtained from Aspen Hysys Dynamics simulation for the process unit using the strip charts for the parameters characterizing the performance of the process system of interest [21]
Summary
The technology is moving forward, and the process safety measures are increasingly becoming automated to facilitate the safe environment in the chemical process plants. Cai et al [7] considered the performance and time-related properties in resilience assessment, in which high availability and a short time to recovery characterized the system's high resilience They applied DBN to predict the future values of availability and their temporal variation by employing the data available at the current state. Zinetullina et al [48] used a bow-tie (BT) diagram to support the development of the DBN model and applied it to analyze the resilience of a two-phase separator operating in a harsh environment In both approaches, resilience is defined as the ability of the system to sustain high functionality under disruptive effects or restore the low functionality state to a high functionality state. The resilience parameters can better be characterized by actions of the objects rather than by their presence ([35]b)
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