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.

Traditional risk assessment approaches mainly focus on the pre-failure scenarios with certain information. For complex systems, the scope of risk assessment needs to be extended to include the post-failure phase; because the emerging hazards of these systems cannot be wholly identified and are usually highly uncertain. Thus, resilience assessment needs to be investigated. Most of the existing literature quantify resilience based on a system's performance loss caused by disruptions. These studies fail to assess the probability of a system to sustain or restore to a normal operational state after disruptions occur, how this probability changes with time, and how fast the system can be restored. The dynamic and probabilistic characteristics of resilience must be considered in systemic resilience assessment, in which the engineered system, human and organizational factors, and external disruptions are considered. This paper aims to develop a dynamic Bayesian network (DBN)-based approach to the probabilistic assessment of the system resilience by incorporating temporal processes of adaption and recovery into the analysis of system functionality. The proposed method also provides a new way to define resilience in terms of the probability of system functionality change during and after a disruption. A case study on the Chevron refinery accident is used to demonstrate the applicability of the proposed methodology.

The Arctic is known for its abundant reserve of natural resources. Last decade has seen some exploration and production activities in this region. The assurance of safe operations in this region is a critical and challenging task because of the harsh environment, the remoteness of operation sites, the limited infrastructure, and resources available in response to emergent situations, the application of costly equipment and facilities, and the sensitive marine environment. For complex process systems operating in a harsh environment, the scope of conventional risk assessment is not enough because of the highly uncertain environment, and its impacts on equipment performance. Risk assessment needs to be extended to include both the pre-failure and the post-failure phases. Additionally, risk assessment approaches under normal operating, and environmental conditions may not be applicable in the Arctic regions with unique and uncertain characteristics of the harsh environment. Therefore, this study aims to develop a quantitative resilience assessment method for process units operating under Arctic extreme conditions. Dynamic Bayesian network (DBN) is applied to model the probabilistic relationships between causes and effects in a dynamic manner. The proposed method is applied to the resilience assessment of a separator (as part of an oil production system). The proposed approach will help reveal the critical operating parameters under extreme conditions for process units. It also helps identify potential design improvement to enhance process safety.