Resilience assessment of chemical processes using operable adaptive sparse identification of systems

Abstract

Ensuring resilience in process systems is essential for safe and sustainable operations. Resilience is a property of the system which is characterized by the absorption, adaptation, and recovery performances of the system. Fault prognosis predicts the system's behavior after the occurrence of a fault and the time to failure which in-turn helps in determining the intervention strategies for restoring the system to its normal operating conditions. In the proposed framework, an adaptive modeling technique called operable adaptive sparse identification of system is implemented for fault prognosis. The time to failure of the system is determined based on the predicted system behavior. The system's absorption, adaptation, and recovery performances are modeled for different available intervention strategies, and they are evaluated based on a resilience metric. A case study is conducted on a batch reactor in thermal runaway condition and various intervention strategies are employed to demonstrate the applicability of the framework.