Nonlinear Adaptive Robust Optimization Model and Algorithm for Resilience Analysis and Enhancement

Abstract

Resilience is a critical ability of process systems to adapt and recover rapidly from the impact of disruptive events. This paper addresses the resilient design and operations of process systems after the occurrence of disruption events. A general framework for resilience optimization is proposed with three steps. First, a set of disruptive events are identified through a preliminary risk assessment. Next, a nonlinear two-stage adaptive robust optimization model is developed. The model consists of two objective functions to optimize the resilience and total capital cost simultaneously. Additionally, the model determines network configuration, equipment capacities, and capital costs in the first stage, and the number of available processes and operating levels in each time period in the second stage. Finally, the resulting problem is solved with a tailored solution algorithm, which integrates the inexact parametric algorithm for handling the fractional objective function and the column-and-constraint generation algorithm for handling the multi-level optimization problem. The applicability of the proposed framework is illustrated through the superstructure optimization of shale gas processing and natural gas liquids recovery processes.