(273e) Resilient Design and Operations of Chemical Process Systems Using Robust Optimization
In this work, we address the resilient design and operations of process systems in response to disruption events. A novel quantitative measure of resilience is proposed as the ratio of the quantity of products manufactured with disruptive events to that without disruptive events. Five resilience enhancement strategies are then introduced, including selecting the most resilient technology/process alternatives, increasing the capacities of operating processes, employing parallel operating processes, building backup processes, and optimizing the operating levels after the occurrence of disruptive events. A general framework for resilience optimization is further proposed to incorporate the quantitative measure of resilience and the resilience enhancement strategies into process design and operations. In the first step of the proposed resilience optimization framework, a preliminary risk assessment is performed for a given system to identify the disruptive events that are worth considering in process design and operations. The numbers of failed processes for the identified disruptive events and the recovery time of each process are used as input parameters for resilient design and operations. In the second step, a multiobjective two-stage adaptive robust mixed-integer fractional programming model is formulated. There are two objective functions: the first objective function is to maximize the resilience under the worst-case realization of disruptive events, and the second objective function is to minimize the total capital cost. Both objective functions are independent of external processes and volatile markets, thus reflecting the intrinsic properties of the given system. The resulting optimization model has a three-level structure: the first level determines the optimal network configuration, equipment capacities, and capital costs; the second level determines the worst-case realization of disruptive events; the third level determines the optimal number of available processes and operating levels in each time period. To tackle the computational challenges stemming from the multilevel structure and the nonlinear objective function, a tailored global optimization method is proposed by integrating the inexact parametric algorithm and the column-and-constraint generation algorithm. The applicability of the proposed resilience optimization framework is illustrated through two applications on process design and planning of a chemical process network and superstructure optimization of shale gas processing and natural gas liquids recovery processes, respectively.
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