Economic Control Structure Selection for Two-Layered Real-Time Optimization Systems

Abstract

In industrial chemical plants, the selection of controlled variables, manipulated variables, and setpoint values directly impacts the economics of plant operation. The economic operation of a controlled plant can be improved using measurements to optimize the setpoint values and/or change the control structure online. These corrective actions are typically implemented by a supervisory setpoint control layer, such as a real-time optimization system. In a recent work, the authors studied the steady-state back-off approach for control structure selection that selects the optimal control structure and setpoint values by minimizing the average economic cost while guaranteeing feasibility in the presence of disturbances. In the present paper, we extend the aforementioned back-off approach by considering the presence of an upper real-time optimization layer. We present formulations for control structure selection that consider changes in the setpoint values as a function of measured (or estimated) disturbances and changes to the control structure when the set of active constraints changes. The usefulness of the proposed formulations is demonstrated in simulation on a linear example, an evaporator process, and a reaction-distillation plant with recycle.