CLOSED-LOOP DYNAMIC REAL-TIME OPTIMIZATION WITH STABILIZING MODEL PREDICTIVE CONTROL

Abstract

Real-time optimization (RTO) is a supervisory strategy at the upper level of the industrial process automation architecture that computes optimal set-point trajectories that are in turn passed on to the lower-level advanced controller for tracking. In recent times, process industries have been operating plants in an increasingly dynamic environment thus motivating the replacement of the traditional steady-state RTO with a dynamic RTO (DRTO). Conventionally, the DRTO problem involved computing optimal input trajectories without incorporating the e ects of the lower-level controller that motivated the development of a closed-loop DRTO (CL-DRTO). A previously published CL-DRTO strategy optimizes set-point trajectories based on an economic cost function, while including the lower-level MPC calculations with an assumption that the process is open-loop stable. But the economic optimum, in certain process industries, could lead to operating the plant at or around an unstable steady-state. Therefore, the goal of this research is to develop a CL-DRTO formulation that enables handling unstable operating points via an underlying MPC with stability constraints. In this work, we focus on a traditional two-layer DRTO approach due to its close ties to the industrial automation architecture. To this end, a stabilizing MPC that handles trajectory tracking for unstable systems is embedded within the upper-level DRTO, resulting in a multi-level dynamic optimization problem. Subsequently, this optimization problem is reformulated by applying a simultaneous solution approach. The economic bene ts realized by the proposed strategy are illustrated through applications to both linearized and nonlinear dynamic models for single-input single-output (SISO) and multi-input multi-output (MIMO) CSTR case studies.