MPC Performance Monitoring of a Rigorously Simulated Industrial Process

We address in this paper the application of a recently proposed MPC performance monitoring method to a rigorously simulated industrial process. The methodology aims at detecting possible sources of suboptimal performance of linear offset-free MPC algorithms by analysis of the prediction error sequence, discriminating between the presence of plant/model mismatch and incorrect disturbance/state estimation, and proposing for each scenario an appropriate corrective action. We focus on the applicability of the method to large-scale industrial systems, which typically comprise a block structure, devising efficient and scalable diagnosis and correction procedures. We also discuss and support the application of this method when the controlled plant shows a mild nonlinear behavior mainly associated with operating point changes. A high-fidelity dynamic simulation model of a crude distillation unit was developed in UniSimr Design and used as representative test bench. Results show the efficacy of the method and indicate possible research directions for further improvements.