An industrial case study on the combined identification and offset-free control of a chemical process

For three decades, model predictive control (MPC) has been the flagship advanced control method in the chemical process industries. However, most implementations still use heuristic methods for designing MPC estimators, especially for offset-free MPC implementations. In this paper, we present a recently developed maximum likelihood-based method for the identification of linear augmented disturbance models for use in offset-free MPC. This method provides noise covariances that are used to derive Kalman filters and moving horizon estimators, forgoing the need for manual design and tuning of the estimator. The method is extended to handle closed-loop plant data. The proposed identification method and estimator design are evaluated in industrial-scale, real-world case study of a process at Eastman Chemical’s Kingsport plant. Using this identified model, we reduced the mean stage cost by 38% compared to the performance of the existing, hand-tuned MPC model. •Maximum likelihood identification of disturbance models for offset-free linear MPC.•Method used to identify model from real-world, closed-loop chemical plant data.•Identified model used to design a replacement MPC for the chemical plant.•Tracking performance is improved by 38% over the old, hand-tuned MPC design.