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25th International Conference on Process Control June 3–6, 2025, Štrbské Pleso, Slovakia Wednesday 14:40: Predictive Control Strategies of a Polymerization Production Unit Vargan, J.1, Fikar, M.1, Latifi, A.M.2 1Slovak University of Technology in Bratislava 2Université de Lorraine Polymerization processes exhibit complex dynamics where achieving precise control is essential. This paper compares various predictive control strategies for a multiple-input, multiple-output system. The studied system includes two plants: a nonlinear kinetic model of a propylene polymerization reactor and a linear model of a counter-current shell-and-tube heat exchanger for removing heat from the exothermic reaction. The controlled variables are the reactor temperature and melt flow index, controlled by coolant and product removal flow rates. We evaluate optimal control strategies based on model predictive controllers (MPC) with differing model structures: (i) nonlinear, (ii) linear, and (iii) linear multi-model approaches. The multi-model strategy uses a weighted predicted output, calculated by combining model outputs using weights derived from prediction errors. Unlike traditional multi-model controllers that constrain only the weighted output, our method enforces constraints on all individual model outputs. This enhances robustness, leading to reduced output oscillations, shorter settling times, and fewer constraint violations. Additionally, multi-model controllers maintained computational efficiency and achieved objective function values within 5% of the nonlinear MPC. Acknowledgment: Jozef Vargan acknowledges the support of from Young Researchers Support Programme grant no. 1326. Authors are grateful to Campus France for the financial support by granting a "France Excellence Eiffel" PhD scholarship to Jozef Vargan. This research was funded by the European Union under Horizon Europe Grant Agreement number 101079342 (Fostering Opportunities Towards Slovak Excellence in Advanced Control for Smart Industries). We also acknowledge the contribution of the Slovak Research and Development Agency under the project APVV-21-0019 and EU RePower project VAIA 09I01-03-V04-00024. 15:00: Offset-free Model Predictive Control of the Van de Vusse Reaction Okienková, K.1, Pannocchia, G.2, Vaccari, M.2, Bacci Di Capaci, R.2, Málik, R.1, Dodek, M.1 , Paulusová, J.1, Tárník, M.1 1Slovak University of Technology in Bratislava 2University of Pisa This paper presents the design of the model predictive control (MPC) of the Van de Vusse reaction (VVR) with the state estimation and compares two offset-free reference tracking techniques. The control algorithm thus compensates for the mismatches between the nonlinear plant and the linearized model, as well as other unmeasurable disturbances. In the first approach, the prediction model of the linear MPC is extended by incorporating separate state and output disturbance vectors and their heuristic smoothing. The second approach considers the augmented state-space model and fully integrates the disturbance estimation into the state estimation. State estimation is achieved using the Kalman filter, which provides optimal design in the statistical framework. The effectiveness of the proposed methodology is validated through the simulation of a nonlinear model of the VVR occurring in the continuous-flow stirred tank reactor (CSTR). Simulation results demonstrate the improvement of classical MPC with the state observer by incorporating offset-free reference tracking. 41
