Target linearization and model predictive control of polymerization processes

Modeling and control of prototypical industrial polymerization processes are presented in the presence of disturbances and plant-model mismatch. The process model consists of the material balances for the reactor, energy balance for the reactor, and a simplified dynamic model for the downstream flash separator. The process model is used to develop a TLMPC that separates the target calculation from dynamic regulation in a closed-loop setting. The main feature of the proposed controller (TLMPC) is that the targets are calculated from the nonlinear model, and then used to get the correct linearized model. The linearized model is utilized to calculate the actual control moves for dynamic regulation, and develop an extended Kalman filter for state estimation. The resulting TLMPC controller does not have the computational burdens associated with a full NMPC and gives similar performance to NMPC The TLMPC shows optimal plant startup, product grade transitions, and regulation around a set point for the polymerization process in comparison to a fixed LMPC Robustness with respect to incorrect model kinetic parameters, initial state estimates and measured disturbances is also shown for TLMPC, as compared to LMPC.