Combined nonlinear model predictive control and moving horizon estimation for a copolymerization process

We implement and test a nonlinear model predictive control (NMPC) setup for first principles models, at the example of a copolymerization process. We address the important question of state and parameter estimation within a moving horizon estimator (MHE) framework. The employed “real-time iteration” algorithm for NMPC and MHE allows computation times 500 times faster than real-time. The MHE is able to estimate both states and unknown parameters well, and the overall scheme allows fast transients in grade changes. Finally, we test an “optimizing” control NMPC scheme which maximizes production while keeping the grade specifications.

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