Data-based modeling and control of nylon-6,6 batch polymerization

This work addresses the problem of modeling the complex nonlinear behavior of a nylon-6,6 batch polymerization process and subsequently tracking trajectories of the important process variables, namely the reaction medium temperature and reactor pressure, using model predictive control (MPC). To this end, a data-based multi-model approach is proposed in which local linear models are identified from previous batch data using latent variable regression and then combined using a continuous weighting function that arises from fuzzy c-means clustering. The resulting data-based model is used to formulate a trajectory tracking predictive controller. Through simulation studies, the modeling approach is shown to capture the major nonlinearities of the process, and closed-loop simulation results demonstrate the efficacy of the proposed predictive controller and its advantages over conventional proportional-integral (PI) trajectory tracking.