Framework design for weight-average molecular weight control in semi-batch polymerization

A framework that embraces a state-of-the-art sensor, multi-objective dynamic optimization, nonlinear state estimation and control, is designed and implemented to achieve target weight-average molecular weight trajectories. The Automatic Continuous Online Monitoring of Polymerization reactions (ACOMP) is combined for the first time with a nonlinear state observer for full polymer characterization and signal processing. A hybrid variation of the discrete-time extended Kalman filter (h-DEKF) is formulated based on an auto-tuning procedure that uses a stochastic global optimization technique. A number of optimal policies are generated and experimentally tested. Results are provided through investigations into the free-radical aqueous polymerization of acrylamide using potassium persulfate as initiator. [Display omitted] •The framework shows the combination of advanced sensors, dynamic optimization, nonlinear state estimation and control into a single environment.•The ACOMP system provided real-time data of polymer properties, constituting the basis for implementing nonlinear state estimation towards full polymer characterization.•Improving the quality of raw noisy data allowed a better control action that achieves polymers with target properties.•Validation experiments underlined excellent performance and robustness of the framework.