Abstract
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.
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