Document Type
Article
Publication Date
9-1-2018
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.
Publication Source (Journal or Book title)
Control Engineering Practice
First Page
12
Last Page
23
Recommended Citation
Salas, S., Ghadipasha, N., Zhu, W., Mcafee, T., Zekoski, T., Reed, W., & Romagnoli, J. (2018). Framework design for weight-average molecular weight control in semi-batch polymerization. Control Engineering Practice, 78, 12-23. https://doi.org/10.1016/j.conengprac.2018.06.004
