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Online DEKF for State Estimation in Semi-Batch Free-Radical Polymerization Reactors

Abstract:

In this work, a dynamic filter for nonlinear systems is implemented for on-line sate estimation, dynamic data reconciliation, molar mass distribution (MMD) monitoring, and feedback control of polymerization processes. A discrete-time extended Kalman filter (DEKF) is designed for a free-radical polymerization reaction that synthetizes polyacrylamide using potassium persulfate as initiator in a semi-batch reactor. For improving the performance of the filter, its free parameters are tuned using a metaheuristic technique. The Automatic Continuous Online Monitoring of Polymerization reactions (ACOMP) is used for obtaining online measurement values in various experiments. Total moles of monomer, initiator and solvent and the corresponding moments of the dead polymers are estimated by the DEKF using weight average molecular weight, monomer concentration and reactor volume as real-time measurements. State estimation permits to estimate polymer properties that cannot be measured directly while reconciling measured values. A tailor made filter/controller module is formulated and implemented in Python environment supporting multiple programming paradigms such as object-oriented and functional programming styles.