Handling Parametric Drift in Batch Crystallization Using Predictive Control with R2R Model Parameter EstimationFinancial support from the Extreme Science and Engineering Discovery Environment (TG-CCR120003), the National Science Foundation (CBET-0967291), and the NSF Graduate Research Fellowship (DGE-1144087) given to Michael Nayhouse is gratefully acknowledged.
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2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved. In this work, we develop a run-to-run (R2R) model parameter estimation scheme based on moving horizon estimation (MHE) concepts for the modeling of batch-to-batch process model parameter variation using a polynomial regression scheme in a moving horizon fashion. Then, a model predictive controller (MPC) with the proposed parameter estimation scheme is applied to a kinetic Monte Carlo (MC) simulation model of a batch crystallization process used to produce hen-egg-white (HEW) lysozyme crystals. The average crystal shape distribution of crystals produced from the closed-loop simulation of the batch crystallizer under the MPC with the proposed R2R model parameter estimation scheme is much closer to a desired set-point value compared to that of MPC based on the nominal process model.
