Please use this identifier to cite or link to this item: https://doi.org/10.1002/aic.11879
Title: Nonlinear model predictive control for the polymorphic transformation of l-glutamic acid crystals
Authors: Hermanto, M.W.
Chiu, M.-S. 
Braatz, R.D.
Keywords: Extended predictive self-adaptive control
Nonlinear model predictive control
Pharmaceutical crystallization
Polymorphism
Unscented Kalman filter
Issue Date: Oct-2009
Source: Hermanto, M.W.,Chiu, M.-S.,Braatz, R.D. (2009-10). Nonlinear model predictive control for the polymorphic transformation of l-glutamic acid crystals. AIChE Journal 55 (10) : 2631-2645. ScholarBank@NUS Repository. https://doi.org/10.1002/aic.11879
Abstract: Polymorphism, a phenomenon where a substance can have more than one crystal forms, has recently become a major interest to the food, speciality chemical, and pharmaceutical industries. The different physical properties for polymorphs such as solubility, morphology, and dissolution rate may jeopardize operability or product quality, resulting in significant effort in controlling crystallization processes to ensure consistent production of the desired polymorph. Here, a nonlinear model predictive control (NMPC) strategy is developed for the polymorphic transformation of L-glutamic acid from the metastable a-form to the stable fi-form crystals. The robustness of the proposed NMPC strategy to parameter perturbations is compared with temperature control (T-control), concentration control (C-control), and quadratic matrix control with successive linearization (SL-QDMC). Simulation studies show that T-control is the least robust, whereas C-control performs very robustly but long batch times may be required. SL-QDMC performs rather poorly even when there is no plant-model mismatch due to the high process nonlinearity, rendering successive linearization inaccurate. The NMPC strategy shows good overall robustness for two different control objectives, which were both within 7% oftheir optimal values, while satisfying all constraints on manipulated and state variables within the specified batch time. © 2009 American Institute of Chemical Engineers.
Source Title: AIChE Journal
URI: http://scholarbank.nus.edu.sg/handle/10635/64295
ISSN: 00011541
DOI: 10.1002/aic.11879
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