Optimization in seeded cooling crystallization: A parameter estimation and dynamic optimization study
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Abstract
In this solution crystallization study, a population balance model that predicts the crystal size distribution (CSD) is used for targeting, by optimization, the product mean size and the coefficient of variation of the final CSD. The model is robust over a wide range of conditions and predicts the effects of heating during a batch since the kinetics of dissolution have been identified and incorporated into the model. The dynamic temperature profile and the initial seed size distribution are optimized while the initial seed induction time is conferred through knowledge of saturation conditions. This is quite a highly developed method that provides a systematic approach towards optimal operation. Results from optimizations under different objective functions are presented and validated experimentally. © 2007 Elsevier B.V. All rights reserved.
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Affiliations
- University of Sydney
- Nanyang Technological University, Singapore
- Louisiana State University
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- Modeling