Optimal operation of batch enantiomer crystallization: From ternary diagrams to predictive control

• Published in: AIChE journal 2018-05, Vol.64 (5), p.1618-1637
• Main Authors: Curitiba Marcellos, Caio Felippe, Durand, Helen, Kwon, Joseph Sang‐Il, Gomes Barreto, Amaro, Laranjeira da Cunha Lage, Paulo, Bezerra de Souza, Maurício, Secchi, Argimiro Resende, Christofides, Panagiotis D.
• Format: Article
• Language: English
• Subjects: batch crystallization control; Crystallization; Crystals; enantiomeric separation; model predictive control; Nuclei; Optimization; population balance models; Predictive control; Seeds; ternary diagram; Yield
• ISSN: 0001-1541; 1547-5905
• DOI: 10.1002/aic.16028

In this work, the modeling and control of batch crystallization for racemic compound forming systems is addressed in a systematic fashion. Specifically, a batch crystallization process is considered for which the initial solution has been pre‐enriched in the desired enantiomer to enable crystallization of only the preferred enantiomer. A method for determining desired operating conditions (composition of the initial pre‐enriched solution and temperature to which the mixture must be cooled for maximum yield) for the batch crystallizer based on a ternary diagram for the enantiomer mixture in a solvent is described. Subsequently, it is shown that the information obtained from the ternary diagram, such as the maximum yield attainable from the process due to thermodynamics, can be used to formulate constraints for an optimization‐based control method to achieve desired product characteristics such as a desired yield. The proposed method is demonstrated for the batch crystallization of mandelic acid in a crystallizer with a fines trap that is seeded with crystals of the desired enantiomer. The process is controlled with an optimization‐based controller to minimize the ratio of the mass of crystals obtained from nuclei to the mass obtained from seeds while maintaining the desired enantioseparation. © 2017 American Institute of Chemical Engineers AIChE J, 64: 1618–1637, 2018