A new framework and a simpler method for the development of batch crystallization recipes



This article is corrected by:

  1. Errata: Erratum Volume 58, Issue 4, 1311, Article first published online: 1 March 2012


We use a new generalized dimensionless model of a seeded batch crystallization process to compare results from four crystal growth rate/concentration trajectories: a numerically-computed optimal trajectory, a constant growth rate trajectory, a trajectory proposed by Mullin and Nyvlt [Chem Eng Sci. 1971;26:369–377] that can be calculated without a kinetic model, and a linear concentration-time trajectory. Because the model is generalized and dimensionless it is not specific to any particular solute–solvent system. We show that if seed properties are good, all trajectories achieve a good result, whereas if seed properties are poor all trajectories achieve a poor result. If seed properties are intermediate, the linear concentration trajectory performs much worse than the other trajectories. On the basis of this, we conclude: (1) Linear trajectories and natural cooling trajectories are poor and should be avoided. When evaluating the benefit derived from an optimization of a temperature/saturation concentration trajectory, the appropriate benchmark should be the Mullin-Nyvlt trajectory, which typically performs much better than the linear trajectory and can be calculated knowing only the mass of seeds at the beginning of the batch. (2) Changing seed properties is more likely to improve batch crystallizer performance than optimizing the growth/saturation concentration trajectory. (3) For rapid process development, the most reasonable approach is to use the Mullin-Nyvlt trajectory and seek to improve process performance by adjusting seed properties. © 2010 American Institute of Chemical Engineers AIChE J, 2011