(57a) Modeling and Simulation of Impinging Jet Crystallizers

The impinging jet crystallizer, developed by Midler et al. [1] more than a decade ago, is recognized as a highly reproducible and reliable industrial crystallization technology that produces small crystals with narrow size distribution. The basic principle in this design is to utilize the high intensity micromixing of fluids to achieve a homogeneous composition of high superstauration before the onset of nucleation. This technology is now being used by various pharmaceutical companies in their commercial drug production [2-4].

Numerous experimental studies have also been carried out by academic as well as industrial researchers to gain a deeper understanding of the impinging jet crystallization process, which can result in more efficient process development and optimization. For various antisolvent and reactive crystallization systems, the dependence of the crystal size distribution on the jet velocity and the inlet concentrations has been investigated [5-7]. Mahajan and Kirwan [5] and Johnson and Prud'homme [8] had characterized the mixing in impinging jets with an overall micromixing time using competitive reactions. The modeling of reactions in impinging jets has been widely studied as well [9, 10]. This paper extends this past computational work to include the population balance equation, with crystal nucleation and growth, to gain a deeper understanding of impinging jet crystallization. The ultimate goal is to speed up the design of impinging jets and tailor the crystal size distribution according to the bioavailability and drug administration requirements.

In this paper, a coupled CFD-Micromixing-Population Balance model [11] was used to simulate the crystallization process in a confined impinging jet crystallizer [8]. In the first part of the study, the CFD-Micromixing algorithm was combined with kinetics of competitive reactions to model the experimental work reported in Johnson and Prud'homme [8]. A parameter sensitivity analysis [12] was performed which guided the selection of model parameters to provide a closer agreement between model predictions and the experimental data.

In the second part of the study, the improved CFD-micromixing model was coupled with the population balance equation to simulate the full crystal size distribution for the antisolvent crystallization of Lovastatin [13] in the confined impinging jet crystallizer. The effects of jet velocity were numerically investigated and compared semi-quantitatively with experimental data in published literature [5]. Good agreement with the experimental data, especially the shape of the distribution, was obtained. The crystal size and distribution width was found to decrease with an increase in inlet jet velocity, which was consistent with the experimental observations. The simulation results showed different degrees of inhomogeneity in the supersaturation and the nucleation and growth rates for different jet velocities.

In the last part of the study, the antisolvent crystallization of two polymorphic forms of L-histidine in the confined impinging jet crystallizer was simulated with the CFD-micromixing-population balance model. The crystal size distribution of the stable and metastable polymorphs was computed for different jet velocities. It was found that, for the range of conditions simulated, the stable polymorph formed a large proportion of the crystals.

To the authors' knowledge, this is the most detailed simulation study on impinging jet crystallizers reported to date. The simulation results show the feasibility of tailoring a specific crystal size distribution or polymorphic ratio by adjusting the operating conditions (for example, jet velocity) of the impinging jet crystallizers. Hence, these computational tools can provide a technological advancement for the process development in the pharmaceutical industry by providing more in-depth understanding of the crystallization process, and reducing the number of trial-and-error experiments required to determine the optimum operating conditions, which reduces the amount of API needed for experiments. Consequently, the process design can be performed much earlier during the drug development, where there is a limited amount of API available.

References

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