Thermodynamic models offer a fast, reliable, and cost-effective method to select the best solvent or solvent mixtures for crystallization of solid components. To optimize the performance of the unit operations which produce active pharmaceutical ingredients (APIs), the physical properties of the solute and solvent must be known. Solubility prediction is very crucial in the fine and specialty chemical industries, as the total cost of production is high in most cases. In this study, the solubility of three chemical compounds, 3-pentadecylphenol, lovastatin, and valsartan, in different solvents and solvent mixtures were studied experimentally and theoretically. The thermodynamic models of the UNIFAC and the NRTL-SAC model were used for prediction. The results of the prediction from the two models and their average relative deviation for the three model compounds showed a better performance for the NRTL-SAC model compared to the UNIFAC. For the case of lovastatin and valsartan, the NRTL-SAC model gives the average relative deviation of 0.2401 and 0.3843, respectively. Because of the flexibility of the NRTL-SAC program code that is written for the phase behavior prediction, it can be used for further analysis and optimization of the performance of crystallization processes (i.e., solvent screening and yield of the process). This study shows that the NRTL-SAC model can be used effectively in pharmaceutical industry, especially for solvent screening purposes.

In this study, an effort has been made to predict the solid–liquid equilibrium (SLE) behavior of different solids (pharmaceuticals) in many common solvents and their mixtures. A modified optimization of a recent thermodynamic model, the NRTL–SAC model, was used in all stages of calculation (VLE, LLE, and SLE predictions). The batch cooling–antisolvent crystallization process was simulated for seven model molecules from the initial temperature to the final temperature and for the volume fraction of each solvent. The feasible region of temperature for each crystallization case was calculated based on the bubble-point temperature of the solvent mixture and the melting point of the model molecules. The NRTL–SAC model was used in conjunction with the optimization procedure to test the complete miscibility of solvents during each part of crystallization. After estimating the optimum solvent mixture (combination) for a specific model molecule, the results for single, binary, and ternary solvent mixtures were compared. The results obtained from the binary and ternary combinations were similar in terms of crystallization yields per mass of solvent mixture and far superior to those obtained with single solvents. The proposed algorithm demonstrates flexibility, simplicity, and accuracy in predicting the phase behavior and eventual optimal solvent screening for the crystallization of pharmaceutical components.

The solution-mediated polymorphic transformation (SMPT) of L-glutamic acid is modeled using the method of moments (MoM) with the addition of a dissolution term to account for the transformation of the metastable to the stable polymorph. The numerical solution methodology involves the kinetics of nucleation, growth, and dissolution for the polymorphic system. The effects of the cooling profile, initial solute concentration, and seeding conditions on the product quality were investigated. In supersaturated solutions with respect to both polymorphs, the natural cooling yielded the highest mass of the metastable form, while the nonlinear cooling resulted in the highest mass of the stable form (13.41 g/kg of solvent). The ratio of the stable to metastable form masses was higher with the higher cooling rate parameters. In solutions supersaturated with respect to the stable form and undersaturated relative to the metastable form, the dissolution of the metastable form favored the production of the stable form. The number-weight average size of the stable particles was 148.5 μm with the nonlinear cooling policy which was 51% and 134% more than those corresponding to the linear and natural cooling policies. Finally, nonlinear programming (NLP) was used in a dynamic mode to investigate the optimal control of the process with different objective functions. It was shown that the optimal control policy had a favorable effect on the yield of the stable or metastable form as well as the particle sizes at the end of the batch. The optimal control using an objective function to maximize the mass of the metastable form at the end of the batch resulted in 7.8 g of crystals/kg of solvent for metastable form which was 33% and 381% higher than the natural and linear cooling policies. For an objective function to maximize the mass of the stable form, the optimal cooling policy increased the mass of the stable form by 3.2% compared to the nonlinear cooling policy.

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