Representative mathematical modeling is essential for understanding the batch cooling crystallization processes. Efficient process design and operation are relevant to achieving high-quality criteria and minimizing variation between batches. This work first presents the modeling of batch cooling crystallization based on online dynamic image analysis. A flow-through microscope was used to track the temporal evolution of the crystal population. A population balance modeling (PBM) approach, parameter estimation, and validation were obtained for the batch cooling crystallization of potassium sulfate in water. The performed experiments provided new experimental data, giving dynamic information about the crystal size throughout each run. The kinetic model parameters for crystal nucleation and growth were estimated using a hybrid optimization algorithm, followed by the confidence region construction using a more exploratory particle swarm algorithm. In the parameter estimation framework, in addition to solute concentration, the first fourth-order moments computed throughout all experiments were included in the objective function. A linear size-dependent growth rate was found to capture well the dynamics of the potassium sulfate crystal size distribution. The experimental results evidenced that the crystal shape of potassium sulfate is predominantly constant, allowing the adequacy of the developed model. The validated PBM was also employed as a digital twin of the crystallization process to develop a machine-learning-based control for the process. Then, a surrogate model based on a recurrent neural network, called an echo state network (ESN), was applied in a nonlinear model predictive controller approach (ESN-NMPC). The ESN model could predict the moments of the population balance model up to five steps (5 min) forward. The ESN-NMPC achieved the desired control scenarios for the crystal size and its coefficient of variation. Its performance was comparable to the controller that uses the PBM as the internal model (PB-NMPC).

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Praziquantel (PZQ) is an anthelmintic first-line drug to treat schistosomiasis, a neglected tropical disease strongly related to poverty. PZQ crystalline structures and polymorphs have been studied mainly via mechanochemical approaches. This work applies a systematic study to investigate the polymorphism of PZQ using cooling crystallization experiments under different conditions not yet explored in the literature for this compound. We accessed all forms previously reported so far obtained via mechanochemistry and the hydrate found through supercritical CO2 processing. A novel dimethylacetamide (DMA) solvate was obtained, and a new form was discovered after desolvation of the DMA solvate by aging and exposure to water-vapor atmosphere. Toluene and triethylamine were solvents capable of enabling the formation of different forms depending on the employed experimental condition. A new form was discovered using triethylamine as solvent, which differs from all known polymorphs. The results of this work demonstrate that solvent selection and variation in the rate of supersaturation generation can generate forms obtained from more complicated techniques as well as potentially finding new forms.

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