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Data Mining Crystallization Kinetics

revised on 11.08.2020, 10:24 and posted on 11.08.2020, 10:40 by Cameron Brown, Diego Maldonado, Antony Vassileiou, Blair Johnston, Alastair Florence

Population balance model is a valuable modelling tool which facilitates the optimization and understanding of crystallization processes. However, in order to use this tool, it is necessary to have previous knowledge of the crystallization kinetics, specifically crystal growth and nucleation. The majority of approaches to achieve proper estimations of kinetic parameters required experimental data. Across time, a vast literature about the estimation of kinetic parameters and population balances have been published. Considering the availability of data, this work built a database with information on solute, solvent, kinetic expression, parameters, crystallization method and seeding. Correlations were assessed and clusters structures identified by hierarchical clustering analysis. The final database contains 336 data of kinetic parameters from 185 different sources. The data were analysed using kinetic parameters of the most common expressions. Subsequently, clusters were identified for each kinetic model. With these clusters, classification random forest models were made using solute descriptors, seeding, solvent, and crystallization methods as classifiers. Random forest models had an overall classification accuracy higher than 70% whereby they were useful to provide rough estimates of kinetic parameters, although these methods have some limitations.




Email Address of Submitting Author


University of Strathclyde


United Kingdom

ORCID For Submitting Author


Declaration of Conflict of Interest

No conflict of interest

Version Notes

Updates to datasets: id 326,327, 328, 329, 330 - order of magnitude of kg corrected id 220 - order of magnitude of kg corrected id 268 - reference updated to that of original experimental work. Value of kg updated assuming published units of micron/min.