Data mining crystallization kinetics

Research output: Contribution to conferenceSpeech


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.
Original languageEnglish
Publication statusUnpublished - 19 Aug 2020
EventCurrent Trends in Chemical Process Technology and Materials Development - Indo-UK Joint International Webinar
Duration: 19 Aug 202020 Aug 2020


ConferenceCurrent Trends in Chemical Process Technology and Materials Development


  • crystallization kinetics
  • population balance model
  • data mining
  • machine learning


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