Data Mining Crystallization Kinetics
2020
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.
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