4.6 Article

Model validation for precipitation in solvent-displacement processes

Journal

CHEMICAL ENGINEERING SCIENCE
Volume 84, Issue -, Pages 671-683

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ces.2012.08.043

Keywords

Precipitation; Population balance; Computational fluid dynamics; Confined impinging jets reactor; DQMOM-IEM; QMOM

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In this work a model for precipitation of polymer nanoparticles in solvent-displacement processes is presented and validated. The model is based on computational fluid dynamics coupled with a population balance model. The standard k-epsilon turbulence model in combination with the enhanced wall treatment approach is used to describe mixing and particle formation in a confined impinging jets reactor. The interaction between turbulent fluctuations and particle formation (i.e., micro-mixing) is modelled with the so-called direct quadrature method of moments coupled with the interaction and exchange with the mean approach, whereas the population balance model is solved by using the quadrature method of moments. The model is used here for the first time to model the precipitation of polymer nanoparticles of poly-e-caprolactone via solvent-displacement with acetone and water as solvent and anti-solvent. Particle formation is described with the classical nucleation, molecular growth and aggregation steps and a discussion on the effect of the polymer molecules behaviour in the system is presented and its effect on the results of the models is shown. The relevant rates are derived from first principles and most of the parameters appearing in the model are identified through independent measurements or from theory. Results show good agreement with experimental data and prove that the approach is very interesting, but further work is needed because, as shown, molecular characteristics of the polymer molecules cannot be neglected and need to be linked with the macroscopic description of the system obtained by computational fluid dynamics. Strategies to assess the value of some missing model parameters via multi-scale modelling are also discussed. (C) 2012 Elsevier Ltd. All rights reserved.

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