Particle Technology and Fluidization

On the use of process analytical technologies and population balance equations for the estimation of crystallization kinetics. A case study

  1. Nesrine Gherras1,
  2. Gilles Fevotte1,2,*

Article first published online: 24 OCT 2011

DOI: 10.1002/aic.12776

Issue

AIChE Journal

AIChE Journal

Volume 58, Issue 9, pages 2650–2664, September 2012

Additional Information

How to Cite

Gherras, N. and Fevotte, G. (2012), On the use of process analytical technologies and population balance equations for the estimation of crystallization kinetics. A case study. AIChE J., 58: 2650–2664. doi: 10.1002/aic.12776

Author Information

  1. 1

    Ecole des Mines de Saint Etienne, Centre SPIN, LPMG, UMR CNRS 5148, 158, cours Fauriel, 42000 Saint Etienne, France

  2. 2

    Université de Lyon, Université Lyon 1, 43 bld du 11 Novembre 1918, 69100 Villeurbanne, France

*Ecole des Mines de Saint Etienne, Centre SPIN, LPMG, UMR CNRS 5148, 158, cours Fauriel, 42000 Saint Etienne, France

Publication History

  1. Issue published online: 8 AUG 2012
  2. Article first published online: 24 OCT 2011
  3. Accepted manuscript online: 12 SEP 2011 11:21AM EST
  4. Manuscript Revised: 9 SEP 2011
  5. Manuscript Received: 9 FEB 2011

Funded by

  • French Research Agency ANR (Agence Nationale de la Recherche) for support granted to “white project” IPAPI (Improving the Properties of Active Pharmaceutical Ingredients). Grant Number: ref.07-BLAN-0183

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Keywords:

  • crystal growth (industrial crystallization);
  • design (process simulation);
  • nucleation;
  • process control;
  • particle/count/measurements

Abstract

The batch cooling solution crystallization of ammonium oxalate was performed in water at various constant cooling rates. Measurements of the solute concentration were obtained using in situ attenuated total reflectance fourier transform infrared (ATR-FTIR) spectroscopy, and final estimates of the crystal size distribution (CSD) were computed; thanks to in situ image acquisition and off-line image analysis. The crystallization process was then simulated using population balance equations (PBEs). Estimates of the nucleation and the growth parameters were computed through model/experiments fitting. According to the cooling rate, the PBE model allowed distinguishing between two distinct crystallization regimes, separated by an “intermediate regime.” The respective contributions and shortcomings of solute concentration measurements and granulometric data to the identification of nucleation and growth kinetic parameters are analyzed and discussed. It is shown in particular that no real separate estimation of nucleation and growth parameters can be obtained in the absence of CSD data. © 2011 American Institute of Chemical Engineers AIChE J, 2012

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