Robust Bayesian estimation of kinetics for the polymorphic transformation of L-glutamic acid crystals

Authors

  • Martin Wijaya Hermanto,

    1. Dept. of Chemical and Biomolecular Engineering, National University of Singapore, Singapore, Singapore 117576
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  • Nicholas C. Kee,

    1. Dept. of Chemical and Biomolecular Engineering, National University of Singapore, Singapore, Singapore 117576
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    • N. C. Kee is also affiliated with Dept. of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801.

  • Reginald B. H. Tan,

    1. Dept. of Chemical and Biomolecular Engineering, National University of Singapore, Singapore, Singapore 117576
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    • R. B. H. Tan is also affiliated with Institute of Chemical and Engineering Sciences Singapore, Singapore 627833.

  • Min-Sen Chiu,

    Corresponding author
    1. Dept. of Chemical and Biomolecular Engineering, National University of Singapore, Singapore, Singapore 117576
    • Dept. of Chemical and Biomolecular Engineering, National University of Singapore, Singapore, Singapore 117576
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  • Richard D. Braatz

    1. Dept. of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801
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Abstract

Polymorphism, in which there exist different crystal forms for the same chemical compound, is an important phenomenon in pharmaceutical manufacturing. In this article, a kinetic model for the crystallization of L-glutamic acid polymorphs is developed from experimental data. This model appears to be the first to include all of the transformation kinetic parameters including dependence on the temperature. The kinetic parameters are estimated by Bayesian inference from batch data collected from two in situ measurements: ATR-FTIR spectroscopy is used to infer the solute concentration, and FBRM that provides crystal size information. Probability distributions of the estimated parameters in addition to their point estimates are obtained by Markov Chain Monte Carlo simulation. The kinetic model can be used to better understand the effects of operating conditions on crystal quality, and the probability distributions can be used to assess the accuracy of model predictions and incorporated into robust control strategies for polymorphic crystallization. © 2008 American Institute of Chemical Engineers AIChE J, 2008

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