(56a) Crystallization Kinetic Measurement and Parameter Estimation Utilizing Population Balance Model in a Dynamic/Oscillatory Baffle Crystallizer

Liu, C. Y., Purdue University
Nagy, Z. K., Purdue University
Eren, A., Purdue University
Crystallization has become a predominant technique in particle design technology and it is widely applied in the pharmaceutical industry. Oscillatory systems have been shown to enhance mass and heat transfer capabilities while imposing less shear on crystals.1-2 A novel commercial dynamic baffle crystallizer(DBC), also known as an oscillatory baffle reactor(OBR), has been studied. Crystallization kinetics of paracetamol(PCM) in ethanol are measured in the DBC to develop the population balance model(PBM) of batch and continuous crystallization in the DBC. PBM is a first-principle model often used to describe cell dynamics, polymerization processes and particulate systems such as crystallization.3Coupling with mass balance, PBM model describes the size distribution as well as concentration profile over time during crystallization processes which aid the understanding and optimization of crystallization. With the help of in-situ process analytical technology(PAT) tools such as Infrared(IR), and focused beam reflectance measurement(FBRM), nucleation and growth kinetic parameters are estimated separately by solving the PBM multiple times using high resolution finite volume(HRFV) method to fit experimental data.4-5 Experiments are repeated for different oscillation conditions to account for the effect of mixing intensity on crystallization kinetics into the PBM by incorporating oscillation-dependent terms into the kinetic expressions. The PBM model is then validated with batch and continuous experiments and showed agreement. The kinetic parameters are then compared to those of a traditional stirred tank crystallizer reported in the literature to further understand the impact of the oscillatory mixing dynamics on crystallization kinetics.

  1. Ni, X.; Mackley, M. R.; Harvey, A. P.; Stonestreet, P.; Baird, M. H. I.; Rama Rao, N. V., Mixing Through Oscillations and Pulsations - a guide to achieving process enhancements in the chemical and process industries. Trans IChemE 2003, 81 (A), 373-383.
  2. Hewgill, M. R.; Mackley, M. R.; Pandit, A. B.; Pannu, S. S., Enhancement of gas-liquid mass transfer using oscillatory flow in a baffled tube. Chemical Engineering Science 1993, 48(4), 799-809.
  3. Ramkrishna, D., Population Balances: Theory and Applications to Particulate Systems in Engineering. Academic Press: San Diego, CA, 2000.
  4. Simone, E.; Szilagyi, B.; Nagy, Z. K., Systematic model identification and optimization-based active polymorphic control of crystallization processes. Chemical Engineering Science 2017, 174, 374-386.
  5. Gunawan, R.; Fusman, I.; Braatz, R. D., High resolution algorithms for multidimensional population balance equations. AIChE Journal 2004, 50 (11), 2738-2749.


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