(548a) Estimation and Modeling of Crystal Size and Shape Evolution Using In Situ Tools

A large proportion of pharmaceutical crystallizations produce crystals with high aspect ratio, which can cause problems in operations downstream from the crystallizer, such as washing and filtration (e.g., see Refs. 1-24). This presentation describes the application of the Focused Beam Reflectance Measurement (FBRM) for the in-process estimation of the crystal characteristics and ReactIR Attenuated Total Reflection Fourier Transform Infrared Spectroscopy for solution concentration estimation during the nucleation, growth, and dissolution of crystals of varying shapes (e.g., see Refs. 3, 8, 12-13, 15, 18 and citations therein). The particle characterization includes the estimation of the mean crystal length and mean crystal width for rod-like crystals, which are commonplace in the pharmaceutical industry. Chord length distributions and their derived statistics are compared for the S- and G-Series FBRM probes for various types of systems, with additional particle characterization using Process Vision Measurement (PVM) and off-line optical microscopy. Experiments that cycle the temperature from conditions of positive and negative supersaturation are used to assess reproducibility of the estimated particle and solution properties while providing a wide range of changes in the aspect ratio in a single well-characterized experiment for assessing the different relative dependen­cies of growth and dissolution on supersaturation along the different crystal axes. The crystal size and solute concentration estimates are used to estimate kinetics in a multidimensional population balance model for prediction of the crystal size and shape distribution.  In contrast to previous studies that have estimated kinetics along multiple crystal axes in mixed-tank crystallizers (e.g., Refs. 14 and 24), this study employs standardized commercial instrumentation that is readily available in pharmaceutical laboratories (FBRM) rather than using specialized instrumentation or excessive sampling. In contrast to Ref. 24, the size characterization in this work is of much higher quality. In contrast to past studies, this work characterizes the different dissolution kinetics along each crystal axis, and is able to collect enough experimental design for kinetics estimation in a single experiment (instead of multiple experiments). The subsequent model predictions are validated with experimental data.

References:

[1]     Yang, G.; Kubota, N.; Sha, Z.; Louhi-Kultanen, M.; Wang, J.; Crystal shape control by manipulating supersaturation in batch cooling crystallization, Crystal Growth & Design 2006, 6, 2799–2803.

[2]     Zhang, Y.; Sizemore, J.; Doherty, M.F.; Shape evolution of 3-dimensional faceted crystals, AIChE J. 2006, 52, 1906–1915.

[3]     Gunawan, R.; Ma, D.L.; Fujiwara, M.; Braatz, R.D.; Identification of kinetic parameters in a multidimensional crystallization process. Special Issue on Crystallization and Interfacial Processes, Int. J. of Modern Physics B 2002, 16, 367-374.

[4]     Lee, K.; Lee, J.H.; Fujiwara, M.; Ma, D.L.; Braatz R.D.; Run-to-run control of multidimensional crystal size distribution in a batch crystallizer, in Proceedings of the American Control Conference, Anchorage, AK, pages 1013–1018, 2002.

[5]     Wan, J.; Wang, X.Z.; Ma C.Y.; Particle shape manipulation and optimization in cooling crystallization involving multiple crystal morphological forms, AIChE J. 2009, 55, 2049–2061.

[6]     Briesen, H.; Simulation of crystal size and shape by means of a reduced two-dimensional population balance model, Chem. Eng. Sci. 2006, 61, 104–112, 2006.   

[7]     Ma, C.Y.; Wang, X.Z.; Roberts, K.J.; Morphological population balance for modeling crystal growth in face directions, AIChE J. 2008, 54, 209–222.

[8]     Patience, D.B.; Rawlings, J.B.; Particle-shape monitoring and control in crystallization processes, AIChE J. 2001, 47, 2125–2130.

[9]     Puel, F.; Fevotte, G.; Klein, J.P.; Simulation and analysis of industrial crystallization processes through multidimensional population balance equations. Part 1: A resolution algorithm based on the method of classes, Chem. Eng. Sci. 2003, 58, 3715–3727.  

[10]   Calderon De Anda, J.; Wang, X.Z.; Lai, X.; Roberts, K.J.; Classifying organic crystals via in-process image analysis and the use of monitoring charts to follow polymorphic and morphological changes, J. Process Control 2005, 15, 785–797.

[11]   Larsen, P.A.; Rawlings, J.B.; Ferrier, N.J.; An algorithm for analyzing noisy, in situ images of high-aspect-ratio crystals to monitor particle size distribution, Chem. Eng. Sci. 2006, 61, 5236–5248

[12]   Ma, Z.H.; Merkus, H.G.; Scarlett, B.; Extending laser diffraction for particle shape characterization: Technical aspects and application, Powder Technology 2001, 118, 180–187.

[13]   Pollanen, K.; Hakkinen, A.W.; Reinikainen, S.P.; Louhi-Kultanen, A.; Nystrom, L.; A study on batch cooling crystallization of sulphathiazole-Process monitoring using ATR-FTIR and product characterization by automated image analysis, Chem. Eng. Res. Des. 2006, 84, 47–59.

[14]   Oullion, M.; Puel, F.; Févotte, G.; Righini, S.; Carvin, P.; Industrial batch crystallization of a plate-like organic product. In situ monitoring and 2D-CSD modelling, Part 1: Experimental study, Chem. Eng. Sci. 2007, 62, 820–832.

[15]   Eggers, J.; Kempkes, M.; Mazzotti, M.; Measurement of size and shape distributions of particles through image analysis, Chem. Eng. Sci. 2008, 63, 5513–5521. 

[16]   Togkalidou, T.; Tung, H.-H.; Sun, Y.; Andrews, A.; Braatz, R.D.; Parameter estimation and optimization of a loosely-bound aggregating pharmaceutical crystallization using in-situ infrared and laser backscattering measurements,    Ind. Eng. Chem. Res. 2004, 43, 6168–6181.

[17]   Grön, H.; Mougin, P.; Thomas, A.; White, G.; Wilkinson, D.; Hammond, R.B.; Lai, X.J.; Roberts, K.J.; Dynamic in-process examination of particle size and crystallographic form under defined conditions of reactant supersaturation as associated with the batch crystallization of monosodium glutamate from aqueous solution. Ind. Eng. Chem. Res. 2003, 42, 4888–4898.

[18]   Togkalidou, T.; Tung, H.-H.; Sun, Y.; Andrews, A.; Braatz, R.D.; Solution concentration prediction for pharmaceutical crystallization processes using robust chemometrics and ATR FTIR spectroscopy, Org. Process Res. Dev. 2002, 6, 317-322.

[19]   Hulburt, H.M.; Katz, S.; Some problems in particle technology, Chem. Eng. Sci. 1964, 19, 555–574.

[20]   Qamar, S.; Noor, S.; ul Ain, Q.; Seidel-Morgenstern, A.; Bivariate extension of the quadrature method of moments for batch crystallization models, Ind. Eng. Chem. Res. 2010, 49, 11633–11644.

[21]   Gunawan, R.; Fusman, I.; Braatz, R.D.; High resolution finite volume methods for simulating multidimensional population balance equations with nucleation and size-dependent growth, AIChE J., 2004, 50, 2738-2749.

[22]   Ma, D.L.; Tafti, D.K.; Braatz, R.D.; High resolution simulation of multidimensional crystal growth, Ind. Eng. Chem. Res. 2002, 41, 6217-6223.

[23]   Ma, D.L.; Tafti, D.K.; Braatz, R.D.; Compartmental modeling of multidimensional crystallization, Int. J. of Modern Physics B 2002, 16:383-390.

[24]   Gunawan, R.; Ma, D.L.; Fujiwara, M.; Braatz, R.D.; Identification of kinetic parameters in a multidimensional crystallization process, Int. J. of Modern Physics B 2002, 16, 367-374.