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Seeding strategies and residence time characteristics of continuous preferential crystallization

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons86438

Qamar,  S.
Physical and Chemical Foundations of Process Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society;
COMSATS Institute of Information Technology, Dep. of Mathematics, Islamabad, Pakistan;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons86282

Elsner,  M. P.
Physical and Chemical Foundations of Process Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society;
Georg Simon Ohm University of Applied Sciences, Nuremberg, Germany;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons86477

Seidel-Morgenstern,  A.
Physical and Chemical Foundations of Process Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society;
Otto-von-Guericke-Universität Magdeburg, External Organizations;

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Citation

Qamar, S., Elsner, M. P., Hussain, I., & Seidel-Morgenstern, A. (2012). Seeding strategies and residence time characteristics of continuous preferential crystallization. Chemical Engineering Science, 71, 5-17. doi:10.1016/j.ces.2011.12.030.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-89BB-E
Abstract
This contribution investigates the effects of different seeding strategies and residence time characteristics on the dynamics of a Mixed Suspension Mixed Product Removal (MSMPR) crystallizer equipped with a fines dissolution unit. For the first time continuous preferential enantioselective crystallization is investigated. The fines dissolution is included as recycle streams around the MSMPR crystallizer. Moreover, primary heterogeneous and secondary nucleation mechanisms along with size-dependent growth rates are taken into account. A semi-discrete high resolution finite volume scheme (FVS) is employed for discretizing the derivatives with respect to the length coordinate. The resulting ordinary differential equations (ODEs) are solved by a Runge–Kutta method of order four. Several numerical case studies are carried out. The results support process design and optimization. Copyright © 2011 Elsevier Ltd. All rights reserved. [accessed January 19th 2012]