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  Seeding strategies and residence time characteristics of continuous preferential crystallization

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.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-89BB-E Version Permalink: http://hdl.handle.net/11858/00-001M-0000-0014-B390-A
Genre: Journal Article

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 Creators:
Qamar, S.1, 2, Author              
Elsner, M. P.1, 3, Author              
Hussain, I.2, Author
Seidel-Morgenstern, A.1, 4, Author              
Affiliations:
1Physical and Chemical Foundations of Process Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society, ou_1738150              
2COMSATS Institute of Information Technology, Dep. of Mathematics, Islamabad, Pakistan, persistent:22              
3Georg Simon Ohm University of Applied Sciences, Nuremberg, Germany, ou_persistent22              
4Otto-von-Guericke-Universität Magdeburg, External Organizations, ou_1738156              

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Free keywords: Population balances; Continuous preferential crystallization; Fines dissolution; Seeding strategies; High resolution schemes; Goal functions
 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]

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Language(s): eng - English
 Dates: 2012
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: Peer
 Identifiers: eDoc: 576474
DOI: 10.1016/j.ces.2011.12.030
Other: 31/12
 Degree: -

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Title: Chemical Engineering Science
  Other : Chem. Eng. Sci.
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: Amsterdam : Pergamon
Pages: - Volume / Issue: 71 Sequence Number: - Start / End Page: 5 - 17 Identifier: ISSN: 0009-2509
CoNE: https://pure.mpg.de/cone/journals/resource/954925389239