LAPSE:2021.0715
Published Article
LAPSE:2021.0715
Optimizing the Axial Resistance Profile of Submerged Hollow Fiber Membranes
Michael Kuhn, Heiko Briesen
September 21, 2021
Submerged hollow fiber membranes (SHFMs) are used for a wide variety of applications. Their applicability and their advantages, however, depend crucially on the prevailing hydrodynamics within single fibers. In this respect, the non-uniform distribution of transmembrane flux is a known problem related to inhomogeneous membrane fouling and disadvantages for cleaning. To address this problem, we propose an approach to homogenize transmembrane flux by varying the local membrane resistance using optimal control methods for the first time in SHFM research. Based on an established model, different scenarios are optimized, namely with different fiber lengths and inner radii. In addition, a double-end setup is explored. It is shown that the optimization goal is reached very well in all tested cases, which underlines the general validity of our strategy. Further uses and extensions of the optimization method are provided, as well as hints for the practical implementation of the suggested measures.
Keywords
filter design, mechanistic model, membrane filtration, optimal control, process optimization
Subject
Suggested Citation
Kuhn M, Briesen H. Optimizing the Axial Resistance Profile of Submerged Hollow Fiber Membranes. (2021). LAPSE:2021.0715
Author Affiliations
Kuhn M: TUM School of Life Sciences, Technical University of Munich, 85354 Freising, Germany [ORCID]
Briesen H: TUM School of Life Sciences, Technical University of Munich, 85354 Freising, Germany
Journal Name
Processes
Volume
9
Issue
1
First Page
pr9010020
Year
2020
Publication Date
2020-12-23
Published Version
ISSN
2227-9717
Version Comments
Original Submission
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PII: pr9010020, Publication Type: Journal Article
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LAPSE:2021.0715
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doi:10.3390/pr9010020
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Sep 21, 2021
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CC BY 4.0
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[v1] (Original Submission)
Sep 21, 2021
 
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Sep 21, 2021
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https://psecommunity.org/LAPSE:2021.0715
 
Original Submitter
Calvin Tsay
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