Computational fluid dynamic simulation of axial and radial flow membrane chromatography: Mechanisms of non-ideality and validation of the zonal rate model
•Rigorous, mechanistic CFD modeling of membrane chromatography capsules at different scales.•Prediction of experimental data with a minimal number of regressed parameters.•Separate quantification of the impacts of non-ideal flow and non-ideal binding on the resulting chromatograms.•Model-based scale...
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Veröffentlicht in: | Journal of Chromatography A 2013-08, Vol.1305, p.114-122 |
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creator | Ghosh, Pranay Vahedipour, Kaveh Lin, Min Vogel, Jens H. Haynes, Charles von Lieres, Eric |
description | •Rigorous, mechanistic CFD modeling of membrane chromatography capsules at different scales.•Prediction of experimental data with a minimal number of regressed parameters.•Separate quantification of the impacts of non-ideal flow and non-ideal binding on the resulting chromatograms.•Model-based scale-up by transferring binding related parameters across capsule geometries.•Validation of the previously published zonal rate model (ZRM).
Membrane chromatography (MC) is increasingly being used as a purification platform for large biomolecules due to higher operational flow rates. The zonal rate model (ZRM) has previously been applied to accurately characterize the hydrodynamic behavior in commercial MC capsules at different configurations and scales. Explorations of capsule size, geometry and operating conditions using the model and experiment were used to identify possible causes of inhomogeneous flow and their contributions to band broadening. In the present study, the hydrodynamics within membrane chromatography capsules are more rigorously investigated by computational fluid dynamics (CFD). The CFD models are defined according to precisely measured capsule geometries in order to avoid the estimation of geometry related model parameters. In addition to validating the assumptions and hypotheses regarding non-ideal flow mechanisms encoded in the ZRM, we show that CFD simulations can be used to mechanistically understand and predict non-binding breakthrough curves without need for estimation of any parameters. When applied to a small-scale axial flow MC capsules, CFD simulations identify non-ideal flows in the distribution (hold-up) volumes upstream and downstream of the membrane stack as the major source of band broadening. For the large-scale radial flow capsule, the CFD model quantitatively predicts breakthrough data using binding parameters independently determined using the small-scale axial flow capsule, identifying structural irregularities within the membrane pleats as an important source of band broadening. The modeling and parameter determination scheme described here therefore facilitates a holistic mechanistic-based method for model based scale-up, obviating the need of performing expensive large-scale experiments under binding conditions. As the CFD model described provides a rich mechanistic analysis of membrane chromatography systems and the ability to explore operational space, but requires detailed knowledge of internal capsule geometries and ha |
doi_str_mv | 10.1016/j.chroma.2013.07.004 |
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Membrane chromatography (MC) is increasingly being used as a purification platform for large biomolecules due to higher operational flow rates. The zonal rate model (ZRM) has previously been applied to accurately characterize the hydrodynamic behavior in commercial MC capsules at different configurations and scales. Explorations of capsule size, geometry and operating conditions using the model and experiment were used to identify possible causes of inhomogeneous flow and their contributions to band broadening. In the present study, the hydrodynamics within membrane chromatography capsules are more rigorously investigated by computational fluid dynamics (CFD). The CFD models are defined according to precisely measured capsule geometries in order to avoid the estimation of geometry related model parameters. In addition to validating the assumptions and hypotheses regarding non-ideal flow mechanisms encoded in the ZRM, we show that CFD simulations can be used to mechanistically understand and predict non-binding breakthrough curves without need for estimation of any parameters. When applied to a small-scale axial flow MC capsules, CFD simulations identify non-ideal flows in the distribution (hold-up) volumes upstream and downstream of the membrane stack as the major source of band broadening. For the large-scale radial flow capsule, the CFD model quantitatively predicts breakthrough data using binding parameters independently determined using the small-scale axial flow capsule, identifying structural irregularities within the membrane pleats as an important source of band broadening. The modeling and parameter determination scheme described here therefore facilitates a holistic mechanistic-based method for model based scale-up, obviating the need of performing expensive large-scale experiments under binding conditions. As the CFD model described provides a rich mechanistic analysis of membrane chromatography systems and the ability to explore operational space, but requires detailed knowledge of internal capsule geometries and has much greater computational requirements, it is complementary to the previously described strengths and uses of the ZRM for process analysis and design.</description><identifier>ISSN: 0021-9673</identifier><identifier>EISSN: 1873-3778</identifier><identifier>DOI: 10.1016/j.chroma.2013.07.004</identifier><identifier>PMID: 23885666</identifier><identifier>CODEN: JOCRAM</identifier><language>eng</language><publisher>Amsterdam: Elsevier B.V</publisher><subject>Analytical chemistry ; Analytical, structural and metabolic biochemistry ; Axial flow ; Binding ; Biological and medical sciences ; Chemistry ; Chromatographic methods and physical methods associated with chromatography ; Chromatography ; Chromatography, Liquid - methods ; Computational fluid dynamics ; Computer simulation ; Exact sciences and technology ; Flow distribution ; Fundamental and applied biological sciences. Psychology ; General aspects, investigation methods ; Hydrodynamics ; Mathematical models ; Membrane chromatography ; Membranes ; Membranes, Artificial ; Model-based scale-up ; Models, Theoretical ; Other chromatographic methods ; Proteins ; Zonal rate model</subject><ispartof>Journal of Chromatography A, 2013-08, Vol.1305, p.114-122</ispartof><rights>2013 Elsevier B.V.</rights><rights>2014 INIST-CNRS</rights><rights>Copyright © 2013 Elsevier B.V. All rights reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c458t-7caba2a823041164d9189fb459200c27e8b19acbfe5a37a3fae7b1081fff07b63</citedby><cites>FETCH-LOGICAL-c458t-7caba2a823041164d9189fb459200c27e8b19acbfe5a37a3fae7b1081fff07b63</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.chroma.2013.07.004$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3548,27922,27923,45993</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=27649297$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/23885666$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Ghosh, Pranay</creatorcontrib><creatorcontrib>Vahedipour, Kaveh</creatorcontrib><creatorcontrib>Lin, Min</creatorcontrib><creatorcontrib>Vogel, Jens H.</creatorcontrib><creatorcontrib>Haynes, Charles</creatorcontrib><creatorcontrib>von Lieres, Eric</creatorcontrib><title>Computational fluid dynamic simulation of axial and radial flow membrane chromatography: Mechanisms of non-ideality and validation of the zonal rate model</title><title>Journal of Chromatography A</title><addtitle>J Chromatogr A</addtitle><description>•Rigorous, mechanistic CFD modeling of membrane chromatography capsules at different scales.•Prediction of experimental data with a minimal number of regressed parameters.•Separate quantification of the impacts of non-ideal flow and non-ideal binding on the resulting chromatograms.•Model-based scale-up by transferring binding related parameters across capsule geometries.•Validation of the previously published zonal rate model (ZRM).
Membrane chromatography (MC) is increasingly being used as a purification platform for large biomolecules due to higher operational flow rates. The zonal rate model (ZRM) has previously been applied to accurately characterize the hydrodynamic behavior in commercial MC capsules at different configurations and scales. Explorations of capsule size, geometry and operating conditions using the model and experiment were used to identify possible causes of inhomogeneous flow and their contributions to band broadening. In the present study, the hydrodynamics within membrane chromatography capsules are more rigorously investigated by computational fluid dynamics (CFD). The CFD models are defined according to precisely measured capsule geometries in order to avoid the estimation of geometry related model parameters. In addition to validating the assumptions and hypotheses regarding non-ideal flow mechanisms encoded in the ZRM, we show that CFD simulations can be used to mechanistically understand and predict non-binding breakthrough curves without need for estimation of any parameters. When applied to a small-scale axial flow MC capsules, CFD simulations identify non-ideal flows in the distribution (hold-up) volumes upstream and downstream of the membrane stack as the major source of band broadening. For the large-scale radial flow capsule, the CFD model quantitatively predicts breakthrough data using binding parameters independently determined using the small-scale axial flow capsule, identifying structural irregularities within the membrane pleats as an important source of band broadening. The modeling and parameter determination scheme described here therefore facilitates a holistic mechanistic-based method for model based scale-up, obviating the need of performing expensive large-scale experiments under binding conditions. As the CFD model described provides a rich mechanistic analysis of membrane chromatography systems and the ability to explore operational space, but requires detailed knowledge of internal capsule geometries and has much greater computational requirements, it is complementary to the previously described strengths and uses of the ZRM for process analysis and design.</description><subject>Analytical chemistry</subject><subject>Analytical, structural and metabolic biochemistry</subject><subject>Axial flow</subject><subject>Binding</subject><subject>Biological and medical sciences</subject><subject>Chemistry</subject><subject>Chromatographic methods and physical methods associated with chromatography</subject><subject>Chromatography</subject><subject>Chromatography, Liquid - methods</subject><subject>Computational fluid dynamics</subject><subject>Computer simulation</subject><subject>Exact sciences and technology</subject><subject>Flow distribution</subject><subject>Fundamental and applied biological sciences. Psychology</subject><subject>General aspects, investigation methods</subject><subject>Hydrodynamics</subject><subject>Mathematical models</subject><subject>Membrane chromatography</subject><subject>Membranes</subject><subject>Membranes, Artificial</subject><subject>Model-based scale-up</subject><subject>Models, Theoretical</subject><subject>Other chromatographic methods</subject><subject>Proteins</subject><subject>Zonal rate model</subject><issn>0021-9673</issn><issn>1873-3778</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqNkcuO1DAURC0EYpqBP0DIGyQ2CX4kscMCadRiAGkQG1hbN37QbsVxYycDzafwteN-MOwQK1vyqfK9VQg9p6SmhHavt7XepBigZoTymoiakOYBWlEpeMWFkA_RihBGq74T_AI9yXlLCBVEsMfognEp267rVuj3OobdMsPs4wQjduPiDTb7CYLXOPuwjMcnHB2Gn74QMBmcwPgjHH_gYMOQYLL4NM0cvyXYbfZv8CerNzD5HPJBPMWp8sbC6Of90eO2XM2997yx-NdxggSzxSEaOz5FjxyM2T47n5fo6_W7L-sP1c3n9x_XVzeVblo5V0LDAAwk46ShtGtMT2XvhqbtGSGaCSsH2oMenG2BC-AOrBgokdQ5R8TQ8Uv06uS7S_H7YvOsgs_ajmPZKi5ZFU_GJO3Ff6At5U3LG04K2pxQnWLOyTq1Sz5A2itK1KFAtVWnyNShQEWEKgUW2YvzD8sQrLkX_WmsAC_PAGQNoyvZa5__cqJretaLwr09cbZEd-ttUll7O2lrfLJ6Vib6f09yB2i7vhM</recordid><startdate>20130830</startdate><enddate>20130830</enddate><creator>Ghosh, Pranay</creator><creator>Vahedipour, Kaveh</creator><creator>Lin, Min</creator><creator>Vogel, Jens H.</creator><creator>Haynes, Charles</creator><creator>von Lieres, Eric</creator><general>Elsevier B.V</general><general>Elsevier</general><scope>IQODW</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QH</scope><scope>7UA</scope><scope>C1K</scope><scope>F1W</scope><scope>H97</scope><scope>L.G</scope><scope>7TB</scope><scope>7U5</scope><scope>8FD</scope><scope>FR3</scope><scope>H8D</scope><scope>L7M</scope></search><sort><creationdate>20130830</creationdate><title>Computational fluid dynamic simulation of axial and radial flow membrane chromatography: Mechanisms of non-ideality and validation of the zonal rate model</title><author>Ghosh, Pranay ; Vahedipour, Kaveh ; Lin, Min ; Vogel, Jens H. ; Haynes, Charles ; von Lieres, Eric</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c458t-7caba2a823041164d9189fb459200c27e8b19acbfe5a37a3fae7b1081fff07b63</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2013</creationdate><topic>Analytical chemistry</topic><topic>Analytical, structural and metabolic biochemistry</topic><topic>Axial flow</topic><topic>Binding</topic><topic>Biological and medical sciences</topic><topic>Chemistry</topic><topic>Chromatographic methods and physical methods associated with chromatography</topic><topic>Chromatography</topic><topic>Chromatography, Liquid - methods</topic><topic>Computational fluid dynamics</topic><topic>Computer simulation</topic><topic>Exact sciences and technology</topic><topic>Flow distribution</topic><topic>Fundamental and applied biological sciences. Psychology</topic><topic>General aspects, investigation methods</topic><topic>Hydrodynamics</topic><topic>Mathematical models</topic><topic>Membrane chromatography</topic><topic>Membranes</topic><topic>Membranes, Artificial</topic><topic>Model-based scale-up</topic><topic>Models, Theoretical</topic><topic>Other chromatographic methods</topic><topic>Proteins</topic><topic>Zonal rate model</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ghosh, Pranay</creatorcontrib><creatorcontrib>Vahedipour, Kaveh</creatorcontrib><creatorcontrib>Lin, Min</creatorcontrib><creatorcontrib>Vogel, Jens H.</creatorcontrib><creatorcontrib>Haynes, Charles</creatorcontrib><creatorcontrib>von Lieres, Eric</creatorcontrib><collection>Pascal-Francis</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Aqualine</collection><collection>Water Resources Abstracts</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 3: Aquatic Pollution & Environmental Quality</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>Journal of Chromatography A</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ghosh, Pranay</au><au>Vahedipour, Kaveh</au><au>Lin, Min</au><au>Vogel, Jens H.</au><au>Haynes, Charles</au><au>von Lieres, Eric</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Computational fluid dynamic simulation of axial and radial flow membrane chromatography: Mechanisms of non-ideality and validation of the zonal rate model</atitle><jtitle>Journal of Chromatography A</jtitle><addtitle>J Chromatogr A</addtitle><date>2013-08-30</date><risdate>2013</risdate><volume>1305</volume><spage>114</spage><epage>122</epage><pages>114-122</pages><issn>0021-9673</issn><eissn>1873-3778</eissn><coden>JOCRAM</coden><abstract>•Rigorous, mechanistic CFD modeling of membrane chromatography capsules at different scales.•Prediction of experimental data with a minimal number of regressed parameters.•Separate quantification of the impacts of non-ideal flow and non-ideal binding on the resulting chromatograms.•Model-based scale-up by transferring binding related parameters across capsule geometries.•Validation of the previously published zonal rate model (ZRM).
Membrane chromatography (MC) is increasingly being used as a purification platform for large biomolecules due to higher operational flow rates. The zonal rate model (ZRM) has previously been applied to accurately characterize the hydrodynamic behavior in commercial MC capsules at different configurations and scales. Explorations of capsule size, geometry and operating conditions using the model and experiment were used to identify possible causes of inhomogeneous flow and their contributions to band broadening. In the present study, the hydrodynamics within membrane chromatography capsules are more rigorously investigated by computational fluid dynamics (CFD). The CFD models are defined according to precisely measured capsule geometries in order to avoid the estimation of geometry related model parameters. In addition to validating the assumptions and hypotheses regarding non-ideal flow mechanisms encoded in the ZRM, we show that CFD simulations can be used to mechanistically understand and predict non-binding breakthrough curves without need for estimation of any parameters. When applied to a small-scale axial flow MC capsules, CFD simulations identify non-ideal flows in the distribution (hold-up) volumes upstream and downstream of the membrane stack as the major source of band broadening. For the large-scale radial flow capsule, the CFD model quantitatively predicts breakthrough data using binding parameters independently determined using the small-scale axial flow capsule, identifying structural irregularities within the membrane pleats as an important source of band broadening. The modeling and parameter determination scheme described here therefore facilitates a holistic mechanistic-based method for model based scale-up, obviating the need of performing expensive large-scale experiments under binding conditions. As the CFD model described provides a rich mechanistic analysis of membrane chromatography systems and the ability to explore operational space, but requires detailed knowledge of internal capsule geometries and has much greater computational requirements, it is complementary to the previously described strengths and uses of the ZRM for process analysis and design.</abstract><cop>Amsterdam</cop><pub>Elsevier B.V</pub><pmid>23885666</pmid><doi>10.1016/j.chroma.2013.07.004</doi><tpages>9</tpages></addata></record> |
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subjects | Analytical chemistry Analytical, structural and metabolic biochemistry Axial flow Binding Biological and medical sciences Chemistry Chromatographic methods and physical methods associated with chromatography Chromatography Chromatography, Liquid - methods Computational fluid dynamics Computer simulation Exact sciences and technology Flow distribution Fundamental and applied biological sciences. Psychology General aspects, investigation methods Hydrodynamics Mathematical models Membrane chromatography Membranes Membranes, Artificial Model-based scale-up Models, Theoretical Other chromatographic methods Proteins Zonal rate model |
title | Computational fluid dynamic simulation of axial and radial flow membrane chromatography: Mechanisms of non-ideality and validation of the zonal rate model |
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