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Predicting performance of constant flow depth filtration using constant pressure filtration data

Goldrick, S; Joseph, A; Mollet, M; Turner, R; Gruber, D; Farid, SS; Titchener-Hooker, NJ; (2017) Predicting performance of constant flow depth filtration using constant pressure filtration data. Journal of Membrane Science , 531 pp. 138-147. 10.1016/j.memsci.2017.03.002. Green open access

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Abstract

This paper describes a method of predicting constant flow filtration capacities using constant pressure datasets collected during the purification of several monoclonal antibodies through depth filtration. The method required characterisation of the fouling mechanism occurring in constant pressure filtration processes by evaluating the best fit of each of the classic and combined theoretical fouling models. The optimised coefficients of the various models were correlated with the corresponding capacities achieved during constant flow operation at the specific pressures performed during constant pressure operation for each centrate. Of the classic and combined fouling models investigated, the Cake-Adsorption fouling model was found to best describe the fouling mechanisms observed for each centrate at the various different pressures investigated. A linear regression model was generated with these coefficients and was shown to predict accurately the capacities at constant flow operation at each pressure. This model was subsequently validated using an additional centrate and accurately predicted the constant flow capacities at three different pressures (0.69, 1.03 and 1.38 bar). The model used the optimised Cake-Adsorption model coefficients that best described the flux decline during constant pressure operation. The proposed method of predicting depth filtration performance proved to be faster than the traditional approach whilst requiring significantly less material, making it particularly attractive for early process development activities.

Type: Article
Title: Predicting performance of constant flow depth filtration using constant pressure filtration data
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.memsci.2017.03.002
Publisher version: http://dx.doi.org/10.1016/j.memsci.2017.03.002
Language: English
Additional information: © 2017 Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/BY/4.0/).
Keywords: Engineering, Chemical, Polymer Science, Engineering, Constant flow, Constant pressure, Depth filtration, Filter sizing, Mammalian cell, Fouling models, BLOCKING LAWS, FLUX DECLINE, MEMBRANE, MICROFILTRATION, ULTRAFILTRATION, CENTRIFUGATION, MECHANISMS, MODELS
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Biochemical Engineering
URI: https://discovery.ucl.ac.uk/id/eprint/1546970
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