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Bioprocess Engineering

, Volume 16, Issue 4, pp 229–235 | Cite as

Multistage affinity cross-flow filtration: process optimization

  • Y. Sun
  • L.-Y. Li
  • X.-Y. Dong

Abstract

The recovery yield (REC) and productivity (PRD) are used as objective functions to optimize the multistage affinity cross-flow filtration (mACFF) process. The effects of the operating conditions such as feed loading volume (Q L +), total protein concentration and target protein purity in the feeding broth are analyzed. For higher affinity system or by a mACFF process with larger number of stages as well as more macroligand loading, there is a critical value of Q L + below which the REC keeps constant and maximal. This maximal value of REC is affected by the stage number as well as macroligand loading of the mACFF process and the affinity system (i.e., the binding constant of the target protein to its macroligand), but independent of the feeding broth properties (i.e., total protein concentration and target protein purity) and membrane permeability. An optimum of Q L + exists to give a maximum of PRD. The optimal Q L + is somewhat larger than the critical Q L + value below which REC keeps constant. The maximum of PRD is raised by increasing the stage number and macroligand loading of the mACFF process, affinity binding constant, and total protein concentration as well as target protein purity in the feeding broth, but reduced by increasing the membrane rejection coefficient (R). However, it is encouraging that the decrease of the maximal PRD is less significant when R is less than 0.5. Therefore, if it is not possible to find a membrane that is completely permeable to proteins and at the same time completely impermeable to the macroligand, a membrane with R less than 0.5 may be selected to obtain a larger PRD. The results obtained in this work give further predictive understanding of the mACFF technique, and will be useful to the process design.

Keywords

Permeability Objective Function Affinity Binding Process Design Target Protein 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Y. Sun
    • 1
  • L.-Y. Li
    • 1
  • X.-Y. Dong
    • 1
  1. 1.Department of Chemical Engineering and Research Center for Biotechnology, Tianjin University, Tianjin 300072, P.R. ChinaCN

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