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Improvement of an overloaded, multi-component, solvent gradient bioseparation through multiobjective optimization.

Abhijit Tarafder1, Lars Aumann, Thomas Müller-Späth

  • 1Institute for Chemical and Bioengineering, Department of Chemistry and Applied Biosciences, ETH Zurich, 8093 Zurich, Switzerland.

Journal of Chromatography. A
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Summary
This summary is machine-generated.

Optimizing solvent gradients in preparative chromatography enhances yield and productivity. This study uses advanced modeling and multilinear gradients to improve purification of complex mixtures like Calcitonin.

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Area of Science:

  • Chromatography
  • Chemical Engineering
  • Bioseparation

Background:

  • Solvent gradient chromatography is vital for analytical separations of complex mixtures.
  • Preparative chromatography increasingly uses solvent gradients for yield and productivity, but optimization is limited.
  • Current methods rely on simple gradients and empirical optimization.

Purpose of the Study:

  • To develop and validate an advanced modeling procedure for optimizing solvent gradients in preparative chromatography.
  • To improve the purification of an industrial multi-component mixture containing Calcitonin.
  • To gain a clearer understanding of modifier gradient effects on separation performance in non-linear chromatography.

Main Methods:

  • Utilized theoretical simulations and experimental validation.
  • Employed multilinear gradients as optimization variables.
  • Focused on an overloaded reversed-phase column for Calcitonin purification.

Main Results:

  • Developed an optimization procedure for preparative chromatography using advanced modeling.
  • Achieved improved purification of a multi-component mixture with Calcitonin as the main product.
  • Identified strategies like peak deformation to enhance yield and productivity.

Conclusions:

  • Advanced modeling and multilinear gradients offer significant improvements in preparative chromatography.
  • Modifier gradient design can be optimized for increased yield and productivity.
  • This work enhances understanding of modifier gradients in non-linear separation processes.