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Nonlinear liquid-liquid chromatography: A comprehensive modeling approach.

Melanie Gerigk1, Daili Peng1, Daniel Espinoza2

  • 1Biothermodynamics, Department of Life Science Engineering, TUM School of Life Sciences, Technical University of Munich, 85354 Freising, Germany.

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Summary
This summary is machine-generated.

This study introduces a novel model combining liquid-liquid chromatography (LLC) and liquid-liquid equilibria (LLE) to simulate solute and solvent behavior in LLC columns. The model accurately predicts cannabidiol (CBD) elution profiles, advancing LLC process understanding.

Keywords:
Centrifugal partition chromatographyCountercurrent chromatographyNRTL modelNonlinear distribution equilibria, Cannabidiol (CBD)

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Area of Science:

  • Chemical Engineering
  • Separation Science
  • Thermodynamics

Background:

  • Liquid-liquid chromatography (LLC) separates mixtures based on differential phase distribution.
  • Accurate modeling of LLC is crucial for process optimization and understanding.
  • Existing models often simplify the complex interactions within the LLC system.

Purpose of the Study:

  • To develop and validate a combined chromatography and liquid-liquid equilibria (LLE) thermodynamic model for LLC.
  • To simulate solute and solvent propagation within an LLC column.
  • To investigate the behavior of cannabidiol (CBD) as a solute in a specific solvent system.

Main Methods:

  • Integration of a chromatography model with the NonRandom Two-Liquid (NRTL) thermodynamic model for LLE.
  • Fitting NRTL parameters to experimental LLE data for a quaternary system (n-hexane/methanol/water/CBD).
  • Inclusion of an overflow term in mass balance equations to account for stationary phase volume limitations.

Main Results:

  • The NRTL model accurately predicted LLE data with a 1.0% RMSE.
  • Simulations showed good agreement with experimental elution profiles for CBD, with elution time deviations under 3 minutes.
  • The model successfully described LLC operation where solute distribution is concentration-dependent.

Conclusions:

  • The developed model provides a more rigorous approach to simulating LLC processes.
  • This integrated modeling framework enhances the understanding of solute and solvent dynamics in LLC.
  • The study represents a significant step towards advanced, predictive modeling of liquid-liquid chromatography.