Correlation and prediction of partition coefficient using nonrandom two-liquid segment activity coefficient model for...

Area of Science:

• Analytical Chemistry
• Separation Science
• Thermodynamics

Background:

• Effective solvent system selection is paramount for successful counter-current chromatography (CCC).
• Predicting partition coefficients (K) is key to optimizing CCC separations.
• Traditional methods for solvent selection can be time-consuming and empirical.

Purpose of the Study:

• To evaluate the Non-Random Two-Liquid Segment Activity Coefficient (NRTL-SAC) model for predicting partition coefficients in CCC.
• To assess the NRTL-SAC model's effectiveness in selecting optimal solvent systems for CCC.
• To demonstrate the practical application of the NRTL-SAC model in a real-world separation scenario.

Main Methods:

• Utilized the NRTL-SAC thermodynamic model, incorporating four conceptual segments to describe molecular interactions.
• Applied the model to correlate and predict partition coefficients (K) for various solutes in different solvent systems.
• Investigated three solvent system families: heptane/methanol/water, heptane/ethyl acetate/methanol/water (Arizona), and hexane/ethyl acetate/methanol/water.
• Validated model predictions against experimental results.

Main Results:

• The NRTL-SAC model demonstrated strong potential in accurately estimating partition coefficients (K).
• Predicted partition coefficients correlated well with experimental data across diverse solvent systems.
• The model's predictions facilitated the selection of suitable solvent systems for CCC.

Conclusions:

• The NRTL-SAC model is a reliable and practical tool for predicting partition coefficients in CCC.
• This thermodynamic approach enhances the efficiency and success rate of CCC solvent system selection.
• The study validates the NRTL-SAC model's utility, including a practical separation of magnolol and honokiol.