Solubility prediction in supercritical CO(2) using minimum number of experiments

Area of Science:

• Chemical Engineering
• Thermodynamics
• Physical Chemistry

Background:

• Supercritical carbon dioxide (scCO2) is a tunable solvent with growing industrial applications.
• Accurate solubility prediction is vital for designing efficient scCO2-based processes.
• Existing empirical equations require evaluation for their predictive accuracy.

Purpose of the Study:

• To assess the correlation ability of empirical equations for solubility in scCO2.
• To develop and validate a modified empirical relationship for predicting solubility.
• To investigate the solubility of nicotinic acid and p-acetoxyacetanilide in scCO2.

Main Methods:

• Experimental determination of solubilities using a dynamic flow system.
• Evaluation of existing empirical equations against literature and experimental data.
• Development and validation of a modified empirical model using a training set.

Main Results:

• Existing empirical equations showed average absolute relative deviations (AARD) of 12.6-24.8%.
• The modified empirical relationship achieved an AARD of 17% for predicted solubilities.
• Experimental data were generated for nicotinic acid and p-acetoxyacetanilide at 35-75°C and 100-200 bar.

Conclusions:

• The modified empirical relationship demonstrates improved prediction capability for scCO2 solubility.
• Accurate solubility data and predictive models are essential for optimizing scCO2 applications.
• This work provides a validated tool for solubility prediction in supercritical fluid technology.