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This study investigates the solubility of 145 solid solutes in supercritical CO2 using Quantitative Structure-Property Relationship (QSPR) models, employing computational intelligence techniques such as Support Vector Regression (SVR) and Artificial Neural Networks (ANN). The SVR-QSPR model demonstrated a superior predictive accuracy with a mean absolute relative deviation of 1.345% compared to 2.772% for the ANN-QSPR model. The findings indicate that the SVR-QSPR model is a powerful tool for predicting sol
- Author
- LAIDI Maamar
- Language
- EN