Abstract
In this study, the solubility of 145 solid solutes in supercritical CO<sub>2</sub> (scCO<sub>2</sub>) was correlated using computational intelligence techniques based on Quantitative Structure-Property Relationship (QSPR) models. A database of 3637 solubility values has been collected from previously published papers. Dragon software was used to calculate molecular descriptors of 145 solid systems. The genetic algorithm (GA) was implemented to optimise the subset of the significantly contributed descriptors. The overall average absolute relative deviation MAARD of about 1.345 % between experimental and calculated values by support vector regress SVR-QSPR model was obtained to predict the solubility of 145 solid solutes in supercritical CO<sub>2</sub>, which is better than that obtained using ANN-QSPR model of 2.772 %. The results show that the developed SVR-QSPR model is more accurate and can be used as an alternative powerful modelling tool for QSAR studies of the solubility of solid solutes in supercritical carbon dioxide (scCO<sub>2</sub>). The accuracy of the proposed model was evaluated using statistical analysis by comparing the results with other models reported in the literature.
Highlights
Supercritical carbon dioxide is generally used in separation processes applied in the food, chemistry, pharmaceutical, and other industries
The present study was carried out to evaluate the predictive performance of computational intelligence techniques including MPL-feedforward neural network (FNN) as the most used neural network type and support vector machine (SVM) to foretell the solubility of 145 different solid solutes in supercritical CO2 (scCO2) based on a mixture between thermodynamic properties and molecular descriptors
The results show that the solubility data of solid solutes in scCO2 are better correlated by Support vector regress (SVR)-Quantitative Structure-Property Relationship (QSPR) model than with the artificial neural network (ANN)-QSPR
Summary
Supercritical carbon dioxide (scCO2) is generally used in separation processes applied in the food, chemistry, pharmaceutical, and other industries. The least square support vector machine LS-SVM may be considered an alternative for classical approaches, such as semi empirical correlations to model the solid solute solubility in scCO2.7 The most common type of ANN model that is being used nowadays is multilayer perceptron (MLP). Some studies have proven the superiority of SVM over ANN.[10] SVM method has gained a wide range of engineering applications in forecasting and regression analysis due to its attractive features and remarkable generalization performance.[11] the present study was carried out to evaluate the predictive performance of computational intelligence techniques including MPL-FNN as the most used neural network type and SVM to foretell the solubility of 145 different solid solutes in scCO2 based on a mixture between thermodynamic properties and molecular descriptors. A sensitivity analysis was computed by calculating the relative importance of each input parameter on the output
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