A new correlation of solubilities of azoic compounds and anthraquinone derivatives in supercritical carbon dioxide

Solubility of vitamin A in supercritical CO2: experimental study and thermodynamic modeling

One of the best methods of extracting Vitamin A, as a helper of the immune body system and vision, was measured in supercritical carbon dioxide (SC-CO₂); Mole fractions were gained at practical conditions in which the temperature was in the range of 303–323 K and the pressure range was 90–235 bar, respectively. Moreover, four Equation of States [Soave–Redlich–Kwong, Peng–Robinson, Stryjek–Vera and Dashtizadeh–Pazuki–Taghikhani–Ghotbi (DPTG)] were compared with the experimental data. Also, the mixing rules of Van der Waals (vdW1 and vdW2) selected to correlate the solubility data of vitamin A. The outcomes indicate that each of EOSs coupled with vdW2, as a method of estimating the physicochemical and critical properties, were correlated with the solubility data of vitamin A in SC-CO₂ with more accuracy, in comparison with vdW1. Among the cubic EOSs, the DPTG model with vdW2 generated the most suitable correlation with the percentage average absolute relative deviation (Average Absolute Relative Deviation%) of 6.

Wavelet neural network modeling in QSPR for prediction of solubility of 25 anthraquinone dyes at different temperatures and pressures in supercritical carbon dioxide

A wavelet neural network (WNN) model in quantitative structure property relationship (QSPR) was developed for predicting solubility of 25 anthraquinone dyes in supercritical carbon dioxide over a wide range of pressures (70-770 bar) and temperatures (291-423 K). A large number of descriptors were calculated with Dragon software and a subset of calculated descriptors was selected from 18 classes of Dragon descriptors with a stepwise multiple linear regression (MLR) as a feature selection technique. Six calculated and two experimental descriptors, pressure and temperature, were selected as the most feasible descriptors. The selected descriptors were used as input nodes in a wavelet neural network (WNN) model. The wavelet neural network architecture and its parameters were optimized simultaneously. The data was randomly divided to the training, prediction and validation sets. The predictive ability of the model was evaluated using validation data set. The root mean squares error (RMSE) and mean absolute errors were 0.339 and 0.221, respectively, for the validation data set. The performance of the WNN model was also compared with artificial neural network (ANN) model and the results showed the superiority of the WNN over ANN model.