Ionic liquids (ILs) have shown remarkable potential for applications in separation, such as extractive distillation and liquid–liquid extraction. Crucial to these applications is the estimation of a significant property of the ILs which is the infinite dilution activity coefficient (IDAC) of different solutes in ILs. In this context, the present paper aims to model IDAC of 17 solutes in 44 imidazolium ILs using 2666 experimental data points gathered from the literature and based on support vector machine for the regression (SVMr) learning algorithm. Two models are developed, one based on SVMr and the other one based on dragonfly algorithm (DA) associated with SVMr. Both models consider the same set of predictive variables which are the temperature, the molecular weight of solute and solvent, and five conductor-like screening models for real solvents (COSMO-RS) σ-profile descriptors related to the solute and IL. The DA is applied for optimization of SVMr hyper-parameters. The results show the superiority of the DA-SVMr model demonstrated by its correlation coefficient (R) and root mean square error values of 0.996 and 0.170, respectively.

The study aims at modelling the drying kinetics of a pharmaceutical powder with active ingredient Candesartan Cilexetil. The kinetics was carried out in a vacuum dryer at different temperature levels, pressure, initial mass, and water content. The effect of some operating parameters on the drying time was studied. The modelling of drying times was based on the use of experimental design method. The data obtained were adjusted using 17 semi-empirical models, one proposed, a static ANN and DA_SVMR, regrouping all studied kinetics. The proposed model and DA_SVMR model were chosen as the most appropriate to describe the drying kinetics.

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.

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