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Ionic liquids (ILs) are considered unique and attractive types of solvents with great potential to capture carbon dioxide (CO2) and reduce its emissions into the atmosphere. On the other hand, carrying out experimental measurements of CO2 solubility for each new IL is time‐consuming and expensive. Whereas, the possible combinations of cations and anions are numerous. Therefore, the preparation and design of such processes requires simple and accurate models to predict the solubility of CO2 as a greenhouse gas. In the present study, two different models, namely: artificial neural network (ANN) and support vector machine optimized with dragonfly algorithm (DA‐SVM) were used in order to establish a quantitative structure–property relationship (QSPR) between the molecular structures of cations and anions and the CO2 solubility. More than 10 116 CO2 solubility data measured in various ionic liquids (ILs) at different temperatures and pressures were collected. 13 significant PaDEL descriptors (E2M, MATS8S, TDB6I, TDB1S, ATSC4V, MATS8M, ATSC7V, Gats2S, Gats5S, Gats5C, ATSC6V, DE, and Lobmax), temperature and pressure were considered as the model input data. For the test set data (2023 data point), the estimated mean absolute error (MAE) and R2 for the ANN model are of 0.0195 and 0.9828 and 0.0219 and 0.9745 for the DA‐SVM model. The results obtained showed that both models can reliably predict the solubility of CO2 in ILs with a slight superiority of the ANN model. Examination of sensitivity and outlier diagnosis examinations confirmed that the QSPR model optimized using the ANN algorithm is better suited to correlate and predict this property. [ABSTRACT FROM AUTHOR]

This work aims to examine the interaction of certain physicochemical properties on micellar systems constituted of a polymer (sodium alginate), two surfactants (CTand tween 80), and Algerian olive oil. Response surface modelling (RSM) was applied to study the combined effects of systems containing each type of surfactant. The monitoring of four independent parameters such as the interfacial tension (Y1), the conductivity (Y2), the viscosity (Y3), and the turbidity (Y4) as responses for the experimental design model allowed to determine the performance of the models established. Based on statistical analyzes, the coefficients R2 and Q2 for the interfacial tension, the conductivity, the viscosity and the turbidity are: 0.998 and 0.805; 0.982 and 0.742; 0.976 and 0.734, and 0.985 and 0.723; respectively. The obtained results indicate that these models showed a good predictive power for an optimal system composed of CTAB, Tween 80, AlgNa, and olive oil. For the CT/ AlgNa and CT/ olive oil systems, interfacial tension values of 33.85 and 34.39 mN m-1. Maximum conductivity values of 4.126 and 4.064 mScm-1 were also obtained. For viscous compounds consisting of AlgNa / Olive Oil and AlgNa / Tween 80, maximum viscosity values of 202.5 and 196.6 mPa s were obtained. For the same systems as those for viscosity, turbidity values of 300 and 304 NTU were obtained.

Ionic liquids (IL) are a new class of liquids considered as green solvents; less toxic, less flammable, and less polluting which retain their liquid state over wide temperature ranges and are considered alternatives to volatile organic solvents. The surface tension of IL‐organic solvent mixtures plays an important role in the design and development of many industrial processes. This work investigated the capability and feasibility of four ANN model topologies ("trainbr, logsig"; "trainbr, tansig"; "trainlm, logsig"; "trainlm, tansig"), a PSO‐SVM model, and an LSSVM model to predict the surface tension of binary systems containing IL. For this purpose, 1623 data points corresponding to the experimental surface tension values of binary mixtures containing IL were collected from the literature. The surface tension values were between 18.9 and 72.7 mN m−1. The temperature, the composition in mole fraction of IL (XIL), descriptors based on the sigma profiles, relating to the H‐bond donor and to the H‐bond acceptor character, the anion, the cation and the solvent were used as input variables of the model in order to differentiate the different compounds involved in the binary systems. A comparison of the experimental and the predicted values in terms of several statistical metrics showed good agreement, however, the prediction (trainbr, logsig) was better than the other approaches with an overall average absolute relative deviation of.8466% and a mean square error of.4952. These results are very encouraging for future projects modeling other physical and chemical properties of ILs. [ABSTRACT FROM AUTHOR]