Eyring’s absolute rate theory was applied for evaluation of the viscosity of ionic liquids (ILs) containing binary mixtures. Considering the mathematical simplicity of the two-suffix-margules model, the Gibbs energy model was further modified. Furthermore, for viscosity evaluation, the proposed Gibbs energy model was coupled with Eyring’s theory. To validate the accuracy of the proposed model, a large set of data containing the binary mixtures of 122 ILs with a total number of 5512 experimental data points was collected from the literature. Moreover, the average absolute relative deviation (AARD %) was obtained as 2.07 %. Also, the capability of the Eyring–MTSM model was tested for the prediction of viscosity for binary and ternary systems. Additionally, comparison of the proposed model with the Eyring–NRTL model indicated a higher accuracy for our model. Finally, the Eyring–UNIFAC model was also checked, and it was found that this model is not accurate enough in its present form.

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The objective of this study is to develop a model to determine the thermal conductivity of pure ionic liquids and Ionanofluids. In order to estimate the thermal conductivity of pure ionic liquids, a group method of data handling model is proposed based on 23 ionic liquids corresponding to 216 experimental data points.The average absolute relative deviation for all studied systems was 1.81%, which is a satisfactory degree of accuracy for the proposed model. Furthermore, the Maxwell model is modified to correlate the thermal conductivity of Ionanofluids as a function of temperature and volume fraction of nanoparticles. The average absolute relative deviation for this model is 0.61%. Additionally, Maxwell and modified geometric mean (mGM) models are used to evaluate the models that * Corresponding predict the thermal conductivity of Ionanofluids. The results show that mGM is more accurate for prediction of thermal conductivity of Ionanofluids.

Due to industrial development, designing and optimal operation of processes in chemical and petroleum processing plants require accurate estimation of the hydrogen solubility in various hydrocarbons. Equations of state (EOSs) are limited in accurately predicting hydrogen solubility, especially at high-pressure or/and high-temperature conditions, which may lead to energy waste and a potential safety hazard in plants. In this paper, five robust machine learning models including extreme gradient boosting (XGBoost), adaptive boosting support vector regression (AdaBoost-SVR), gradient boosting with categorical features support (CatBoost), light gradient boosting machine (LightGBM), and multi-layer perceptron (MLP) optimized by Levenberg–Marquardt (LM) algorithm were implemented for estimating the hydrogen solubility in hydrocarbons. To this end, a databank including 919 experimental data points of hydrogen solubility in 26 various hydrocarbons was gathered from 48 different systems in a broad range of operating temperatures (213–623 K) and pressures (0.1–25.5 MPa). The hydrocarbons are from six different families including alkane, alkene, cycloalkane, aromatic, polycyclic aromatic, and terpene. The carbon number of hydrocarbons is ranging from 4 to 46 corresponding to a molecular weight range of 58.12–647.2 g/mol. Molecular weight, critical pressure, and critical temperature of solvents along with pressure and temperature operating conditions were selected as input parameters to the models. The XGBoost model best fits all the experimental solubility data with a root mean square error (RMSE) of 0.0007 and an average absolute percent relative error (AAPRE) of 1.81%. Also, the proposed models for estimating the solubility of hydrogen in hydrocarbons were compared with five EOSs including Soave–Redlich–Kwong (SRK), Peng–Robinson (PR), Redlich–Kwong (RK), Zudkevitch–Joffe (ZJ), and perturbed-chain statistical associating fluid theory (PC-SAFT). The XGBoost model introduced in this study is a promising model that can be applied as an efficient estimator for hydrogen solubility in various hydrocarbons and is capable of being utilized in the chemical and petroleum industries.

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