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.

Determining the solubility of non-hydrocarbon gases such as carbon dioxide (CO₂) and nitrogen (N₂) in water and brine is one of the most controversial challenges in the oil and chemical industries. Although many researches have been conducted on solubility of gases in brine and water, very few researches investigated the solubility of power plant flue gases (CO₂–N₂mixtures) in aqueous solutions. In this study, using six intelligent models, including Random Forest, Decision Tree (DT), Gradient Boosting-Decision Tree (GB-DT), Adaptive Boosting-Decision Tree (AdaBoost-DT), Adaptive Boosting-Support Vector Regression (AdaBoost-SVR), and Gradient Boosting-Support Vector Regression (GB-SVR), the solubility of CO₂–N₂mixtures in water and brine solutions was predicted, and the results were compared with four equations of state (EOSs), including Peng–Robinson (PR), Soave–Redlich–Kwong (SRK), Valderrama–Patel–Teja (VPT), and Perturbed-Chain Statistical Associating Fluid Theory (PC-SAFT). The results indicate that the Random Forest model with an average absolute percent relative error (AAPRE) value of 2.8% has the best predictions. The GB-SVR and DT models also have good precision with AAPRE values of 6.43% and 7.41%, respectively. For solubility of CO₂present in gaseous mixtures in aqueous systems, the PC-SAFT model, and for solubility of N₂, the VPT EOS had the best results among the EOSs. Also, the sensitivity analysis of input parameters showed that increasing the mole percent of CO₂in gaseous phase, temperature, pressure, and decreasing the ionic strength increase the solubility of CO₂–N₂mixture in water and brine solutions. Another significant issue is that increasing the salinity of brine also has a subtractive effect on the solubility of CO₂–N₂mixture. Finally, the Leverage method proved that the actual data are of excellent quality and the Random Forest approach is quite reliable for determining the solubility of the CO₂–N₂gas mixtures in aqueous systems.

Asphaltene precipitation and its adsorption on different surfaces are challenging topics in the upstream and downstream of the oil industries and the environment. In this research, the phenomenon of asphaltenes adsorption in the presence and absence of water on the surface of magnetite, hematite, calcite, and dolomite nanoparticles (NPs) was investigated. Five asphaltenes of different origins, four NPs as adsorbents and Persian Gulf water were used for three-phase (asphaltene/toluene solution + NPs + water) experiments. Characterization of asphaltenes and NPs was performed using Fourier transform infrared spectroscopic (FTIR), dynamic light scattering (DLS), elemental analysis, and field emission scanning electron microscopy (FESEM). Adsorption experiments were performed in two- (asphaltene/toluene solution + NPs) and three-phase systems. The results showed that the most effective parameters for asphaltene adsorption onto these NPs are the asphaltene composition, namely nitrogen content, and the aromaticity of asphaltenes. The significant effects of these parameters were also confirmed by the relevancy factor function as a sensitivity analysis. In the competition of asphaltene adsorption capacity by NPs, iron oxide NPs had the highest adsorption (Magnetite NPs > Hematite NPs > Calcite NPs > Dolomite NPs). From the results of the experiments in the presence of water phase, it could be pointed out that the asphaltenes adsorption onto the NPs was accompanied by a decrease compared to the experiments in the absence of water. The modeling also showed that physical adsorption has a significant contribution to the asphaltenes adsorption on the surface of iron oxides and lime NPs. The results of this research can assist in a better understanding of the asphaltene adsorption phenomenon and the role of iron oxide and lime NPs in solving this problem.