Investigating the effect of [C8Py][Cl] and [C18Py][Cl] ionic liquids on the water/oil interfacial tension by considering Taguchi method

Capillary and interfacial forces are of great influences of trapping hydrocarbon in porous media after primary and secondary recovery processes. The trapped crude oil in the reservoir can be mobilized and produced by reducing these forces. Thus, surfactant flooding, as a main enhanced oil recovery (EOR) method, is usually applied to reduce the interfacial tension (IFT) of crude oil–water system in porous medium and improves the oil recovery. This study focused on the effect of [C8Py][Cl] and [C18Py][Cl] ionic liquids (ILs), as a new family of surfactant, in combination with various salts including sodium chloride, potassium chloride, magnesium sulfate and potassium sulfate on IFT reduction. EOR injection solutions were prepared from mixing the ILs at different concentrations of 100, 250, 500 and 1000 ppm with the salts ranging from 500 to 80,000 ppm. Obtained results showed that the minimum IFT value from both ILs was achieved when the concentration of the ILs was about 1000 g/mL, and the concentrations of KCl, K2SO4, MgSO4 and NaCl were 1000, 2000, 500 and 80,000 ppm, respectively. The minimum IFTs were achieved when NaCl and ILs concentrations were the maximum and MgSO4 concentration was the minimum

Effects of conventional and ionic liquid-based surfactants and sodium tetraborate on interfacial tension of acidic crude oil

Abstract The application of a new class of surfactants such as ionic liquids (ILs) compared with the conventional surfactants and their interactions with each other concomitant and alkaline under salinities is not well examined based on the best knowledge of the authors. So, the current work focused on the impact of sodium lauryl sulfate (SDS), sodium dodecyl benzene sulfonate (SDBS), 1-dodecyl 3-methyl imidazolium chloride (C12mim][Cl]), 1-octadecyl 3-methyl imidazolium chloride ([C18mim][Cl]) in the presence and absence of alkali namely sodium tetraborate known as borax (Na2B4O7) on the IFT variation while the salinity was changed 0–82,000 ppm (ionic strength of 0–1.4 M). The results showed the positive impact of salinity on the pH reduction and reduced the alkaline effect for pH reduction. Also, the measurements showed that the presence of surfactant reduces the role of alkaline for pH variation as it moved from 9.2 to 6.63 for the solution prepared using SLS and SDBS. The measured IFT values showed that not only alkali has a significant impact as it combined with SLS and SDBS due to a desired synergy between these chemicals, it can reduce the critical micelle concentration (CMC) for the SDBS from 1105 to 852 ppm and much higher for [C12mim][Cl]

Prediction of carbon dioxide solubility in ionic liquids using MLP and radial basis function (RBF) neural networks

Elimination of carbon dioxide from gas mixtures is a common commercial step in natural gas refineries. Nowadays, room-temperature ionic liquids, which are a relatively novel type of compounds have gained attention in recent years and have potential to be considered as a substitution for conventional volatile organic solvents in reaction and separation processes. No flammability, high thermal stability, a wide liquid range, and electric conductivity are some properties of ILs, which make them interesting more and more. Information about the solubility and the rate of solubility is a crucial factor for consideration of ILs for potential industrial processes. Because of some difficulties associated with experimental measurements and expenses spent on ILs, developing predictive methods for prognostication of the phase behavior of such types of systems are more favorable. Thermodynamic models are relatively complex and require complicated mathematical operations. Due to such difficulties there is a need to develop general models capable to predict phase behavior of systems such as CO2 with various kinds of ILs. In this study, four different methods based on artificial intelligence are proposed to predict CO2 solubility in different ionic liquids. The results showed that the predicted values are in great agreement with the experimental data and the maximum absolute error deviation for the best predictor is no more than 3.5%. A comparison between developed models and previously published ones reveals the superiority of the proposed models in this study