COSMO-RS: An ionic liquid prescreening tool for gas hydrate mitigation

Effect of ammonium hydroxide-based ionic liquids' freezing point and hydrogen bonding on suppression temperature of different gas hydrates

The study presents the effect of freezing point depression and hydrogen bonding energy interaction on four ammonium hydroxide-based ionic liquids (AHILs) of gas hydrate systems. The AHILs investigated are tetramethylammonium hydroxide, tetraethylammonium hydroxide, tetrapropylammonium hydroxide, and tetrabutylammonium hydroxide. The considered hydrate system includes methane (CH₄), carbon dioxide (CO₂), and three binary mixed gas hydrates (70-30 CO₂ + CH₄, 50-50 CO₂ + CH₄, 30-70 CO₂ + CH₄), which are often encountered in the flow assurance pipelines. The experimental temperature range is between 274.0 and 285.0 K, corresponding to pipeline pressures for different gas systems. The thermodynamic influence, i.e., average suppression temperature (ΔŦ) of the studied system, was reported for different mass concentrations (1, 5, and 10 wt%) and correlated with the freezing point depression and hydrogen bonding energy interaction of AHILs. The study also covers the structural impact of AHILs (in the form of alkyl chain variation) on the thermodynamic hydrate inhibition (THI) behaviour via freezing point and hydrogen bonding energy interactions. Findings revealed that the increased alkyl chain length of AHILs reduced the ΔŦ due to a decrease in hydrogen bonding ability. The highest THI inhibition (ΔŦ = 2.27 K) is attained from the lower alkyl chain AHIL, i.e., TMAOH (10 wt%) for the CO₂ hydrate system. The freezing point depression of AHILs is a concentration-dependent phenomenon. Increased concentration of the AHILs in the system yielded lower freezing point temperature, positively influencing hydrate mitigation. Although the study provided the initial insight between the freezing point tendency and hydrogen bonding energies of AHILs on thermodynamic inhibition (ΔŦ). Based on the freezing point depression and hydrogen bonding energy interaction, a more generalized correlation should be developed to predict any potential ionic liquids regarded as promising hydrate inhibitors.

A Prediction for the Conversion Performance of H2S to Elemental Sulfur in an Ionic-Liquid-Incorporated Transition Metal Using COSMO-RS

In the present study, the conversion performance of hydrogen sulfide (H2S) to elemental sulfur in ionic-liquid-incorporated transition metals (ILTMs) is predicted using a conductor-like screening model for realistic solvents (COSMO-RS). The predictions were made via the establishment of a correlation between the conversion performance and solubility of H2S in ionic liquids (ILs). All molecules involved were optimized at the DFT/TZVP/M06 computational level and imported on the COSMOtherm program at equimolar conditions. For validation purposes, the solubility of ILs was predicted at 1 bar pressure. Simple regression analysis was used to establish a relationship between the solubility and conversion performance of H2S. The results indicate that the solubility prediction of ILs is accurate (R2 = 93.40%) with a p-value of 0.0000000777. Additionally, the conversion performance is generally found to be dependent on the solubility value. Furthermore, 1-butyl-3-methylimidazolium chloride [bmim][Cl] was chosen as the base IL for incorporating the transition metal, owing to its solubility and selectivity to H2S. The solubility trend of ILTMs is found to follow the following order: [bmim][NiCl3] > [bmim][FeCl4] > [bmim][CoCl3] > [bmim][CuCl3]. According to the viscosity measurements of ILTMs, [bmim][NiCl3] and [bmim][FeCl4] exhibited the highest and lowest viscosity values, respectively. Therefore, [bmim][FeCl4] is a promising ILTM owing to its higher solubility and low viscosity for the application studied.

Kinetic Behavior of Quaternary Ammonium Hydroxides in Mixed Methane and Carbon Dioxide Hydrates

This study evaluates the kinetic hydrate inhibition (KHI) performance of four quaternary ammonium hydroxides (QAH) on mixed CH₄ + CO₂ hydrate systems. The studied QAHs are; tetraethylammonium hydroxide (TEAOH), tetrabutylammonium hydroxide (TBAOH), tetramethylammonium hydroxide (TMAOH), and tetrapropylammonium hydroxide (TPrAOH). The test was performed in a high-pressure hydrate reactor at temperatures of 274.0 K and 277.0 K, and a concentration of 1 wt.% using the isochoric cooling method. The kinetics results suggest that all the QAHs potentially delayed mixed CH₄ + CO₂ hydrates formation due to their steric hindrance abilities. The presence of QAHs reduced hydrate formation risk than the conventional hydrate inhibitor, PVP, at higher subcooling conditions. The findings indicate that increasing QAHs alkyl chain lengths increase their kinetic hydrate inhibition efficacies due to better surface adsorption abilities. QAHs with longer chain lengths have lesser amounts of solute particles to prevent hydrate formation. The outcomes of this study contribute significantly to current efforts to control gas hydrate formation in offshore petroleum pipelines.

Prediction of the Solubility of CO2 in Imidazolium Ionic Liquids Based on Selective Ensemble Modeling Method

Solubility data is one of the essential basic data for CO2 capture by ionic liquids. A selective ensemble modeling method, proposed to overcome the shortcomings of current methods, was developed and applied to the prediction of the solubility of CO2 in imidazolium ionic liquids. Firstly, multiple different sub–models were established based on the diversities of data, structural, and parameter design philosophy. Secondly, the fuzzy C–means algorithm was used to cluster the sub–models, and the collinearity detection method was adopted to eliminate the sub–models with high collinearity. Finally, the information entropy method integrated the sub–models into the selective ensemble model. The validation of the CO2 solubility predictions against experimental data showed that the proposed ensemble model had better performance than its previous alternative, because more effective information was extracted from different angles, and the diversity and accuracy among the sub–models were fully integrated. This work not only provided an effective modeling method for the prediction of the solubility of CO2 in ionic liquids, but also provided an effective method for the discrimination of ionic liquids for CO2 capture.

Rheology Impact of Various Hydrophilic-Hydrophobic Balance (HLB) Index Non-Ionic Surfactants on Cyclopentane Hydrates

In this study, series of non-ionic surfactants from Span and Tween are evaluated for their ability to affect the viscosity profile of cyclopentane hydrate slurry. The surfactants; Span 20, Span 40, Span 80, Tween 20, Tween 40 and Tween 80 were selected and tested to provide different hydrophilic–hydrophobic balance values and allow evaluation their solubility impact on hydrate formation and growth time. The study was performed by using a HAAKE ViscotesterTM 500 at 2 °C and a surfactant concentration ranging from 0.1 wt%–1 wt%. The solubility characteristic of the non-ionic surfactants changed the hydrate slurry in different ways with surfactants type and varying concentration. The rheological measurement suggested that oil-soluble Span surfactants was generally inhibitive to hydrate formation by extending the hydrate induction time. However, an opposite effect was observed for the Tween surfactants. On the other hand, both Span and Tween demonstrated promoting effect to accelerate hydrate growth time of cyclopentane hydrate formation. The average hydrate crystallization growth time of the blank sample was reduced by 86% and 68% by Tween and Span surfactants at 1 wt%, respectively. The findings in this study are useful to understand the rheological behavior of surfactants in hydrate slurry.

Elgharbawy A, Khezri R, Wilfred CD. Prediction of Potential Ionic Liquids (ILs) for the Solid-Liquid Extraction of Docosahexaenoic Acid (DHA) from Microalgae Using COSMO-RS Screening Model

This study performs a screening of potential Ionic Liquids (ILs) for the extraction of Docosahexaenoic Acid (DHA) compounds by the calculation of capacity values. For this purpose, a Conductor-Like Screening Model for Real Solvents (COSMO-RS) was employed to study the molecular structures of the ILs, and therefore, predict their extraction potential. The capacity values of 22 anions combined with 16 cations based ILs, were investigated to evaluate the effectiveness of ILs in the extraction of DHA. It was found that among the investigated ILs, a combination of tetramethyl ammonium with SO₄ or Cl was the best fit for DHA extraction, followed by pyrrolidinium, imidazolium, pyridinium and piperidinium. Furthermore, it was observed that the extraction capacity and the selectivity of ILs decreased with an increase in alkyl chain length; therefore, ethyl chain-ILs, with the shortest chain lengths, were found to be most suitable for DHA extraction. The predicted results were validated through the experimentally calculated extraction yield of a DHA compound from Nannochloropsis sp. Microalgae. Five selected ILs, namely [EMIM][Cl], [BMIM][Cl], [TMAm][Cl], [EMPyr][Br] and [EMPyrro][Br], were selected from COSMO-RS for empirical extraction purposes, and the validation results pinpointed the good prediction capabilities of COSMO-RS. The findings in this study can simplify the process of selecting suitable ILs for DHA extraction and reduce the number of required empirical evaluations.

Prediction of CO2 Solubility in Ionic Liquids Based on Multi-Model Fusion Method

Reducing the emissions of greenhouse gas is a worldwide problem that needs to be solved urgently for sustainable development in the future. The solubility of CO2 in ionic liquids is one of the important basic data for capturing CO2. Considering the disadvantages of experimental measurements, e.g., time-consuming and expensive, the complex parameters of mechanism modeling and the poor stability of single data-driven modeling, a multi-model fusion modeling method is proposed in order to predict the solubility of CO2 in ionic liquids. The multiple sub-models are built by the training set. The sub-models with better performance are selected through the validation set. Then, linear fusion models are established by minimizing the sum of squares of the error and information entropy method respectively. Finally, the performance of the fusion model is verified by the test set. The results showed that the prediction effect of the linear fusion models is better than that of the other three optimal sub-models. The prediction effect of the linear fusion model based on information entropy method is better than that of the least square error method. Through the research work, an effective and feasible modeling method is provided for accurately predicting the solubility of CO2 in ionic liquids. It can provide important basic conditions for evaluating and screening higher selective ionic liquids.