RESUMEN

In the relentless battle against the impending climate crisis, deep eutectic solvents (DESs) have emerged as beacons of hope in the realm of green chemistry, igniting a resurgence of scientific exploration. These versatile compounds hold the promise of revolutionizing carbon capture, effectively countering the rising tide of carbon dioxide (CO2) emissions responsible for global warming and climate instability. Their adaptability offers a tantalizing prospect, as they can be finely tailored for a multitude of applications, thereby encompassing the uncharted territory of potential DESs. Navigating this unexplored terrain underscores the vital need for predictive computational methods, which serve as our guiding compass in the expansive landscape of DESs. Thermodynamic modeling and solubility prognostications stand as our unwavering navigational aides on this treacherous odyssey. In this direction, the COSMO-RS model intertwined with the captivating Stochastic Gradient Boosting (SGB) algorithm. Together, they unveil the elusive truths pertaining to CO2 solubility in DESs, forging a path toward a sustainable future. Our quest is substantiated by two exhaustive datasets, a repository of knowledge encompassing 1973 and 2327 CO2 solubility data points spanning 132 and 150 distinct DESs respectively, encapsulating a spectrum of conditions. The SGB models, incorporating features derived from COSMO-RS, as well as accounting for pressure and temperature variables, furnishes predictions that harmonize seamlessly with experimental CO2 solubility values, boasting an impressive Average Absolute Relative Deviation (AARD) of a mere 0.85% and 2.30% respectively. When juxtaposed with literature-reported methodologies like different EoS, as well as Computational Solvation, and machine learning (ML) models, our SGB model emerges as the epitome of reliability, offering robust forecasts of CO2 solubility in DESs. It emerges as a potent tool for the design and selection of DESs for CO2 capture and utilization, heralding a sustainable and environmentally conscientious future in the battle against climate change.

Asunto(s)

RESUMEN

This study investigated the effect of silane-based silica (SiO2) Janus nanoparticles (JNPs) on stabilizing the foam generated by different types of gases. Two types of SiO2 JNPs were synthesized through surface modification using HMDS and APTS silane compounds. Static analyses were conducted to examine the impact of different concentrations of the synthesized nanoparticles in various atmospheres (air, CO2, and CH4) on surface tension, foamability, and foam stability. The results indicated that the synthesized SiO2 JNPs and bare SiO2 nanoparticles exhibited nearly the same ability to reduce surface tension at ambient temperature and pressure. Both of these nanoparticles reduced the surface tension from 71 to 58-59 mN m-1 at 15,000 ppm and 25 °C. While bare SiO2 nanoparticles exhibited no foamability, the synthesis of SiO2 JNPs significantly enhanced their ability to generate and stabilize gas foam. The foamability of HMDS-SiO2 JNPs started at a higher concentration than APTS-SiO2 JNPs (6000 ppm compared to 4000 ppm, respectively). The type of gas atmosphere played a crucial role in the efficiency of the synthesized JNPs. In a CH4 medium, the foamability of synthesized JNPs was superior to that in air and CO2. At a concentration of 1500 ppm in a CH4 medium, HMDS-SiO2 and APTS-SiO2 JNPs could stabilize the generated foam for 36 and 12 min, respectively. Due to the very low dissolution of CO2 gas in water at ambient pressure, the potential of synthesized JNPs decreased in this medium. Finally, it was found that HMDS-SiO2 JNPs exhibited better foamability and foam stability in all gas mediums compared to APTS-SiO2 JNPs for use in oil reservoirs. Also, the optimal performance of these JNPs was observed at a concentration of 15,000 ppm in a methane gas medium.

RESUMEN

Precise estimation of the physical properties of both ionic liquids (ILs) and their mixtures is crucial for engineers to successfully design new industrial processes. Among these properties, surface tension is especially important. It's not only necessary to have knowledge of the properties of pure ILs, but also of their mixtures to ensure optimal utilization in a variety of applications. In this regard, this study aimed to evaluate the effectiveness of Stochastic Gradient Boosting (SGB) tree in modeling surface tensions of binary mixtures of various ionic liquids (ILs) using a comprehensive dataset. The dataset comprised 4010 experimental data points from 48 different ILs and 20 non-IL components, covering a surface tension range of 0.0157-0.0727 N m-1 across a temperature range of 278.15-348.15 K. The study found that the estimated values were in good agreement with the reported experimental data, as evidenced by a high correlation coefficient (R) and a low Mean Relative Absolute Error of greater than 0.999 and less than 0.004, respectively. In addition, the results of the used SGB model were compared to the results of SVM, GA-SVM, GA-LSSVM, CSA-LSSVM, GMDH-PNN, three based ANNs, PSO-ANN, GA-ANN, ICA-ANN, TLBO-ANN, ANFIS, ANFIS-ACO, ANFIS-DE, ANFIS-GA, ANFIS-PSO, and MGGP models. In terms of the accuracy, the SGB model is better and provides significantly lower deviations compared to the other techniques. Also, an evaluation was conducted to determine the importance of each variable in predicting surface tension, which revealed that the most influential factor was the mole fraction of IL. In the end, William's plot was utilized to investigate the model's applicability range. As the majority of data points, i.e. 98.5% of the whole dataset, were well within the safety margin, it was concluded that the proposed model had a high applicability domain and its predictions were valid and reliable.

RESUMEN

Seawater pollution from various sources such as industrial effluents, ship washing at sea, and oil spills harm humans and the marine environment. Therefore, finding ways to eliminate this pollution is crucial. This study successfully modified a polyurethane sponge through a simple dip-coating method with functionalized graphene oxide incorporating octadecylamine and oleic acid, resulting in a hydrophobic sponge capable of absorbing crude oil and various organic solvents. Characterization analyses confirmed the synthesis. The absorption capacity of the modified sponges was examined, for example, the PU sponge has absorbed 4 g/g engine oil, while the modified GO-ODA-PU sponge has increased its absorption to 36 g/g. The GO-ODA-PU sponge demonstrated great reusability compared to the GO-OA-PU sponge owing to the strong covalent bond formed between GO and ODA, which is superior to the weak hydrogen bond formed between GO and OA. The absorption capacity of the GO-OA-PU sponge decreased by 30%. The contact angle test showed that GO-ODA-PU and GO-OA-PU sponges had contact angles of 131° and 115°, respectively. Additionally, the GO-ODA-PU sponge performed optimally for semi-polar solvents in the solubility parameter range of 18-19, with its absorption capacity reaching its maximum value. The amount of oil recycling is even possible up to 98%.

Asunto(s)

RESUMEN

In recent years, the application of smart water and surfactant in order to improve oil recovery has attracted special attention in carbonate reservoirs. In this research, the effects of various salts in smart water and two surfactants of Cetyl Trimethyl Ammonium Bromide (CTAB) and Sodium Dodecyl Sulfate (SDS) on the wettability alteration of carbonate rock and IFT were studied. Besides, along with micromodel flooding, core flooding tests were conducted to assess the amount of oil recovery at reservoir conditions as an injection scheme was used. In this regard, the results illustrated that the presence of CTAB or SDS in seawater (SW) can act better in contact angle reduction compared to smart water. Also, a four times increase in the concentration of SO42- and removing Na+ from SW reduced the contact angle to 68° and 71°, respectively, being the best possible options to alter the carbonate surface wettability to more water-wet states. Moreover, in the second-order process in which the rock section was first placed in SW, and then was put in the smart solution (with or without surfactant), CTAB had a great effect on the wettability alteration. In the case of IFT reduction, although SW4Mg2+, compared to other ions, better decreased the IFT to 17.83 mN/m, SW + SDS and SW + CTAB further declined the IFT to 0.67 and 0.33 mN/m, respectively. Concerning different ions, divalent cations (Mg2+ and Ca2+) show better results in improving oil recovery factor. However, the combination of SW and surfactants has a more positive effect on boosting oil recovery, as compared to smart water flooding. It should be mentioned that the first-order injection is better than the second-order one since SW is flooded at first, and then, after the breakthrough, smart water is injected into the micromodel. In addition, the core flooding tests showed that SW + CTAB and SW + SDS in tertiary injection increased the oil recovery to about 59 and 57%, respectively, indicating that the presence of CTAB could be more effective than that of SDS.

Asunto(s)

RESUMEN

In the current investigation, molecular dynamics (MD) and Grand Canonical Monte Carlo (GCMC) simulation as remarkable and competent approaches have been employed for understanding structural and transport properties of MMMs in the realm of gas separation. The two commonly used polymers i.e. polysulfone (Psf) and polydimethylsiloxane (PDMS) as well as zinc oxide (ZnO) nanoparticle (NP) were used to carefully examine the transport properties of three light gasses (CO2, N2 and CH4) through simple Psf, Psf/PDMS composite loaded by different amounts of ZnO NP. Also, the fractional free volume (FFV), X-ray diffraction (XRD), glass transition temperature (Tg), and Equilibrium density were calculated to scrutinize the structural characterizations of the membranes. Moreover, the effect of feed pressure (4-16 bar) on gas separation performance of simulated MMMs was investigated. Results obtained in different experiments showed a clear improvement in the performance of simulated membranes by adding PDMS to PSf matrix. The selectivity of studied MMMs was in the range from 50.91 to 63.05 at pressures varying from 4 to 16 bar for the CO2/N2 gas pair, whereas the corresponding value for CO2/CH4 system was found to be in the range 27.27-46.24. For 6 wt% ZnO in 80%PSf + 20%PDMS membrane, high permeabilities of 78.02, 2.86 and 1.33 barrers were observed for CO2, CH4 and N2 gases, respectively. The 90%PSf + 10%PDMS membrane with 2% ZnO had a highest CO2/N2 selectivity value of 63.05 and its CO2 permeability at 8 bar was 57 barrer.