Emilio J. González

Synthesis and characterization of new polysubstituted pyridinium-based ionic liquids: application as solvents on desulfurization of fuel oils

The production of transportation fuels which have a very low content of sulfur has became one of the priority challenges for the oil industry worldwide, due to by strict new regulatory requirements. Ionic liquids (ILs) have been proposed as suitable and promising solvents for this purpose due to their excellent qualities as solvents. In this work a series of ten new ILs derived from pyridinium cation substituted with different alkyl chains have been synthesized from 2-alkyl-3,5-dimethylpyridines. The starting materials were prepared by selective metalation of 2,3,5-trimethylpyridine, which allowed the introduction of different alkyl groups in pyridine position 2 with high yields. To test the ILs sulfur-removal capacity, liquid–liquid equilibrium (LLE) data for ternary systems (heptane + thiophene + IL) were determined at T = 298.15 K and atmospheric pressure. Selectivity and solute distribution ratio, calculated from tie-lines, were used to evaluate whether these new ILs could be used as solvents for the extraction of thiophene from heptane. Finally, the experimental LLE data were correlated with the NRLT thermodynamic model.

Excess properties of binary mixtures hexane, heptane, octane and nonane with benzene, toluene and ethylbenzene at T = 283.15 and 298.15 K

Densities, speeds of sound and the refractive indices of binary systems containing alkanes (hexane, heptane, octane and nonane) with aromatic compounds (benzene, toluene and ethylbenzene) at T = 283.15 and 298.15 K under atmospheric pressure were determined over the whole composition range. From the experimental results, the derived and excess properties (excess molar volumes, isentropic compressibility, excess molar isentropic compressibility and refractive index deviations) at T = 283.15 and 298.15 K were calculated and satisfactorily fitted to the Redlich–Kister equation.

Modeling of Ionic Liquid Systems: Phase Equilibria and Physical Properties

Ionic liquids (ILs) are a class of salts with a melting temperature below 100 °C, and the study of these compounds is considered priority by the U.S. Environmental Protection Agency. Due to their specific properties, which can be adjusted by changing either the cation or the anion, ILs have received great attention by the scientific community as potential replace‐ ments for volatile organic solvents (VOCs), and nowadays, ILs are starting to leave academ‐ ic labs and find their way into a wide variety of industrial applications [1]. For example, ILs are used for the dispersion of nano-materials at IOLITEC, Air Products uses ILs instead of pressurized cylinders as a transport medium for reactive gases, ION Engineering is com‐ mercializing technology using ILs and amines for CO2 capture and natural gas sweetening, and many others.

Solubility, density and excess molar volume of binary mixtures of aromatic compounds and common ionic liquids at T = 283.15 K and atmospheric pressure

In this work, the solubility of aromatic compounds (benzene, or toluene, or ethylbenzene, or o-xylene, or m-xylene, or p-xylene) in several ionic liquids (1-ethyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide, or 1-hexyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide, or 1-ethyl-3-methylpyridinium bis(trifluoromethylsulfonyl)imide, or 1-propyl-3-methylpyridinium bis(trifluoromethylsulfonyl)imide, or 1-ethyl-3-methylpyridinium ethylsulfate, or 1-hexyl-3-methylimidazolium dicyanamide) was experimentally determined at T = 283.15 K and atmospheric pressure. Moreover, density of the above-mentioned mixtures was also measured over the miscible region at the same temperature. This physical property was used to calculate the corresponding excess molar volume, which was fitted to the Redlich–Kister equation.

Evaluation of [C3mim][NTf2] as Solvent for the Liquid-Liquid Extraction of Benzene from Mixtures of Benzene and Hexane

In this paper, the liquid-liquid extraction of benzene from hexane with 1-propyl-3-methylimidazolium bis{trifluoromethylsulfonyl}imide, [C3mim][NTf2], as solvent was studied. The liquid-liquid equilibrium (LLE) data of the ternary system [hexane + benzene + ionic liquid] were measured at 298.15 K and atmospheric pressure. From the experimental composition data, the selectivity and the solute distribution ratio were calculated. The reliability of the experimental LLE data was ascertained using the Othmer-Tobias equation, and the NRTL thermodynamic model was used to correlate the experimental data. Moreover, a comparison with literature data for the ternary systems [hexane + benzene + ionic liquid] was also carried out.

Active Learning of Process Control

Abstract Process Control is a course that needs a thorough understanding of how the different unit operations work and what are the implications of changing operation variables in a process. This paper presents how education innovation can help students to improve their learning and understanding of the different concepts and thus to get better results in the subject and to achieve the desired outcomes. The Process Control Course is taught in the Bachelor Degree in Chemical Engineering at the Technical University of Madrid. Different methodologies have been integrated and used in the course as: flipped classroom, peer instruction, and gamification. In order to implement the mentioned methods, the following material has been developed: screencasts, concept tests, trivia contest and simulations besides the traditional material (slides and text). First year results show high student motivation, higher participation in class and better results (marks) in the subject.

COSMO-derived descriptors applied in ionic liquids physical property modelling using machine learning algorithms

Abstract An application of machine learning algorithms for the prediction of physical properties of ionic liquids is presented herein. Molecular descriptors obtained from quantum-chemistry calculations (COSMO theory (Klamt, 2004)) containing both structural and energetic information are used as input parameters. In this sense, a set of COSMO-based descriptors is proposed by reduction of the original σ-profile (51 descriptors reduced to 9 bins). A critically evaluated set of viscosity data is used for a large number of ionic liquids (159). Artificial neural networks are then trained for the correlation of liquid viscosity and compared with available tools (QSPR).

Selection of a minimum toxicity and high performance ionic liquid mixture for the separation of aromatic - aliphatic mixtures by extractive distillation

Abstract The separation of aromatic compounds from naphtha (mainly aliphatics) has a great interest for the petrochemical industry. This separation is commonly carried out by liquid- liquid extraction using different solvents which entails very high energy cost during the solvent recovery steps. Recently, ionic liquids (ILs) have been proposed as entrainers for the separation of this mixtures. In this paper we present a multiobjective optimization to select of ILs. One objective is minimize the toxicity of the mixture of ionic liquids used as entrainer in the separation of aromatics and aliphatics mixtures. The second target is to improve the separation performance in the extractive distillation process. The estimation of the toxicity has been carried out by training an artificial neural network (ANN) from structure information and toxicity values of ionic liquids. Results show a high correlation between the presence of heteroatoms and toxicity. The separation of aromatic and aliphatic hydrocarbons was evaluated through rigorous simulation of an extractive distillation process as detailed in Diaz et al. (2016). The evaluation of objectives was carried out in MATLAB connecting Aspen Plus simulations of the single stage extractive distillation process (vapor-liquid-liquid equilibria)through COM objects and evaluating the trained ANN for the ILs mixtures evaluated in each iteration. The multiobjective optimization of the problem was performed using a derivative free algorithm (genetic algorithm). Results show that 1-ethyl-3-methylimidazolium dicyanamide ([EMIM][DCA]) is a promising solvent in terms of separation performance and low toxicity.