Correlation of ionic liquid viscosity using Valderrama-Patel-Teja cubic equation of state and the geometric similitude concept. Part II: Binary mixtures of ionic liquids

Valderrama, José O.; Cardona, Luis F.; Rojas, Roberto E.

doi:10.1016/j.fluid.2019.04.034

Cited by 18 publications

(28 citation statements)

References 33 publications

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Section: Resultsmentioning

confidence: 99%

“…Mixtures involving ILs have been tested by different authors at variable temperature [26,27] and also pressure. [26] Good fit is obtained when the model is correlative [26] or the test set selection is random . [27] Our approach, differently from those works, is based on fixed length matrixes and allows one to compare systems of different number/nature of components at variable temperature and pressure.…”

Section: Resultsmentioning

confidence: 99%

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Machine‐Learning Approaches to Tune Descriptors and Predict the Viscosities of Ionic Liquids and Their Mixtures

Carrera

Ponte

2020

Chemistry Methods

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This work consists on a new chemoinformatic approach based on two complementary artificial intelligence concepts. Random Forest and Kohonen neural network are applied on this context. The former provides a relevance measure of the numerical descriptors encoding either an ionic liquid or its mixtures. The code of a given chemical system is weighted according that relevance measure. The Kohonen neural network is trained with a set of weighted chemical systems. The next step comprises the use of the trained neural network as platform to obtain a tuned profile of numerical descriptors representing a generical chemical system. The tuning mechanism involves the topology of a chemical system-encoding vector in the neural network. The last step comprises the use of the tuned chemical systems to build predictive models of viscosities. The MOLMAP encoding technology is applied to represent ionic liquid systems and its mixtures.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Machine‐Learning Approaches to Tune Descriptors and Predict the Viscosities of Ionic Liquids and Their Mixtures

Carrera

Ponte

2020

Chemistry Methods

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“…En la tabla 1, el 79 % de los líquidos iónicos son del tipo imidazolium, seguido de pyridinium con un 11 % y finalizando con phosphonium (5 %), pyrrolidinium y ammonium (3 %). Frente a las propiedades físicas y de transporte, se observa que la conductividad térmica varía entre 1.17•10 -1 y 2.04•10 -1 W/mK, la velocidad del sonido varía entre 1.17•10 3 y 2.89•10 3 m/s y la tensión superficial entre 2.15•10 -2 y 5.04•10 -2 N/m, valores que se encuentran dentro de lo reportado por diferentes trabajos en la literatura (Valderrama et al, 2019b;Cardona y Valderrama (2020a) y Cardona y Valderrama (2020b)). En particular, la viscosidad de los líquidos iónicos varía entre 1.10•10 -2 y 8.54•10 -1 Pa•s, altos valores cuando son comparados con el agua (µ = 1.00•10 -3 Pa•s a T = 293 K y P = 1 atm) y el decano (µ = 9.16•10 -4 Pa•s a T = 293 K y P = 1 atm).…”

Section: Modelo De Conductividad Térmicaunclassified

Modelo simple y generalizado para estimar la conductividad térmica de líquidos iónicos

Cardona-Palacio

2021

Rev. Cient.

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En este trabajo se generaliza un modelo empírico para predecir la conductividad térmica de líquidos iónicos. Inicialmente se realiza una recopilación de datos experimentales con diferentes tipos de aniones y cationes. En total, se evalúan 38 líquidos iónicos (28 sustancias para correlación y 10 sustancias para predicción) que corresponden a 265 datos experimentales. Los rangos de temperatura varían entre 273 K y 390 K a presión atmosférica (1 atm). Los parámetros del modelo son generalizados, utilizando 215 datos experimentales y las desviaciones promedio son de 4.72 %. Posteriormente, se evalúan las capacidades predictivas en 50 datos experimentales con desviaciones promedio de 3.48 %. El modelo generalizado es extendido a mezclas binarias utilizando una regla de mezcla simple con un parámetro de interacción y la desviación absoluta es de 0.77 %. Finalmente, el modelo es comparado con otros en la literatura y los resultados estadísticos muestran que proporciona resultados aceptables.

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“…Thus, new pressure–viscosity–temperature and pressure–thermal conductivity–temperature cubic EoS’s have been recently proposed. 84 − 87 In these equations, the density is replaced for the new property to be calculated.…”

Section: Introductionmentioning

confidence: 99%

Pressure–Surface Tension–Temperature Equation of State for n-Alkanes

Mulero

Cachadiña

Cardona

et al. 2022

Ind. Eng. Chem. Res.

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Herein, the geometric similitude concept is applied to propose a cubic equation that relates surface tension, saturation pressure, and temperature for n -alkanes. The input properties for each fluid are the molecular mass, pressure, temperature, and compressibility factor at the critical point. The model is applied to temperatures below 0.93· T c (critical point temperature). A total of 2429 surface tension values have been selected for 32 n -alkanes. The parameters of the model have been obtained with a fit of the surface tension values for 19 pure n -alkanes that are randomly chosen. Then, it is tested for the other 13 pure n -alkanes and used to predict the surface tension for 11 binary and 4 ternary mixtures. These predictions are compared with the reported experimental data. For pure n -alkanes, the overall absolute average deviation is 2.4%, including the correlation and testing sets. No additional adjustable coefficients are used for mixtures, yielding an overall absolute average deviation of 2.98% for the binary systems and 7.97% for the ternary ones. The results show that the model is accurate enough for predictions and that the highest deviations are due to the lack of agreement in the values of surface tension of pure fluids obtained from different sources.

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Correlation of ionic liquid viscosity using Valderrama-Patel-Teja cubic equation of state and the geometric similitude concept. Part II: Binary mixtures of ionic liquids

Cited by 18 publications

References 33 publications

Machine‐Learning Approaches to Tune Descriptors and Predict the Viscosities of Ionic Liquids and Their Mixtures

Machine‐Learning Approaches to Tune Descriptors and Predict the Viscosities of Ionic Liquids and Their Mixtures

Modelo simple y generalizado para estimar la conductividad térmica de líquidos iónicos

Pressure–Surface Tension–Temperature Equation of State for n-Alkanes

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