Estimation of viscosity of binary mixtures of ionic liquids and solvents using an artificial neural network based on the structure groups of the ionic liquid

The applications of ionic liquids solve a lot of major problems regarding green energy production and environment. Ionic liquids are solvents used as alternative to unfriendly traditional and hazardous solvents which reduces the negative impact to environment to a great extent. This study produced models to predict two of the basic physical properties of binary ionic liquid and ketone mixtures: density and speed of sound. The artificial neural network algorithm was used to predict these properties by varying the temperature, mole fraction, atom count in cation, methyl group count in cation, atom count in anion, hydrogen atom count in anion of ionic liquid and atom count in ketone. Total experimental data points of 2517 for density and 947 for speed of sound were used to train the algorithm and to test the network obtained. The optimum neural network structure determined for density and speed of sound of binary ionic liquid and ketone mixtures were 7-9-9-1 and 7-7-4-1 respectively; overall average percentage error of 2.45% and 2.17% respectively; and mean absolute error of 28.21 kg/m3 and 33.91 m/s respectively. The said algorithm was found applicable for the prediction of density and speed of sound of binary ionic liquid and ketone mixtures.

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Correlation of density for binary mixtures of methanol+ionic liquids using back propagation artificial neural network

Korean Journal of Chemical Engineering ◽

10.1007/s11814-012-0112-2 ◽

2013 ◽

Vol 30 (1) ◽

pp. 213-220 ◽

Cited By ~ 29

Author(s):

Mostafa Lashkarbolooki ◽

Ali Zeinolabedini Hezave ◽

Aziz Babapoor

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Ionic Liquids ◽

Binary Mixtures ◽

Back Propagation ◽

Artificial Neural

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Prediction of Density of Binary Mixtures of Ionic Liquids with Alcohols (Methanol/Ethanol/1-Propanol) using Artificial Neural Network

ASEAN Journal of Chemical Engineering ◽

10.22146/ajche.49685 ◽

2016 ◽

Vol 15 (2) ◽

pp. 33

Author(s):

Karen Faith P. Ornedo Ramos ◽

Carla Angela M. Muriel ◽

Adonis P Adornado ◽

Allan N Soriano ◽

Vergel C Bungay

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Ionic Liquids ◽

Binary Mixtures ◽

Network Architecture ◽

Binary Systems ◽

Chemical Properties ◽

Hydrogen Atoms ◽

Mathematical Correlation ◽

Artificial Neural

Ionic liquids demonstrated successful potential applications in the industry most specifically as the new generation of solvents for catalysis and synthesis in chemical processes, thus knowledge of their physico-chemical properties is of great advantage. The present work presents a mathematical correlation that predicts density of binary mixtures of ionic liquids with various alcohols (ethanol/methanol/1-propanol). The artificial neural network algorithm was used to predict these properties based on the variations in temperature, mole fraction, number of carbon atoms in the cation, number of atoms in the anion, number of hydrogen atoms in the anion and number of carbon atoms in the alcohol. The data used for the calculations were taken from ILThermo Database. Total experimental data points of 1946 for the considered binaries were used to train the algorithm and to test the network obtained. The best neural network architecture determined was found to be 6-6-10-1 with a mean absolute error of 48.74 kg/m3. The resulting correlation satisfactorily represents the considered binary systems and can be used accurately for solvent related calculations requiring properties of these systems.

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