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Received July 7, 2010
Accepted December 7, 2010
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This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/bync/3.0) which permits
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Viscosity of ionic liquids using the concept of mass connectivity and artificial neural networks
1Dept. of Mech. Eng., Faculty of Engineering, University of La Serena, Casilla 554, La Serena, Chile 2Centro de Informaci´on Tecnol´ogica, Mons. Subercaseaux 667, La Serena, Chile 3Dept. of Chemistry, Faculty of Sciences, University of La Serena, Casilla 554, La Serena, Chile
jvalderr@userena.cl
Korean Journal of Chemical Engineering, June 2011, 28(6), 1451-1457(7), 10.1007/s11814-010-0512-0
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Abstract
Artificial neural networks (ANN) and the concept of mass connectivity index are used to correlate and predict the viscosity of ionic liquids. Different topologies of a multilayer feed forward artificial neural network were studied and the optimum architecture was determined. Viscosity data at several temperatures taken from the literature for 58 ionic liquids with 327 data points were used for training the network. To discriminate among the different substances, the molecular mass of the anion and of the cation, the mass connectivity index and the density at 298 K were considered as the independent variables. The capabilities of the designed network were tested by predicting viscosities for situations not considered during the training process (31 viscosity data for 26 ionic liquids). The results demonstrate that the chosen network and the variables considered allow estimating the viscosity of ionic liquids with acceptable accuracy for engineering calculations. The program codes and the necessary input files to calculate the viscosity for other ionic liquids are provided.
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Valderrama JO, Rojas RE, Fluid Phase Equilib., 297(1), 107 (2010)
Valderrama JO, Reategui A, Rojas RE, Ind. Eng. Chem. Res., 48(6), 3254 (2009)
