Journal of Industrial and Engineering Chemistry, Vol.21, 1350-1353, January, 2015
Viscosity estimation of binary mixtures of ionic liquids through a multi-layer perceptron model
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Ionic liquids (ILs) are chemical compounds which are being more and more employed due to their advantageous properties which are useful in many chemical and industrial fields. The study of the physicochemical properties of ILs and their mixtures is essential in order to improve their efficacy. One way to study these properties is by designing powerful mathematical estimative models. To do so, an artificial neural network multilayer perceptron (MLP) model has been proposed and developed to estimate the viscosity of four IL binary mixtures (1-hexyl-3-methylimidazolium tetrafluoroborate ([C6MIM,BF4]) + 1-ethyl-3-methylimidazolium tetrafluoroborate ([C2MIM,BF4]), [C6MIM,BF4] + 1-butyl-3-methylimidazolium tetrafluoroborate ([C4MIM,BF4]), [C4MIM,BF4] + 1-butyl-3-methylimidazolium methylsulfate ([C4MIM,MeSO4]), and [C4MIM,BF4] + 1-butyl-3-methylimidazolium hexafluorophosphate ([C4MIM,PF6])) inside the temperature range 298.15-308.15 K. The statistical results offered by the designed MLP model confirmed that it can be used to accurately estimate the viscosity of the tested IL mixtures (mean prediction errors around 1.5% for a k-fold cross-validation and about 2.3% for three blind tests). Additionally, it is possible to assess the purity level of the studied ILs or binary mixtures through
the estimated viscosity values.
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