Artificial Neural Network for Compositional Ionic Liquid Viscosity Prediction

DOI

10.1080/18756891.2012.696909How to use a DOI?

Keywords

artificial neural network, room temperature ionic liquids, viscosity, viscosity compositions

Abstract

Being a new generation of green solvents and high-tech reaction media of the future, ionic liquids have increasingly attracted much attention. Of particular interest in this context are room temperature ionic liquids (in short as ILs in this paper). Due to the relatively high viscosity, ILs is expected to be used in the form of solvent diluted mixture with reduced viscosity in industrial application, where predicting the viscosity of IL mixture has been an important research issue. Different IL mixture and many modelling approaches have been investigated. The objective of this study is to provide an alternative model approach using soft computing technique, i.e., artificial neural network (ANN) model, to predict the compositional viscosity of binary mixtures of ILs [C-mim][NTf] with =4, 6, 8, 10 in methanol and ethanol over the entire range of molar fraction at a broad range of temperatures from =293.0-328.0K. The results show that the proposed ANN model provides alternative way to predict compositional viscosity successfully with highly improved accuracy and also show its potential to be extensively utilized to predict compositional viscosity taking account of IL alkyl chain length, as well as temperature and compositions simultaneously, i.e., more complex intermolecular interactions between components in which it would be hard or impossible to establish the analytical model. This illustrates the potential application of ANN in the case that the physical and thermodynamic properties are highly non-linear or too complex.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Yiqing Miao
AU  - DavidW. Rooney
AU  - Quan Gan
PY  - 2012
DA  - 2012/06/01
TI  - Artificial Neural Network for Compositional Ionic Liquid Viscosity Prediction
JO  - International Journal of Computational Intelligence Systems
SP  - 460
EP  - 471
VL  - 5
IS  - 3
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2012.696909
DO  - 10.1080/18756891.2012.696909
ID  - Miao2012
ER  -