Artificial neural network model to predict compositional viscosity over a broad range of temperatures

Yiqing Miao*, Quan Gan, David Rooney

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

The objective of this study is to provide an alternative model approach, i.e., artificial neural network (ANN) model, to predict the compositional viscosity of binary mixtures of room temperature ionic liquids (in short as ILs) [C n-mim] [NTf 2] with n=4, 6, 8, 10 in methanol and ethanol over the entire range of molar fraction at a broad range of temperatures from T=293.0328.0K. 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 over a wide range of temperatures and more complex viscosity compositions, i.e., more complex intermolecular interactions between components in which it would be hard or impossible to establish the analytical model.

Original languageEnglish
Title of host publicationProceedings of 2010 IEEE International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2010
Pages668-673
Number of pages6
DOIs
Publication statusPublished - 2010
Event2010 IEEE International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2010 - Hangzhou, China
Duration: 15 Nov 201016 Nov 2010

Conference

Conference2010 IEEE International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2010
CountryChina
CityHangzhou
Period15/11/201016/11/2010

Keywords

  • Artificial neural network
  • Room temperature ionic liquids
  • Viscosity
  • Viscosity compositions

ASJC Scopus subject areas

  • Artificial Intelligence
  • Information Systems

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