DocumentCode :
1944372
Title :
Artificial neural network model to predict compositional viscosity over a broad range of temperatures
Author :
Miao, Yiqing ; Gan, Quan ; Rooney, David
Author_Institution :
Sch. of Chem. & Chem. Eng., Queen´´s Univ. Belfast, Belfast, UK
fYear :
2010
fDate :
15-16 Nov. 2010
Firstpage :
668
Lastpage :
673
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) [Cn-mim][NTf2] 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=293.0-328.0 K. 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.
Keywords :
chemistry computing; liquid mixtures; neural nets; organic compounds; physics computing; viscosity; ANN; artificial neural network; binary [Cη-mim] [NTf2]-methanol mixture; binary [Cn-mim] [NTf2]-ethanol mixture; binary mixtures; complex intermolecular interactions; compositional viscosity prediction; molar fraction; room temperature ionic liquids; temperature 293 K to 328 K; Artificial neural networks; Neurons; Predictive models; Temperature distribution; Temperature measurement; Temperature sensors; Viscosity; artificial neural network; room temperature ionic liquids; viscosity; viscosity compositions;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Systems and Knowledge Engineering (ISKE), 2010 International Conference on
Conference_Location :
Hangzhou
Print_ISBN :
978-1-4244-6791-4
Type :
conf
DOI :
10.1109/ISKE.2010.5680773
Filename :
5680773
Link To Document :
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