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Simultaneous Prediction of the Density, Viscosity and Electrical Conductivity of Pyridinium-Based Hydrophobic Ionic Liquids Using Artificial Neural Network

  • Ehsan Kianfar
  • Maryam Shirshahi
  • Farangis Kianfar
  • Farshid Kianfar
Original Paper

Abstract

An Artificial Neural Network (ANN) model is presented for the Simultaneous prediction of density, viscosity and electrical conductivity of pyridinium-based hydrophobic ionic liquids. Data density, viscosity and electrical conductivity obtained from paper and from a three layer feed forward artificial neural network is used to estimate them. The optimal ANN model consisted of, two neurons in the input layer, ten neurons in the hidden layer and three neurons in the output layer. This model predicts the density with a Mean Square Error (MSE) of 7.5714 × 10− 7 and the coefficient of determination (R2) of 1.0000, viscosity with a Mean Square Error (MSE) of 1.1332 × 10− 4 and the coefficient of determination (R2) of 0.9982 and electrical conductivity with a Mean Square Error (MSE) of 2.2668 × 10− 6 and the coefficient of determination (R2) of 0.9999. The results show that the Simultaneous predicted of density, viscosity and electrical conductivity of pyridinium-based hydrophobic ionic liquids by using artificial neural network well done. The artificial neural network model shows lower errors and higher precision compared to statistical models while use of ANN is easier and quicker than statistical methods.

Keywords

Density Viscosity Electrical conductivity Artificial neural network Ionic liquids 

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Notes

Glossary

k

Walden constant

M

Molar mass

S0

Standard molar entropy

UPOT

Lattice energy

ρ

Density

Electrical conductivity

η

Dynamic viscosity

α

Thermal expansion coefficients

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Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Chemical Engineering, Arak BranchIslamic Azad UniversityArakIran
  2. 2.Department of Civil Engineering, Arak BranchIslamic Azad UniversityArakIran
  3. 3.Department of Chemistry, Gachsaran BranchIslamic Azad UniversityGachsaranIran
  4. 4.Department of Chemical Engineering, North Tehran BranchIslamic Azad UniversityNorth TehranIran

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