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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


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.


Density Viscosity Electrical conductivity Artificial neural network Ionic liquids 


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Walden constant


Molar mass


Standard molar entropy


Lattice energy



Electrical conductivity


Dynamic viscosity


Thermal expansion coefficients


  1. 1.
    Abbaspour A, Baramakeh L (2005) Simultaneous determination of antimony and bismuth by beta-correction spectrophotometry and an artificial neural network algorithm. Talanta 65:692CrossRefGoogle Scholar
  2. 2.
    Agrawal VK, Louis B, Khadikar PV (2010) Prediction of intrinsic solubility of generic drugs using MLR, ANN and SVM analyses. Eur J Med Chem 45:4018CrossRefGoogle Scholar
  3. 3.
    Akbari E, Buntat Z, Enzevaee A, Ebrahimi M, Yazdavar AH, Yusof R (2014) Analytical modeling and simulation of I–V characteristics in carbon nanotube based gas sensors using ANN and SVR methods. Chemom Intel Lab 137:173CrossRefGoogle Scholar
  4. 4.
    Balabin RM, Lomakina EI, Safieva RZ (2011) Neural network (ANN) approach to biodiesel analysis: analysis of biodiesel density, kinematic viscosity, methanol and water contents using near infrared (NIR) spectroscopy. Fuel 90:2007CrossRefGoogle Scholar
  5. 5.
    Bordbar MM, Khajehsharifi H, Solhjoo A (2015) PC-ANN assisted to the determination of Vanadium (IV) ion using an optical sensor based on immobilization of Eriochorome Cyanine R on a triacetylcellulose film. Spectrochim Acta A Mol Biomol Spectrosc 151:225CrossRefGoogle Scholar
  6. 6.
    Khajehsharifi H, Bordbar MM (2015) A highly selective chemosensor for detection and determination ofcyanide by using an indicator displacement assay and PC-ANN and itslogic gate behavior. Sensors Actuators 209:1015CrossRefGoogle Scholar
  7. 7.
    Ni Y, Xia Z, Kokot S (2011) A kinetic spectrophotometric method for simultaneous determination of phenol and its three derivatives with the aid of artificial neural network. J Hazard Mater 192:722CrossRefGoogle Scholar
  8. 8.
    Tenorio-Borroto E, Peñuelas Rivas CG, Vásquez Chagoyán JC, Castañedo N, Prado-Prado FJ, García-Mera X, González-Díaz H (2012) ANN multiplexing model of drugs effect on macrophages; theoretical and flow cytometry study on the cytotoxicity of the anti-microbial drug G1 in spleen. Bioorg Med Chem 20:6181CrossRefGoogle Scholar
  9. 9.
    Zamaniyan A, Joda F, Behroozsarand A, Ebrahimi H (2013) Application of artificial neural networks (ANN) for modeling of industrial hydrogen plant. Int J Hydrog Energy 38:6289CrossRefGoogle Scholar
  10. 10.
    Lazzari M, Mastragostino M, Pandolfo AG, Ruiz V, Soavi F (2011) J Electrochem Soc 158:A22–A25CrossRefGoogle Scholar
  11. 11.
    Rantwijk FV, Sheldon RA (2007) Chem Rev 107:2757–2785CrossRefGoogle Scholar
  12. 12.
    Greaves TL, Drummond CJ (2008) Chem Rev 108:206–237CrossRefGoogle Scholar
  13. 13.
    Hapiot P, Lagrost C (2008) Chem Rev 108:2238–2264CrossRefGoogle Scholar
  14. 14.
    Keskin S, Kayrak D, Akman U (2007) A review of ionic liquids towards supercritical fluid applications. J Supercrit Fluids 43:150CrossRefGoogle Scholar
  15. 15.
    Liu QS, Li PP, Welz-Biermann U, Chen J, Liu XX (2013) Density, dynamic viscosity, and electrical conductivity of pyridinium-based hydrophobic ionic liquids. J Chem Thermodyn 66:88CrossRefGoogle Scholar
  16. 16.
    Eliassi A, Modarress H, Mansoori A (1998) Density of poly ethylene glycol + water mixtures in the 298.15-328.15 K temperature rang. J Chem Eng Data 43(5):719–721CrossRefGoogle Scholar
  17. 17.
    Ensafi A, Khayamian T, Benvidi A, Mirmomtaz E (2006) Simultaneous determination of copper, lead and cadmium by cathodic adsorptive stripping voltammetry using artificial neural network. Anal Chem Acta 561:225CrossRefGoogle Scholar
  18. 18.
    Ensafi A, Khayamian T, Atabati M (2002) Simultaneous voltammetric determination of molybdenum and copper by adsorption cathodic differential pulse stripping method using a principal component artificial neural network. Talanta 59:792Google Scholar

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© 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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