Assessment of Viscosity of Coconut-Oil-Based CeO2/CuO Hybrid Nano-lubricant Using Artificial Neural Network

  • Ayamannil SajeebEmail author
  • Perikinalil Krishnan Rajendrakumar
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 949)


In the present work, coconut-oil-based hybrid nano-lubricants are prepared by dispersing CeO2 and CuO nanoparticles in three different proportions 75/25, 50/50, and 25/75. Experimental studies on the viscosity of hybrid nano-lubricants have been carried out by varying the concentration of combined nanoparticles in weight % from 0 to 1% and temperature ranging from 30 to 60 °C for each proportion of CeO2/CuO nanoparticles. A new empirical correlation and an optimal artificial neural network (ANN) for each proportion of CeO2/CuO nanoparticles in terms of temperature and concentration are devised to assess the viscosity ratio of hybrid nano-lubricant, using 48 experimental data. The results showed that the output of correlation and optimal ANN have a margin of deviation of 2 and 1%, respectively, and hence, the optimal artificial neural network is better in predicting the viscosity of hybrid nano-lubricant in comparison with empirical correlation.


ANN Hybrid nano-lubricant Viscosity Coconut oil Correlation 


  1. 1.
    Esfe, M.H., Saedodin, S., Yan, W.M., Afrand, M., Sina, N.: Study on thermal conductivity of water-based nanofluids with hybrid suspensions of CNTs/Al2O3 nanoparticles. J. Therm. Anal. Calorim. 124(1), 455–460 (2016)CrossRefGoogle Scholar
  2. 2.
    Soltanimehr, M., Afrand, M.: Thermal conductivity enhancement of COOH-functionalized MWCNTs/ethylene glycol–water nanofluid for application in heating and cooling systems. Appl. Therm. Eng. 105, 716–723 (2016). Scholar
  3. 3.
    Toghraie, D., Chaharsoghi, V.A., Afrand, M.: Measurement of thermal conductivity of ZnO–TiO2/EG hybrid nanofluid. J. Therm. Anal. Calorimetry 125(1), 527–535 (2016). Scholar
  4. 4.
    Esfe, M.H., Refahi, A.H., Teimouri, H., Noroozi, M., Afrand, M., Karimiopour, A.: Mixed convection fluid flow and heat transfer of the Al2O3—water nanofluid with variable properties in a cavity with an inside quadrilateral obstacle. Heat Transf. Res. 46(5) (2015)Google Scholar
  5. 5.
    Yiamsawas, T., Mahian, O., Dalkilic, A.S., Kaewnai, S., Wongwises, S.: Experimental studies on the viscosity of TiO2 and Al2O3 nanoparticles suspended in a mixture of ethylene glycol and water for high temperature applications. Appl. Energy 111, 40–45 (2013)CrossRefGoogle Scholar
  6. 6.
    Esfe, M.H., Saedodin, S.: An experimental investigation and new correlation of viscosity of ZnO–EG nanofluid at various temperatures and different solid volume fractions. Exp. Therm. Fluid Sci. 55, 1–5 (2014)CrossRefGoogle Scholar
  7. 7.
    Esfe, M.H., Saedodin, S., Mahian, O., Wongwises, S.: Thermophysical properties, heat transfer and pressure drop of COOH-functionalized multi walled carbon nanotubes/water nanofluids. Int. Commun. Heat Mass Transf. 58, 176–183 (2014)CrossRefGoogle Scholar
  8. 8.
    Baratpour, M., Karimipour, A., Afrand, M., Wongwises, S.: Effects of temperature and concentration on the viscosity of nanofluids made of single-wall carbon nanotubes in ethylene glycol. Int. Commun. Heat Mass Transf. 74, 108–113 (2016)CrossRefGoogle Scholar
  9. 9.
    Eshgarf, H., Afrand, M.: An experimental study on rheological behavior of non-Newtonian hybrid nano-coolant for application in cooling and heating systems. Exp. Therm. Fluid Sci. 76, 221–227 (2016)CrossRefGoogle Scholar
  10. 10.
    Sarkar, J., Ghosh, P., Adil, A.: A review on hybrid nanofluids: recent research, development and applications. Renew. Sustain. Energy Rev. 43, 164–177 (2015)CrossRefGoogle Scholar
  11. 11.
    Suresh, S., Venkitaraj, K.P., Selvakumar, P., Chandrasekar, M.: Effect of Al2O3–Cu/water hybrid nanofluid in heat transfer. Exp. Therm. Fluid Sci. 38, 54–60 (2012)CrossRefGoogle Scholar
  12. 12.
    Madhesh, D., Parameshwaran, R., Kalaiselvam, S.: Experimental investigation on convective heat transfer and rheological characteristics of Cu–TiO2 hybrid nanofluids. Exp. Thermal Fluid Sci. 52, 104–115 (2014)CrossRefGoogle Scholar
  13. 13.
    Esfe, M.H., Arani, A.A.A., Rezaie, M., Yan, W.M., Karimipour, A.: Experimental determination of thermal conductivity and dynamic viscosity of Ag–MgO/water hybrid nanofluid. Int. Commun. Heat Mass Transf. 66, 189–195 (2015)CrossRefGoogle Scholar
  14. 14.
    Munkhbayar, B., Tanshen, M.R., Jeoun, J., Chung, H., Jeong, H.: Surfactant-free dispersion of silver nanoparticles into MWCNT-aqueous nanofluids prepared by one-step technique and their thermal characteristics. Ceram. Int. 39(6), 6415–6425 (2013)CrossRefGoogle Scholar
  15. 15.
    Chen, L., Cheng, M., Yang, D., Yang, L.: Enhanced thermal conductivity of nanofluid by synergistic effect of multi-walled carbon nanotubes and Fe2O3 nanoparticles. Appl. Mech. Mater. 548–549, 118–123 (2014)Google Scholar
  16. 16.
    Esfe, M.H., Saedodin, S., Sina, N., Afrand, M., Rostami, S.: Designing an artificial neural network to predict thermal conductivity and dynamic viscosity of ferromagnetic nanofluid. Int. Commun. Heat Mass Transf. 68, 50–57 (2015)CrossRefGoogle Scholar
  17. 17.
    Afrand, M., Najafabadi, K.N., Sina, N., Safaei, M.R., Kherbeet, A.S., Wongwises, S., Dahari, M.: Prediction of dynamic viscosity of a hybrid nano-lubricant by an optimal artificial neural network. Int. Commun. Heat Mass Transf. 76, 209–214 (2016). Scholar
  18. 18.
    Sajeeb, A., Rajendrakumar, P.K.: Investigation on the rheological behavior of coconut oil based hybrid CeO2/CuO nanolubricants. Proc. Inst. Mech. Eng. Part J J. Eng. Tribol. 1–8, 1350650118772149 (2018). Scholar
  19. 19.
    Marquardt, D.W.: An algorithm for least-squares estimation of nonlinear parameters. J. Soc. Ind. Appl. Math. 11(2), 431–441 (1963)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Shakeri, S., Ghassemi, A., Hassani, M., Hajian, A.: Investigation of material removal rate and surface roughness in wire electrical discharge machining process for cementation alloy steel using artificial neural network. Int. J. Adv. Manuf. Technol. 82(1–4), 549–557 (2016). Scholar
  21. 21.
    Shirani, M., Akbari, A., Hassani, M.: Adsorption of cadmium (ii) and copper (ii) from soil and water samples onto a magnetic organozeolite modified with 2-(3, 4-dihydroxyphenyl)-1, 3-dithiane using an artificial neural network and analysed by flame atomic absorption spectrometry. Anal. Methods 7(14), 6012–6020 (2015)CrossRefGoogle Scholar
  22. 22.
    Vaferi, B., Samimi, F., Pakgohar, E., Mowla, D.: Artificial neural network approach for prediction of thermal behavior of nanofluids flowing through circular tubes. Powder Technol. 267, 1–10 (2014)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Ayamannil Sajeeb
    • 1
    Email author
  • Perikinalil Krishnan Rajendrakumar
    • 2
  1. 1.Government Engineering CollegeKozhikodeIndia
  2. 2.National Institute of TechnologyCalicutIndia

Personalised recommendations