Artificial Neural Network Modelling of Electrical Conductivity in GNP-Al₂O₃ Hybrid Nanofluids

Abstract:

This study investigated the effects of mixing ratio and temperature on the electrical conductivity of a GNP-Al2O3 hybrid nanofluid. The results showed that an increase in the mixing ratio reduced the electrical conductivity ratio of the nanofluid, while an increase in temperature improved the electrical conductivity ratio. Additionally, an Artificial Neural Network (ANN) was used to predict the electrical conductivity of the nanofluid based on the mixing ratio and temperature. The optimal number of neurons in the hidden layer was found to be four neurons, with a low root mean square error (RMSE) value of 0.00696. The regression plot for the training, validation, and test data exhibited high correlation coefficients, indicating the reliability of the ANN model. These findings provide valuable insights into the behaviour of hybrid nanofluids and highlight the potential of using ANN for predicting their electrical conductivity.

• Periodical: Engineering Headway (Volume 2)
• Pages: 69-76
• DOI: https://doi.org/10.4028/p-Yd084f
• Online since: January 2024
• Authors: Adeola Borode*, Thato Tshephe, Peter Apata Olubambi
• Keywords: Alumina, Artificial Neural Network (ANN), Electrical Conductivity, Graphene Nanoplatelet, Hybrid Nanofluid, Mixing Ratio, Temperature

References

[1] J. Eastman, U. S. Choi, S. Li, L. J. Thompson and S. Lee, Enhanced Thermal Conductivity Through the Development of Nanofluids, MRS Proceedings vol. 457 (1996).

DOI: 10.1557/proc-457-3

[2] M. Osama, A. Singh, R. Walvekar, M. Khalid, T. C. S. M. Gupta and W. W. Yin, Recent developments and performance review of metal working fluids, Tribol. Int. 114 (2017) 389–401.

DOI: 10.1016/j.triboint.2017.04.050

[3] K. S. Suganthi and K. S. Rajan, Metal oxide nanofluids: Review of formulation, thermo-physical properties, mechanisms, and heat transfer performance, Renew. Sustain. Energy Rev. 76 (2017) 226–255.

DOI: 10.1016/j.rser.2017.03.043

[4] A. Borode, N. Ahmed and P. Olubambi, A review of solar collectors using carbon-based nanofluids, J. Clean. Prod. 241 (2019) 118311.

DOI: 10.1016/j.jclepro.2019.118311

[5] A. Ijam, R. Saidur, P. Ganesan and A. Moradi Golsheikh, Stability, thermo-physical properties, and electrical conductivity of graphene oxide-deionized water/ethylene glycol based nanofluid, Int. J. Heat Mass Transf. 87 (2015) 92–103.

DOI: 10.1016/j.ijheatmasstransfer.2015.02.060

[6] P. G. Kumar, V. Kumaresan and R. Velraj, Stability, viscosity, thermal conductivity, and electrical conductivity enhancement of multi-walled carbon nanotube nanofluid using gum arabic, https://doi.org/10.1080/1536383X.2017.1283615 25 (2017) 230–240.

DOI: 10.1080/1536383x.2017.1283615

[7] A.A. Minea and R.S. Luciu, Investigations on electrical conductivity of stabilized water based Al2O3 nanofluids, Microfluid. Nanofluidics 2012 136 13 (2012) 977–985.

DOI: 10.1007/s10404-012-1017-4

[8] A.O. Borode, N.A. Ahmed, P. A. Olubambi, M. Sharifpur and J. P. Meyer, Effect of Various Surfactants on the Viscosity, Thermal and Electrical Conductivity of Graphene Nanoplatelets Nanofluid, Int. J. Thermophys. 42 (2021) 158.

DOI: 10.1007/s10765-021-02914-w

[9] G. Huminic and A. Huminic, Hybrid nanofluids for heat transfer applications – A state-of-the-art review, Int. J. Heat Mass Transf. 125 (2018) 82–103.

DOI: 10.1016/j.ijheatmasstransfer.2018.04.059

[10] L. Yang, W. Ji, M. Mao and J. nan Huang, An updated review on the properties, fabrication and application of hybrid-nanofluids along with their environmental effects, J. Clean. Prod. 257 (2020) 120408.

DOI: 10.1016/j.jclepro.2020.120408

[11] A. O. Borode, N. A. Ahmed, P. A. Olubambi, M. Sharifpur and J. P. Meyer, Investigation of the Thermal Conductivity, Viscosity, and Thermal Performance of Graphene Nanoplatelet-Alumina Hybrid Nanofluid in a Differentially Heated Cavity , Frontiers in Energy Research vol. 9 (2021) 482.

DOI: 10.3389/fenrg.2021.737915

[12] S. O. Giwa, M. Sharifpur, M. H. Ahmadi, S. M. Sohel Murshed and J. P. Meyer, Experimental Investigation on Stability, Viscosity, and Electrical Conductivity of Water-Based Hybrid Nanofluid of MWCNT-Fe2O3, 11 (2021) 136.

DOI: 10.3390/nano11010136

[13] M. Amani, P. Amani, A. Kasaeian, O. Mahian, I. Pop and S. Wongwises, Modeling and optimization of thermal conductivity and viscosity of MnFe2O4 nanofluid under magnetic field using an ANN, Sci. Rep. 7 (2017).

DOI: 10.1038/s41598-017-17444-5

[14] M. Mehrali, E. Sadeghinezhad, S. Tahan Latibari, M. Mehrali, H. Togun, M. N. M. Zubir, S. N. Kazi and H.S.C. Metselaar, Preparation, characterization, viscosity, and thermal conductivity of nitrogen-doped graphene aqueous nanofluids, J. Mater. Sci. 49 (2014) 7156–7171.

DOI: 10.1007/s10853-014-8424-8

[15] E. E. Tkalya, M. Ghislandi, G. de With and C. E. Koning, The use of surfactants for dispersing carbon nanotubes and graphene to make conductive nanocomposites, Curr. Opin. Colloid Interface Sci. 17 (2012) 225–232.

DOI: 10.1016/j.cocis.2012.03.001

[16] R. Aghayari, H. Maddah, M. H. Ahmadi, W. M. Yan and N. Ghasemi, Measurement and Artificial Neural Network Modeling of Electrical Conductivity of CuO/Glycerol Nanofluids at Various Thermal and Concentration Conditions, Energies 2018, Vol. 11, Page 1190 11 (2018) 1190.

DOI: 10.3390/en11051190

[17] R. A. Mohamed, Modeling electrical properties of nanofluids using artificial neural network, Phys. Scr. 94 (2019) 105222.

DOI: 10.1088/1402-4896/ab1939