Modeling electrical conductivity and stability of nanofluids using artificial neural network: Derivation of correlation dependent on mass ratio and temperature

Abstract

The lengthy preparation times and the laborious process of determining their thermophysical properties have directed nanofluid studies towards artificial intelligence-supported modeling efforts. Moreover, these modeling studies have primarily focused on thermal conductivity and viscosity. In this study, considering the often neglected aspects of stability and electrical conductivity in nanofluid research, an artificial neural network model was developed to predict the pH, electrical conductivity, and zeta potential of MgO/EG nanofluids within a mass ratio range of 0.1%-3% and a temperature range of 20°C-70°C. The MSE and R2 values of the developed model are 0.011118 and 0.99987, respectively, while the mean absolute percentage deviations (MAPD) for pH, electrical conductivity, and zeta potential are determined to be 0.11%, 0.78%, and 0.74%, respectively. These performance parameters revealed that the developed network is high-performance. Additionally, for the first time in the literature, a common correlation with different coefficients for these three properties was established using the model data. The new correlation has mean absolute percentage deviations of 0.35%, 2.08%, and 1.54% for pH, electrical conductivity, and zeta potential, respectively. These values, which reveal the absolute % error values with experimental data, reveal the high accuracy prediction ability of the new correlation.

Keywords

• Nanofluid
• Stability
• Zeta potential
• Electrical conductivity
• Artificial neural networks

Nanoakışkanların elektriksel iletkenlik ve stabilitelerinin yapay sinir ağları ile modellenmesi: Kütlesel oran ve sıcaklığa bağlı korelasyon türetilmesi

Öz

Hazırlanma sürelerinin uzun olması ve termofiziksel özelliklerinin belirlenmesinin zahmetli olması nanoakışkan çalışmalarını yapay zekâ destekli modelleme çalışmalarına yönlendirmiştir. Bununla birlikte yapılan modelleme çalışmaları ağırlıklı olarak ısıl iletkenlik ve viskozite üzerine yoğunlaşmıştır. Bu çalışmada, nanoakışkan çalışmalarında genellikle ihmal edilen stabilite ve elektriksel iletkenlik göz önüne alınarak, %0.1-%3 kütlesel oran ve 20°C-70°C sıcaklık aralığındaki MgO/EG nanoakışkanların pH, elektriksel iletkenlik ve zeta potansiyelini tahmin etmek için bir yapay sinir ağları modeli geliştirilmiştir. Geliştirilen modelin MSE ve R2 değeri sırasıyla 0.011118 ve 0.99987 iken, pH, elektriksel iletkenlik ve zeta potansiyeli için ortalama mutlak MoD değerleri ise sırasıyla %0.11, %0.78 ve %0.74 olarak belirlenmiştir. Bahsi geçen bu performans parametreleri geliştirilen ağın yüksek performanslı olduğunu ortaya koymuştur. Ayrıca model verileri kullanılarak literatürde ilk defa bu üç özellik için de geçerli, katsayıları birbirinden farklı ortak bir korelasyon ortaya konulmuştur. Yeni korelasyonun pH, elektriksel iletkenlik ve zeta potansiyeli için ortalama mutlak MoD değerleri sırasıyla %0.35, %2.08 ve %1.54’tür. Deneysel veriler ile mutlak % hata değerlerini ortaya çıkaran bu değerler yeni korelasyonun yüksek doğrulukta tahmin yeteneğini ortaya koymaktadır.

Anahtar Kelimeler

• Nanoakışkan
• Stabilite
• Zeta potansiyeli
• Elektriksel iletkenlik
• Yapay sinir ağları

Kaynakça

1. S.U. Choi and J.A. Eastman, Enhancing thermal conductivity of fluids with nanoparticles. Argonne National Lab.(ANL), Argonne, IL (United States), 1995.
2. T.-K. Hong, H.-S. Yang and C. Choi, Study of the enhanced thermal conductivity of Fe nanofluids, Journal of Applied Physics, 97, 064311, 2005. https://doi.org/10.1063/1.1861145
3. M. Abareshi, E.K. Goharshadi, S.M. Zebarjad, H.K. Fadafan and A. Youssefi, Fabrication, characterization and measurement of thermal conductivity of Fe3O4 nanofluids. Journal of Magnetism and Magnetic Materials, 322, 3895-3901, 2010. https://doi.org/10.1016/j.jmmm.2010.08.016
4. H. Zhu, C. Zhang, S. Liu, Y. Tang and Y. Yin, Effects of nanoparticle clustering and alignment on thermal conductivities of Fe3O4 aqueous nanofluids. Applied Physics Letters, 89, 023123, 2006. https://doi.org/10.1063/1.2221905
5. A. Karimi, S.S.S. Afghahi, H. Shariatmadar and M. Ashjaee, Experimental investigation on thermal conductivity of MFe2O4 (M= Fe and Co) magnetic nanofluids under influence of magnetic field. Thermochimica Acta, 598, 59-67, 2014. https://doi.org/10.1016/j.tca.2014.10.022
6. L.S. Sundar, M.K. Singh and A.C. Sousa, Enhanced heat transfer and friction factor of MWCNT–Fe3O4/water hybrid nanofluids. International Communications in Heat and Mass Transfer, 52, 73-83, 2014.https://doi.org/10.1016/j.icheatmasstransfer.2014.01.012
7. Q. Li, Y. Xuan and J. Wang, Experimental investigations on transport properties of magnetic fluids. Experimental Thermal and Fluid Science, 30, 109-116, 2005. https://doi.org/10.1016/j.expthermflusci.2005.03.021
8. L.S. Sundar, E.V. Ramana, M. Singh and A. De Sousa, Viscosity of low volume concentrations of magnetic Fe3O4 nanoparticles dispersed in ethylene glycol and water mixture. Chemical physics letters, 554, 236-242, 2012. https://doi.org/10.1016/j.cplett.2012.10.042

9. M. Afrand, D. Toghraie and N. Sina, Experimental study on thermal conductivity of water-based Fe3O4 nanofluid: development of a new correlation and modeled by artificial neural network. International Communications in Heat and Mass Transfer, 75, 262-269, 2016. https://doi.org/10.1016/j.icheatmasstransfer.2016.04.023
10. M.M. Papari, F. Yousefi, J. Moghadasi, H. Karimi and A. Campo, Modeling thermal conductivity augmentation of nanofluids using diffusion neural networks. International journal of thermal sciences, 50, 44-52, 2011. https://doi.org/10.1016/j.ijthermalsci.2010.09.006
11. M. Hojjat, S.G. Etemad, R. Bagheri and J. Thibault, Thermal conductivity of non-Newtonian nanofluids: experimental data and modeling using neural network. International Journal of Heat and Mass Transfer, 54, 1017-1023, 2011. https://doi.org/10.1016/j.ijheatmasstransfer.2010.11.039
12. A.Y. Bhat and A. Qayoum, Viscosity of CuO nanofluids: experimental investigation and modelling with FFBP-ANN. Thermochimica Acta, 714, 179267, 2022. https://doi.org/10.1016/j.tca.2022.179267
13. X. Yang, A. Boroomandpour, S. Wen, D. Toghraie and F. Soltani, Applying Artificial Neural Networks (ANNs) for prediction of the thermal characteristics of water/ethylene glycol-based mono, binary and ternary nanofluids containing MWCNTs, titania, and zinc oxide. Powder Technology, 388, 418-424, 2021. https://doi.org/10.1016/j.powtec.2021.04.093
14. F. Sahin and O. Genc, Experimentally determining the thermal properties of NiFe2O4 magnetic nanofluid under suitable stability conditions: Proposal the new correlation for thermophysical properties. Powder Technology, 427, 118706, 2023. https://doi.org/10.1016/j.powtec.2023.118706
15. A. Ghadimi and I.H. Metselaar, The influence of surfactant and ultrasonic processing on improvement of stability, thermal conductivity and viscosity of titania nanofluid. Experimental Thermal and Fluid Science, 51, 1-9, 2013. https://doi.org/10.1016/j.expthermflusci.2013.06.001
16. P.K. Das, N. Islam, A.K. Santra and R. Ganguly, Experimental investigation of thermophysical properties of Al2O3–water nanofluid: Role of surfactants. Journal of Molecular Liquids 237, 304-312, 2017. https://doi.org/10.1016/j.molliq.2017.04.099
17. G. Xia, H. Jiang, R. Liu and Y. Zhai, Effects of surfactant on the stability and thermal conductivity of Al2O3/de-ionized water nanofluids. International Journal of Thermal Sciences, 84, 118-124, 2014. https://doi.org/10.1016/j.ijthermalsci.2014.05.004
18. S. Umar, F. Sulaiman, N. Abdullah and S.N. Mohamad, Investigation of the effect of pH adjustment on the stability of nanofluid. AIP conference proceedings: AIP Publishing, 2018. https://doi.org/10.1063/1.5066987
19. H. Zhang, S. Qing, Y. Zhai, X. Zhang and A. Zhang, The changes induced by pH in TiO2/water nanofluids: Stability, thermophysical properties and thermal performance. Powder technology, 377, 748-759, 2021. https://doi.org/10.1016/j.powtec.2020.09.004
20. K. Cacua, F. Ordoñez, C. Zapata, B. Herrera, E. Pabón and R. Buitrago-Sierra, Surfactant concentration and pH effects on the zeta potential values of alumina nanofluids to inspect stability. Colloids and Surfaces A: Physicochemical and Engineering Aspects, 583, 123960, 2019. https://doi.org/10.1016/j.colsurfa.2019.123960
21. X. Wei and L. Wang, Synthesis and thermal conductivity of microfluidic copper nanofluids. Particuology, 8, 262-271, 2010. https://doi.org/10.1016/j.partic.2010.03.001
22. X. Li, D. Zhu and X. Wang, Evaluation on dispersion behavior of the aqueous copper nano-suspensions. Journal of colloid and interface science, 310, 456-463, 2007. https://doi.org/10.1016/j.jcis.2007.02.067
23. S.U. Ilyas, R. Pendyala and N. Marneni, Settling characteristics of alumina nanoparticles in ethanol-water mixtures. Applied Mechanics and Materials, 372, 143-148, 2013.https://doi.org/10.4028/www.scientif ic.net/AMM.372.143
24. F. Sahin, O. Genc, M. Gökcek and A.B. Çolak, An experimental and new study on thermal conductivity and zeta potential of Fe3O4/water nanofluid: Machine learning modeling and proposing a new correlation. Powder Technology, 420, 118388, 2023. https://doi.org/10.1016/j.powtec.2023.118388
25. K. Suganthi and K. Rajan, Temperature induced changes in ZnO–water nanofluid: zeta potential, size distribution and viscosity profiles. International Journal of Heat and Mass Transfer, 55, 7969-7980, 2012. https://doi.org/10.1016/j.ijheatmasstransfer.2012.08.032
26. V. Singh, A. Kumar, M. Alam, A. Kumar, P. Kumar and V. Goyat, A study of morphology, UV measurements and zeta potential of zinc ferrite and Al2O3 nanofluids. Materials Today: Proceedings, 59, 1034-1039, 2022. https://doi.org/10.1016/j.matpr.2022.02.371
27. A.K. Singh and V.S. Raykar, Microwave synthesis of silver nanofluids with polyvinylpyrrolidone (PVP) and their transport properties. Colloid and Polymer Science, 286, 1667-1673, 2008. https://doi.org/10.1007/s00396-008-1932-9
28. K. Lee, Y. Hwang, S. Cheong, L. Kwon, S. Kim and J. Lee, Performance evaluation of nano-lubricants of fullerene nanoparticles in refrigeration mineral oil. Current Applied Physics, 9, e128-e131, 2009. https://doi.org/10.1016/j.cap.2008.12.054
29. X.-j. Wang and D.-s. Zhu, Investigation of pH and SDBS on enhancement of thermal conductivity in nanofluids. Chemical Physics Letters, 470, 107-111, 2009. https://doi.org/10.1016/j.cplett.2009.01.035
30. N. Ali, J.A. Teixeira and A. Addali, A review on nanofluids: fabrication, stability, and thermophysical properties. Journal of Nanomaterials, 2018. https://doi.org/10.1155/2018/6978130
31. P.I. Soares, C.A. Laia, A. Carvalho, L.C. Pereira, J.T. Coutinho, I.M. Ferreira, C.M. Novo and J.P. Borges, Iron oxide nanoparticles stabilized with a bilayer of oleic acid for magnetic hyperthermia and MRI applications. Applied Surface Science, 383, 240-247, 2016. https://doi.org/10.1016/j.apsusc.2016.04.181
32. S. Ganguly, S. Sikdar and S. Basu, Experimental investigation of the effective electrical conductivity of aluminum oxide nanofluids. Powder Technology, 196, 326-330, 2009. https://doi.org/10.1016/j.powtec.20 09.08.010
33. L.B. Modesto-Lopez and P. Biswas, Role of the effective electrical conductivity of nanosuspensions in the generation of TiO2 agglomerates with electrospray. Journal of Aerosol Science, 41, 790-804, 2010. https://doi.org/10.1016/j.jaerosci.2010.04.010
34. I. Zakaria, W. Mohamed, W. Azmi, A. Mamat, R. Mamat and W. Daud, Thermo-electrical performance of PEM fuel cell using Al2O3 nanofluids. International Journal of Heat and Mass Transfer, 119, 460-471, 2018. https://doi.org/10.1016/j.ijheatmasstransfer.2017.11.137
35. I. Zakaria, W. Azmi, A. Mamat, R. Mamat, R. Saidur, S.A. Talib and W. Mohamed, Thermal analysis of Al2O3–water ethylene glycol mixture nanofluid for single PEM fuel cell cooling plate: an experimental study. International Journal of Hydrogen Energy, 41, 5096-5112, 2016. https://doi.org/10.1016/j.ijhydene.2016.01.041
36. I. Zakaria, W. Mohamed, N. Azid, M. Suhaimi and W. Azmi, Heat transfer and electrical discharge of hybrid nanofluid coolants in a fuel cell cooling channel application. Applied Thermal Engineering, 210, 118369, 2022. https://doi.org/10.1016/j.appltherma leng.2022.118369
37. G.A. Longo, C. Zilio, E. Ceseracciu and M. Reggiani, Application of artificial neural network (ANN) for the prediction of thermal conductivity of oxide–water nanofluids. Nano Energy, 1, 290-296, 2012. https://doi.org/10.1016/j.applthermaleng.2022.118369
38. M. Hemmat Esfe, S. Saedodin, M. Bahiraei, D. Toghraie, O. Mahian and S. Wongwises, Thermal conductivity modeling of MgO/EG nanofluids using experimental data and artificial neural network. Journal of Thermal Analysis and Calorimetry, 118, 287-294, 2014. https://doi.org/10.1007/s10973-014-4002-1
39. S.A. Adio, M. Sharifpur and J.P. Meyer, Factors affecting the pH and electrical conductivity of MgO–ethylene glycol nanofluids. Bulletin of Materials Science, 38, 1345-1357, 2015. https://doi.org/10.1007/s12034-015-1020-y
40. G.L. Fan, A.S. El-Shafay, S.A. Eftekhari, M. Hekmatifar, D. Toghraie, A.S. Mohammed and A. Khan, A well-trained artificial neural network (ANN) using the trainlm algorithm for predicting the rheological behavior of water - Ethylene glycol/WO - MWCNTs nanofluid. International Communications in Heat and Mass Transfer, 131, 105857, 2022. https://doi.org/10.1016/j.icheatmasstransfer.2021.105857