Skip to main content
Log in

A hybrid genetic–BP algorithm approach for thermal conductivity modeling of nanofluid containing silver nanoparticles coated with PVP

  • Published:
Journal of Thermal Analysis and Calorimetry Aims and scope Submit manuscript

Abstract

Since nanoparticles play a significant role in increasing the thermal conductivity of fluids, the present study aims to predict the thermal conductivity of silver nanofluid coated with polyvinylpyrrolidone (PVP) by the combinational model of multilayer perceptron artificial neural network and genetic algorithm. For modeling, the results of experimental measurements have been used for thermal conductivity of nanofluid containing PVP-coated silver nanoparticle-based deionized water at 25–55 °C in volume fraction of 250 ppm, 500 ppm and 1000 ppm. Henceforth, genetic algorithm is applied to improve learning process in the artificial neural network. It is in this way that the masses were chosen for each neuron’s communications as well as their bias happens according to optimization performed by the genetic algorithm. To evaluate the accuracy of the model in predicting thermal conductivity of nanofluid, mean absolute percentage error, root mean square error, coefficient of determination (R2) and mean bias error have exerted indices which are 1.202, 0.345, 0.989 and − 0.016, respectively. The results of the indices and predictions, compared to the experimental results, show high accuracy and reliable combinational model of artificial neural network and genetic algorithm.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

References

  1. Said Z, Arora S, Bellos E. A review on performance and environmental effects of conventional and nanofluid-based thermal photovoltaics. Renew Sustain Energy Rev. 2018;94:302–16.

    Article  CAS  Google Scholar 

  2. Saffarian MR, Moravej M, Doranehgard MH. Heat transfer enhancement in a flat plate solar collector with different flow path shapes using nanofluid. Renew Energy. 2020;146:2316–29.

    Article  CAS  Google Scholar 

  3. Ilyas SU, Narahari M, Theng JTY, Pendyala R. Experimental evaluation of dispersion behavior, rheology and thermal analysis of functionalized zinc oxide-paraffin oil nanofluids. J Mol Liq. 2019;294:111613.

    Article  CAS  Google Scholar 

  4. Das SK, Choi SU, Yu W, Pradeep T. Nanofluids: science and technology. Hoboken: Wiley; 2007.

    Book  Google Scholar 

  5. Choi SUS, Li S, Eastman JA. Measuring thermal conductivity of fluids containing oxide nanoparticles. J Heat Transf. 1999;121:280–9.

    Article  Google Scholar 

  6. Eastman JA, Choi SUS, Li S, Yu W, Thompson LJ. Anomalously increased effective thermal conductivities of ethylene glycol-based nanofluids containing copper nanoparticles. Appl Phys Lett. 2001;78:718–20.

    Article  CAS  Google Scholar 

  7. Taherialekouhi R, Rasouli S, Khosravi A. An experimental study on stability and thermal conductivity of water-graphene oxide/aluminum oxide nanoparticles as a cooling hybrid nanofluid. Int J Heat Mass Transf. 2019;145:118751.

    Article  CAS  Google Scholar 

  8. Abbas N, Awan MB, Amer M, Ammar SM, Sajjad U, Ali HM, et al. Applications of nanofluids in photovoltaic thermal systems: a review of recent advances. Phys A Stat Mech Appl. 2019;536:122513.

    Article  CAS  Google Scholar 

  9. Bojdi MK, Behbahani M, Sahragard A, Amin BG, Fakhari A, Bagheri A. A palladium imprinted polymer for highly selective and sensitive electrochemical determination of ultra-trace of palladium ions. Electrochim Acta. 2014;149:108–16.

    Article  CAS  Google Scholar 

  10. Sedghi R, Heidari B, Behbahani M. Synthesis, characterization and application of poly(acrylamide-co-methylenbisacrylamide) nanocomposite as a colorimetric chemosensor for visual detection of trace levels of Hg and Pb ions. J Hazard Mater. 2015;285:109–16.

    Article  CAS  PubMed  Google Scholar 

  11. Wole-Osho I, Okonkwo EC, Adun H, Kavaz D, Abbasoglu S. An intelligent approach to predicting the effect of nanoparticle mixture ratio, concentration and temperature on thermal conductivity of hybrid nanofluids. J Therm Anal Calorim. 2020. https://doi.org/10.1007/s10973-020-09594-y.

    Article  Google Scholar 

  12. Mahyari M, Shaabani A, Behbahani M, Bagheri A. Thiol-functionalized fructose-derived nanoporous carbon as a support for gold nanoparticles and its application for aerobic oxidation of alcohols in water. Appl Organomet Chem. 2014;28:576–83.

    Article  CAS  Google Scholar 

  13. Omidi F, Behbahani M, Kalate Bojdi M, Shahtaheri SJ. Solid phase extraction and trace monitoring of cadmium ions in environmental water and food samples based on modified magnetic nanoporous silica. J Magn Magn Mater. 2015;395:213–20.

    Article  CAS  Google Scholar 

  14. Shafiey Dehaj M, Zamani Mohiabadi M. Experimental study of water-based CuO nanofluid flow in heat pipe solar collector. J Therm Anal Calorim. 2019;137:2061–72.

    Article  CAS  Google Scholar 

  15. Walshe J, Amarandei G, Ahmed H, McCormack S, Doran J. Development of poly-vinyl alcohol stabilized silver nanofluids for solar thermal applications. Sol Energy Mater Sol Cells. 2019;201:110085.

    Article  CAS  Google Scholar 

  16. Shi L, Hu Y, He Y. Magnetocontrollable convective heat transfer of nanofluid through a straight tube. Appl Therm Eng. 2019;162:114220.

    Article  CAS  Google Scholar 

  17. Goel N, Taylor RA, Otanicar T. A review of nanofluid-based direct absorption solar collectors: design considerations and experiments with hybrid PV/Thermal and direct steam generation collectors. Renew Energy. 2020;145:903–13.

    Article  CAS  Google Scholar 

  18. Parashar N, Aslfattahi N, Yahya SM, Saidur R. An artificial neural network approach for the prediction of dynamic viscosity of MXene-palm oil nanofluid using experimental data. J Therm Anal Calorim. 2020. https://doi.org/10.1007/s10973-020-09638-3.

    Article  Google Scholar 

  19. Dadhich M, Prajapati OS, Rohatgi N. Flow boiling heat transfer analysis of Al2O3 and TiO2 nanofluids in horizontal tube using artificial neural network (ANN). J Therm Anal Calorim. 2020;139:3197–217.

    Article  CAS  Google Scholar 

  20. Pourrajab R, Noghrehabadi A, Behbahani M, Hajidavalloo E. An efficient enhancement in thermal conductivity of water-based hybrid nanofluid containing MWCNTs-COOH and Ag nanoparticles: experimental study. J Therm Anal Calorim. 2020. https://doi.org/10.1007/s10973-020-09300-y.

    Article  Google Scholar 

  21. Pourrajab R, Noghrehabadi A, Hajidavalloo E, Behbahani M. Investigation of thermal conductivity of a new hybrid nanofluids based on mesoporous silica modified with copper nanoparticles: synthesis, characterization and experimental study. J Mol Liq. 2020;300:112337.

    Article  CAS  Google Scholar 

  22. Naphon P, Wiriyasart S, Arisariyawong T, Nakharintr L. ANN, numerical and experimental analysis on the jet impingement nanofluids flow and heat transfer characteristics in the micro-channel heat sink. Int J Heat Mass Transf. 2019;131:329–40.

    Article  CAS  Google Scholar 

  23. Safaei MR, Hajizadeh A, Afrand M, Qi C, Yarmand H, Zulkifli NWBM. Evaluating the effect of temperature and concentration on the thermal conductivity of ZnO-TiO2/EG hybrid nanofluid using artificial neural network and curve fitting on experimental data. Phys A Stat Mech Appl. 2019;519:209–16.

    Article  CAS  Google Scholar 

  24. Shahsavar A, Khanmohammadi S, Toghraie D, Salihepour H. Experimental investigation and develop ANNs by introducing the suitable architectures and training algorithms supported by sensitivity analysis: measure thermal conductivity and viscosity for liquid paraffin based nanofluid containing Al2O3 nanoparticles. J Mol Liq. 2019;276:850–60.

    Article  CAS  Google Scholar 

  25. Hemmat Esfe M, Afrand M. Predicting thermophysical properties and flow characteristics of nanofluids using intelligent methods: focusing on ANN methods. J Therm Anal Calorim. 2020;140:501–25.

    Article  CAS  Google Scholar 

  26. Shahsavar A, Bahiraei M. Experimental investigation and modeling of thermal conductivity and viscosity for non-Newtonian hybrid nanofluid containing coated CNT/Fe3O4 nanoparticles. Powder Technol. 2017;318:441–50.

    Article  CAS  Google Scholar 

  27. Amani P, Vajravelu K. Intelligent modeling of rheological and thermophysical properties of green covalently functionalized graphene nanofluids containing nanoplatelets. Int J Heat Mass Transf. 2018;120:95–105.

    Article  CAS  Google Scholar 

  28. Amani M, Amani P, Bahiraei M, Wongwises S. Prediction of hydrothermal behavior of a non-Newtonian nanofluid in a square channel by modeling of thermophysical properties using neural network. J Therm Anal Calorim. 2019;135:901–10.

    Article  CAS  Google Scholar 

  29. Nasirzadehroshenin F, Maddah H, Sakhaeinia H, Pourmozafari A. Investigation of exergy of double-pipe heat exchanger using synthesized hybrid nanofluid developed by modeling. Int J Thermophys. 2019;40:1–24.

    Article  CAS  Google Scholar 

  30. Ahmadi MH, Mohseni-Gharyehsafa B, Ghazvini M, Goodarzi M, Jilte RD, Kumar R. Comparing various machine learning approaches in modeling the dynamic viscosity of CuO/water nanofluid. J Therm Anal Calorim. 2020;139:2585–99.

    Article  CAS  Google Scholar 

  31. Ahmadi MH, Baghban A, Ghazvini M, Hadipoor M, Ghasempour R, Nazemzadegan MR. An insight into the prediction of TiO2/water nanofluid viscosity through intelligence schemes. J Therm Anal Calorim. 2020;139:2381–94.

    Article  CAS  Google Scholar 

  32. Mirsaeidi AM, Yousefi F. Viscosity, thermal conductivity and density of carbon quantum dots nanofluids: an experimental investigation and development of new correlation function and ANN modeling. J Therm Anal Calorim. 2019. https://doi.org/10.1007/s10973-019-09138-z.

    Article  Google Scholar 

  33. Rostami S, Toghraie D, Esfahani MA, Hekmatifar M, Sina N. Predict the thermal conductivity of SiO2/water–ethylene glycol (50:50) hybrid nanofluid using artificial neural network. J Therm Anal Calorim. 2020. https://doi.org/10.1007/s10973-020-09426-z.

    Article  Google Scholar 

  34. Komeilibirjandi A, Raffiee AH, Maleki A, Alhuyi Nazari M, Safdari Shadloo M. Thermal conductivity prediction of nanofluids containing CuO nanoparticles by using correlation and artificial neural network. J Therm Anal Calorim. 2020;139:2679–89.

    Article  CAS  Google Scholar 

  35. Maleki A, Elahi M, Assad MEH, Alhuyi Nazari M, Safdari Shadloo M, Nabipour N. Thermal conductivity modeling of nanofluids with ZnO particles by using approaches based on artificial neural network and MARS. J Therm Anal Calorim. 2020. https://doi.org/10.1007/s10973-020-09373-9.

    Article  Google Scholar 

  36. Rostami S, Toghraie D, Shabani B, Sina N, Barnoon P. Measurement of the thermal conductivity of MWCNT-CuO/water hybrid nanofluid using artificial neural networks (ANNs). Netherlands: J Therm Anal Calorim. Springer; 2020. p. 1–9.

    Google Scholar 

  37. Hemmat Esfe M, Wongwises S, Naderi A, Asadi A, Safaei MR, Rostamian H, et al. Thermal conductivity of Cu/TiO2-water/EG hybrid nanofluid: experimental data and modeling using artificial neural network and correlation. Int Commun Heat Mass Transf. 2015;66:100–4.

    Article  CAS  Google Scholar 

  38. Hemmat Esfe M, Afrand M, Yan WM, Akbari M. Applicability of artificial neural network and nonlinear regression to predict thermal conductivity modeling of Al2O3-water nanofluids using experimental data. Int Commun Heat Mass Transf. 2015;66:246–9.

    Article  CAS  Google Scholar 

  39. Hemmat Esfe M, Esfandeh S, Saedodin S, Rostamian H. Experimental evaluation, sensitivity analyzation and ANN modeling of thermal conductivity of ZnO-MWCNT/EG-water hybrid nanofluid for engineering applications. Appl Therm Eng. 2017;125:673–85.

    Article  CAS  Google Scholar 

  40. Hemmat Esfe M, Alirezaie A, Rejvani M. An applicable study on the thermal conductivity of SWCNT-MgO hybrid nanofluid and price-performance analysis for energy management. Appl Therm Eng. 2017;111:1202–10.

    Article  CAS  Google Scholar 

  41. Hemmat Esfe M, Behbahani PM, Arani AAA, Sarlak MR. Thermal conductivity enhancement of SiO2–MWCNT (85:15%)–EG hybrid nanofluids: ANN designing, experimental investigation, cost performance and sensitivity analysis. J Therm Anal Calorim. 2017;128:249–58.

    Article  CAS  Google Scholar 

  42. Hemmat Esfe M, Abbasian Arani AA, Firouzi M. Empirical study and model development of thermal conductivity improvement and assessment of cost and sensitivity of EG-water based SWCNT-ZnO (30%:70%) hybrid nanofluid. J Mol Liq. 2017;244:252–61.

    Article  CAS  Google Scholar 

  43. Hemmat Esfe M, Goodarzi M, Reiszadeh M, Afrand M. Evaluation of MWCNTs-ZnO/5W50 nanolubricant by design of an artificial neural network for predicting viscosity and its optimization. J Mol Liq. 2019;277:921–31.

    Article  CAS  Google Scholar 

  44. Khosrojerdi S, Vakili M, Yahyaei M, Kalhor K. Thermal conductivity modeling of graphene nanoplatelets/deionized water nanofluid by MLP neural network and theoretical modeling using experimental results. Int Commun Heat Mass Transf. 2016;74:11–7.

    Article  CAS  Google Scholar 

  45. Tahani M, Vakili M, Khosrojerdi S. Experimental evaluation and ANN modeling of thermal conductivity of graphene oxide nanoplatelets/deionized water nanofluid. Int Commun Heat Mass Transf. 2016;76:358–65.

    Article  CAS  Google Scholar 

  46. Vafaei M, Afrand M, Sina N, Kalbasi R, Sourani F, Teimouri H. Evaluation of thermal conductivity of MgO-MWCNTs/EG hybrid nanofluids based on experimental data by selecting optimal artificial neural networks. Phys E Low Dimens Syst Nanostruct. 2017;85:90–6.

    Article  CAS  Google Scholar 

  47. Afrand M, Hemmat Esfe M, Abedini E, Teimouri H. Predicting the effects of magnesium oxide nanoparticles and temperature on the thermal conductivity of water using artificial neural network and experimental data. Phys E Low Dimens Syst Nanostruct. 2017;87:242–7.

    Article  CAS  Google Scholar 

  48. Vakili M, Karami M, Delfani S, Khosrojerdi S, Kalhor K. Experimental investigation and modeling of thermal conductivity of CuO–water/EG nanofluid by FFBP-ANN and multiple regressions. J Therm Anal Calorim. 2017;129:629–37.

    Article  CAS  Google Scholar 

  49. Kavitha R, Kumar PC. A comparison between MLP and SVR models in prediction of thermal properties of nano fluids. J Appl Fluid Mech. 2018;11:7–14.

    Google Scholar 

  50. Ahmadi MH, Tatar A, Seifaddini P, Ghazvini M, Ghasempour R, Sheremet MA. Thermal conductivity and dynamic viscosity modeling of Fe2O3/water nanofluid by applying various connectionist approaches. Numer Heat Transf Part A Appl. 2018;74:1301–22.

    Article  CAS  Google Scholar 

  51. Alrashed AAAA, Gharibdousti MS, Goodarzi M, de Oliveira LR, Safaei MR, Bandarra Filho EP. Effects on thermophysical properties of carbon based nanofluids: experimental data, modelling using regression, ANFIS and ANN. Int J Heat Mass Transf. 2018;125:920–32.

    Article  CAS  Google Scholar 

  52. Agarwal R, Verma K, Agrawal NK, Singh R. Comparison of experimental measurements of thermal conductivity of Fe2O3 nanofluids against standard theoretical models and artificial neural network approach. J Mater Eng Perform. 2019;28:4602–9.

    Article  CAS  Google Scholar 

  53. Fogel David B. What is evolutionary computation? IEEE Spectr. 2000;37(2):26–8.

    Article  Google Scholar 

  54. Karimi H, Yousefi F. Application of artificial neural network-genetic algorithm (ANN-GA) to correlation of density in nanofluids. Fluid Phase Equilib. 2012;336:79–83.

    Article  CAS  Google Scholar 

  55. Karimi H, Yousefi F, Rahimi MR. Correlation of viscosity in nanofluids using genetic algorithm-neural network (GA-NN). Heat Mass Transf Stoffuebertragung. 2011;47:1417–25.

    Article  CAS  Google Scholar 

  56. Ramezanizadeh M, Ahmadi MA, Ahmadi MH, Alhuyi Nazari M. Rigorous smart model for predicting dynamic viscosity of Al2O3/water nanofluid. J Therm Anal Calorim. 2019;137:307–16.

    Article  CAS  Google Scholar 

  57. Ahmadi MH, Ahmadi MA, Nazari MA, Mahian O, Ghasempour R. A proposed model to predict thermal conductivity ratio of Al2O3/EG nanofluid by applying least squares support vector machine (LSSVM) and genetic algorithm as a connectionist approach. J Therm Anal Calorim. 2019;135:271–81.

    Article  CAS  Google Scholar 

  58. Hemmat Esfe M, Hajmohammad MH, Sina N, Afrand M. Optimization of thermophysical properties of Al2O3/water-EG (80:20) nanofluids by NSGA-II. Phys E Low Dimens Syst Nanostruct. 2018;103:264–72.

    Article  CAS  Google Scholar 

  59. Amani M, Amani P, Kasaeian A, Mahian O, Pop I, Wongwises S. Modeling and optimization of thermal conductivity and viscosity of MnFe2O4 nanofluid under magnetic field using an ANN. Sci Rep. 2017;7:1–13.

    Article  CAS  Google Scholar 

  60. Amani M, Amani P, Mahian O, Estellé P. Multi-objective optimization of thermophysical properties of eco-friendly organic nanofluids. J Clean Prod. 2017;166:350–9.

    Article  CAS  Google Scholar 

  61. Mirabdolah Lavasani A, Khosrojerdi S, Delfani S, Vakili M. Experimental study based graphene oxide nanoplatelets nanofluid used in domestic application on the performance of direct absorption solar water heaters with indirect circulation systems. AUT J Mech Eng. 2018;3:43–52.

    Google Scholar 

  62. Vakili M, Hosseinalipour SM, Delfani S, Khosrojerdi S. Photothermal properties of graphene nanoplatelets nanofluid for low-temperature direct absorption solar collectors. Sol Energy Mater Sol Cells. 2016;152:187–91.

    Article  CAS  Google Scholar 

  63. Pak BC, Cho YI. Hydrodynamic and heat transfer study of dispersed fluids with submicron metallic oxide particles. Exp Heat Transf. 1998;11:151–70.

    Article  CAS  Google Scholar 

  64. Bagherzadeh SA, Sulgani MT, Nikkhah V, Bahrami M, Karimipour A, Jiang Y. Minimize pressure drop and maximize heat transfer coefficient by the new proposed multi-objective optimization/statistical model composed of “ANN + genetic algorithm” based on empirical data of CuO/paraffin nanofluid in a pipe. Phys A Stat Mech Appl. 2019;527:121056.

    Article  CAS  Google Scholar 

  65. Ebrahimi-Moghadam A, Moghadam AJ. Optimal design of geometrical parameters and flow characteristics for Al2O3/water nanofluid inside corrugated heat exchangers by using entropy generation minimization and genetic algorithm methods. Appl Therm Eng. 2019;149:889–98.

    Article  CAS  Google Scholar 

  66. Man K, Tang K, Kwong S. Genetic algorithms: concept and design. Berlin: Springer; 1999.

    Book  Google Scholar 

  67. Vakili M, Karami M, Delfani S, Khosrojerdi S. Experimental investigation and modeling of thermal radiative properties of f-CNTs nanofluid by artificial neural network with Levenberg–Marquardt algorithm. Int Commun Heat Mass Transf. 2016;78:224–30.

    Article  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to M. Vakili.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Paknezhad, B., Vakili, M., Bozorgi, M. et al. A hybrid genetic–BP algorithm approach for thermal conductivity modeling of nanofluid containing silver nanoparticles coated with PVP. J Therm Anal Calorim 146, 17–30 (2021). https://doi.org/10.1007/s10973-020-09989-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10973-020-09989-x

Keywords

Navigation