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Journal of the Iranian Chemical Society

, Volume 16, Issue 12, pp 2629–2637 | Cite as

A comparative study in the prediction of thermal conductivity enhancement of nanofluids using ANN-MLP, ANN-RBF, ANFIS, and GMDH methods

  • Majid MoosaviEmail author
  • Khadijeh Firoozi Rad
  • Azadeh Daneshvar
Original Paper
First Online:
  • 39 Downloads

Abstract

In this work, four types of data mining methods, namely adaptive neuro-fuzzy inference system, artificial neural network—multilayer perceptron algorithm (ANN-MLP), artificial neural network—radial basis function algorithm (ANN-RBF), and group method of data handling (GMDH) have been used to predict the enhancement of the relative thermal conductivity of a wide range of nanofluids with different base fluids and nanoparticles. The total number of experimental data used in this work is 483 from 18 different nanofluids. The input parameters are thermal conductivity of base fluid and nanoparticles, volume fraction percent, the average size of nanoparticles, and temperature. Although the results showed that all four models are in relatively good agreement with experimental data, the ANFIS method is the best. The average absolute relative deviations (AARD%) between the experimental data and those of obtained using ANFIS, ANN-MLP, ANN-RBF, and GMDH methods were calculated as 2.7, 2.8, 4.2, and 4.3, respectively, for the test sets and as 1.1, 2.4, 3.9, and 4.5, respectively, for the training sets. Comparison between the predictions of the proposed ANN-MLP, ANN-RBF, ANFIS, and GMDH models and those predicted by traditional models, namely Maxwell and Bruggeman models showed that much better agreements can be obtained using the four models especially ANFIS model. Accordingly, the ANFIS method can able us to predict the relative thermal conductivity of new nanofluids in different conditions with good accuracy.

Keywords

Nanofluids Thermal conductivity Artificial neural network (ANN) Adaptive neuro-fuzzy inference system (ANFIS) Radial basis function (RBF) Group method of data handling (GMDH) 
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Notes

Acknowledgements

This research was supported by the Research Council of University of Isfahan. The authors also thank Dr. Vahid Moosavi for his assistance and valuable comments on the used algorithms.

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Copyright information

© Iranian Chemical Society 2019

Authors and Affiliations

  1. 1.Department of ChemistryUniversity of IsfahanIsfahanIran

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