Measurement of the thermal conductivity of MWCNT-CuO/water hybrid nanofluid using artificial neural networks (ANNs)
Authors: Rostami, Sara
Toghraie, Davood
Shabani, Behrooz
Sina, Nima
Barnoon, Pouya
Journal: Journal of Thermal Analysis and Calorimetry (2021)
Discipline: CHEMISTRY
Keywords: artificial neural networks; thermal conductivity; multi-walled carbon nanotubes; CuO/water nanofluid; prediction

Abstract: In this paper, artificial neural networks (ANNs) are developed to predict the thermal conductivity ( $$k_{\text{nf}}$$ ) of multi-walled carbon nanotubes (MWCNTs)-CuO/water nanofluid. After generating experimental data points, an algorithm is proposed to find the optimum ANN regarding the best performance. Three different states including ANN, experimental, and fitting method have been evaluated, and their errors in $$k_{\text{nf}}$$ prediction have been investigated. Regarding the obtained results, it can be seen that the best and worst neuron numbers are 8 and 31, respectively. Then, using curve fitting method, the behavior of nanofluid is predicted by a surface equation with third order. Finally, the ANN results and fitting results have been compared. Finally, it is found that the ability of the ANN to predict the $$k_{\text{nf}}$$ is greater. It was also found that the ANN has better performance and correlation and thus less error in the predicted data. On the other hand, comparing methods in predicting the $$k_{\text{nf}}$$ is an important issue. The use of ANNs in predicting the $$k_{\text{nf}}$$ as a new approach can lead to a great contribution in determining the most desirable performance and achieving the best and most accurate state. In addition, mean squared error (MSE) has obtained 2.4451e\u221205 for fitting method. According to the experimental data, it can be seen that in φ\u2009=\u20090.6% and T\u2009=\u200950&nbsp;°C, an increase of more than 30.38% has occurred in the $$k_{\text{nf}}$$ compared to the ambient temperature.

Retracted: No

PubPeer comments:
 1. Fig. 1 of this paper is of concern: ![file] It cites Saeedi AH, Akbari M, Toghraie D. An experimental study on rheological behavior of a nanofuid containing oxide nanoparticle and proposing a new correlation. Physica E. 2018;99:285u201393 (DOI: 10.1016 j.physe.2018.02.018), which is obviously irrelevant as that paper seems to investigate ethylene glycol cerium oxide (!!!) nanofluid, while this paper investigates water copper oxide nanofluid. Instead, the figure is strikingly similar to Fig. 5 from Zadkhast, M., Toghraie, D., Karimipour, A. (2017). Developing a new correlation to estimate the thermal conductivity of MWCNT-CuO water hybrid nanofluid via an experimental investigation. Journal of Thermal Analysis and Calorimetry, 129(2), 859-867 (DOI: 10.1007 s10973-017-6213-8), which is not on the list of references. ![file] 2. The claim below is interesting. ![file] However, I could not find any hard data across the article to support this particular claim. , 
 The article also claims that **42** data points were used for either of test validation training. However, Fig. 1 reports only **36** data points. Also, it is quite hard to take 15% of 42 data points, because 6 points make 14.3%, while 7 points make 16.7%. How are the rounding rules applied here? , 
 And then there is a paper Karimipour, Aliakbar, et al.  Thermal conductivity enhancement via synthesis produces a new hybrid mixture composed of copper oxide and multi-walled carbon nanotube dispersed in water: experimental characterization and artificial neural network modeling. International Journal of Thermophysics 41.8 (2020): 1-27. (DOI: 10.1007 s10765-020-02702-y) from somewhat closely related group of authors. Despite somewhat similar setup (water + 1:1 CuO MWCNT) it reports much lower conductivity values. Why the proposed fits the results of that paper so poorly? ![file] And then there is a paper Karimipour, Arash, et al.  A novel nonlinear regression model of SVR as a substitute for ANN to predict conductivity of MWCNT-CuO water hybrid nanofluid based on empirical data. Physica A: Statistical Mechanics and its Applications 521 (2019): 89-97 (DOI: 10.1016 j.physa.2019.01.055) also from a somewhat related group of authors. It compares SVR to ANN with the same description as in this paper. And - surprisingly! - the comparison is very unfavorable to ANN. Why wasn t this issue addressed at all? ,