Prediction the thermal conductivity of nanofluids has been subject of many researches. Artificial Neural Networks are used to obtain thermal conductivity of NAnofluids because not only this method is fast and acurate but also it can reduce the Lab costs. To predict the thermal conductivity of water- EG/MWCNT-Al₂O₃ hybrid nanofluid ($k_{nf}$) a feed-forward neural network with different neuron numbers has been tested and the best network based on the performance is selected. The Levenberg Marquardt algorithm is used for training the network, which is one of the best algorithms in machine learning. Also, using a fitting method, a surface is used to illustrate the behavior of nanofluids based on the volume fraction of nanoparticles ($\varphi$) and temperature (T). $\varphi =$0, 0.001, 0.002, 0.004, 0.008, 0.0016 and T = 25, 30, 35, 40, 45, 50 °C are used.. The obtained results show that the ANN and Fitting results are close to the experimental datapoints, and both methods can predict $k_{nf}$ accurately. As the results of these methods are very close, but the ANN method is better in predicting the behavior of this nanofluid.

预测纳米流体的热导率一直是许多研究的主题。人工神经网络用于获得纳米流体的热导率，因为这种方法不仅快速准确，而且可以降低实验室成本。预测水-EG/MWCNT-Al ₂ O ₃混合纳米流体的热导率（${克}_{nF}$) 测试了具有不同神经元数的前馈神经网络，并根据性能选择了最佳网络。Levenberg Marquardt 算法用于训练网络，它是机器学习中最好的算法之一。此外，使用拟合方法，基于纳米粒子的体积分数，使用表面来说明纳米流体的行为（$\phi$) 和温度 (T)。 $\phi =$0, 0.001, 0.002, 0.004, 0.008, 0.0016 and T = 25, 30, 35, 40, 45, 50 °C方法可以预测 ${克}_{nF}$准确。由于这些方法的结果非常接近，但 ANN 方法在预测这种纳米流体的行为方面更好。