Since experimental studies in the field of nanofluid pool boiling requires costly and time-consuming tests, numerical methods such as artificial neural networks with higher predictability and nonlinear features are suitable for prediction and modeling of problem parameters. In this paper, 180 pool boiling laboratory data of Fe₃O₄/water nanofluid are employed as datasets used for network training to determine the effect of different parameters of nanofluid pool boiling on Boiling Heat Transfer Coefficient (BHTC) and wall superheat. The concerned input parameters for the neural network include concentration, roughness, and heat flux, while the network outputs are the BHTC and wall superheat. Finally, it becomes clear that the trainbr training algorithm with the optimal quantity of 41 neurons within the hidden layer shows the best performance. In addition, the present model can accurately predict the BHTC and wall superheat with correlation coefficients (R) of 0.99936 and 0.9986 and the mean square error (mse) of 0.103 and 0.013, respectively. Also, given the optimization objectives considered in this research, including maximizing the heat transfer coefficient and minimizing the wall superheat in the nanofluid pool boiling process, the multi-objective genetic algorithm has been used to optimize the two objective functions concerned.

由于纳米流体池沸腾领域的实验研究需要进行昂贵且耗时的测试，因此数值方法（例如具有较高可预测性和非线性特征的人工神经网络）适用于问题参数的预测和建模。本文研究了Fe ₃ O _4的180个池沸腾实验数据。将水/水纳米流体用作网络训练的数据集，以确定纳米流体池沸腾的不同参数对沸腾传热系数（BHTC）和壁过热的影响。神经网络的相关输入参数包括浓度，粗糙度和热通量，而网络输出为BHTC和壁过热。最后，很明显，在隐藏层中具有41个神经元的最佳数量的trainbr训练算法显示出最佳性能。此外，本模型可以准确地预测BHTC和壁过热，相关系数（R）为0.99936和0.9986，均方误差（mse）为0.103和0.013。同样，鉴于本研究中考虑的优化目标，