In the present study, the modified evacuated tube solar collector (METSC) with a bypass pipe utilizing copper oxide/distilled water (Cu₂O/DW) nanofluid is experimented. Then, the performance of METSC was predicted through Artificial Neural Networks (ANNs) techniques. The input variables were different volumes of the storage tank from 5 to 8 l, various diameters of the bypass pipe from 6 to 10 mm, and various volumetric concentration of the nanofluid from 0 to 0.04. Also, the output variables were the temperature difference of fluid in 1-h period and the energetic efficiency of METSC. The results demonstrated that the METSC performance was mostly impacted by the tank volume alteration. Moreover, the optimum bypass tube diameter value was obtained, and it was denoted that using the Cu₂O/DW nanofluid enhances the daily energy efficiency of METSC up to 4%. Furthermore, it was shown that both MLP and RBF techniques are two reliable algorithms to predict the thermal characteristics of an METSC. The maximum amounts of mean relative percentage error for MLP and RBF algorithms were reported as 0.576 and 0.907, respectively. Hence, two mathematical models were reported for formulating the output variables in terms of the input variables using the MLP technique.

在本研究中，采用带氧化铜/蒸馏水（Cu ₂ O / DW）纳米流体的旁路管对改进的真空管太阳能集热器（METSC）进行了实验。然后，通过人工神经网络（ANN）技术预测METSC的性能。输入变量是5到8 l的储罐容积，6到10 mm的旁路管直径以及0到0.04的纳米流体容积浓度。同样，输出变量是1小时内流体的温差和METSC的能量效率。结果表明，METSC性能主要受储罐容积变化的影响。此外，获得了最佳的旁通管直径值，并表示使用Cu ₂O / DW纳米流体可将METSC的每日能源效率提高多达4％。此外，结果表明，MLP和RBF技术都是预测METSC的热特性的两种可靠算法。据报道，MLP和RBF算法的最大平均相对百分比误差分别为0.576和0.907。因此，报告了两个数学模型，用于使用MLP技术根据输入变量来表示输出变量。