Abstract The properties of water (W)/ethylene glycol (EG) mixtures vary significantly with the proportion of EG and temperature, so it is suitable to use such fluids as exchange heat mediums in a waste heat system with temperature fluctuations. The experiments were conducted with 1.0 wt% Cu/Al2O3- EG/W hybrid nanofluids at temperatures ranging from 20 to 50 °C, where the base fluid (EG/W) mixture ratio was varied from 20:80 to 80:20. To search individuals which contain optimal weights and thresholds, a genetic algorithm (GA) and a mind evolutionary algorithm (MEA) coupled with a back-propagation neural network (GA-BPNN and MEA-BPNN, respectively) were used to improve the accuracy in the predicted thermal conductivity. The results show that the thermal conductivity increases nonlinearly with the ratio of water to ethylene glycol and temperature, due to the higher thermal conductivity of water and stronger collision frequency between molecular and nanoparticles. Binary Polynomial Regression (BPR) was fit with (coefficient of determination) R2 = 0.9984 as functions of temperature and mixture ratio. Comparisons of the prediction performance and capability of BPR, the performance of R2 increases by 0.11% and 0.13% for GA-BPNN and MEA-BPNN. It indicates that the combined BPNNs both predicate more accurately, particularly MEA-BPNN has the highest prediction accuracy.

中文翻译：

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基于人工神经网络（ANN）模型的余热系统混合纳米流体热导率预测模型的建立

摘要 水（W）/乙二醇（EG）混合物的性质随EG的比例和温度的变化而显着变化，因此适合在温度波动较大的余热系统中使用此类流体作为交换热介质。实验使用 1.0 wt% Cu/Al2O3-EG/W 混合纳米流体在 20 到 50°C 的温度范围内进行，其中基液 (EG/W) 混合比从 20:80 到 80:20 变化。为了搜索包含最佳权重和阈值的个体，使用遗传算法 (GA) 和思维进化算法 (MEA) 与反向传播神经网络（分别为 GA-BPNN 和 MEA-BPNN）相结合来提高在预测的热导率。结果表明，热导率随水乙二醇比和温度呈非线性增加，由于水的热导率更高，分子和纳米粒子之间的碰撞频率更高。二元多项式回归 (BPR) 与（决定系数）R2 = 0.9984 拟合为温度和混合比的函数。对比BPR的预测性能和能力，GA-BPNN和MEA-BPNN的R2性能分别提高了0.11%和0.13%。这表明组合的 BPNNs 都更准确地预测，尤其是 MEA-BPNN 的预测准确度最高。对于 GA-BPNN 和 MEA-BPNN，R2 的性能分别提高了 0.11% 和 0.13%。这表明组合的 BPNNs 都更准确地预测，尤其是 MEA-BPNN 的预测准确度最高。对于 GA-BPNN 和 MEA-BPNN，R2 的性能分别提高了 0.11% 和 0.13%。这表明组合的 BPNNs 都更准确地预测，尤其是 MEA-BPNN 的预测准确度最高。