The critical importance of developing solar energy technologies for sustainable power generation places a high priority on the crucial efforts to optimize the performance of parabolic trough solar collector (PTSC) systems. This study focuses on optimizing the volume frictions of the hybrid nanofluids (1% AlO− 2% MWCNT/Syltherm-800, 1.5% AlO− 1.5% MWCNT/Syltherm-800, and 2% AlO− 1% MWCNT/Syltherm-800) in order to attain the most effective performance of the PTSC system. A mathematical model to investigate the PTSC performance under various volume concentrations is developed. Furthermore, this study trained three machine learning models (Decision Tree, Support Vector Machine, and Artificial Neural Network) using the generated data from the developed mathematical model to predict the PTSC outlet temperature more quickly, with the goal of achieving higher accuracy with fewer inputs. The findings of this study indicate that the PTSC system exhibits higher thermal efficiency when utilizing a combination of 2% AlO and 1% MWCNT/Syltherm-800, with an average thermal efficiency of 70.54%. Moreover, the Artificial Neural Network (ANN) was the most accurate model out of the three. It performed remarkably well, showing an astounding R value of 99.99%, a mean absolute percentage error (MAPE) of 4.8x10, a root mean square error (RSME) of 0.012, and a mean absolute error (MAE) of 0.0057.

中文翻译：

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使用 Al2O3-MWCNT 作为混合纳米流体的抛物面槽式太阳能集热器的性能分析和机器学习算法

开发太阳能技术对于可持续发电至关重要，因此优化抛物面槽式太阳能集热器 (PTSC) 系统性能的关键工作是重中之重。本研究重点优化混合纳米流体（1% Al2O− 2% MWCNT/Syltherm-800、1.5% Al2O− 1.5% MWCNT/Syltherm-800 和 2% Al2O− 1% MWCNT/Syltherm-800）的体积摩擦以获得 PTSC 系统最有效的性能。开发了一个数学模型来研究不同体积浓度下的 PTSC 性能。此外，本研究使用开发的数学模型生成的数据训练了三种机器学习模型（决策树、支持向量机和人工神经网络），以更快地预测 PTSC 出口温度，目标是用更少的输入实现更高的精度。本研究结果表明，当使用 2% Al2O3 和 1% MWCNT/Syltherm-800 组合时，PTSC 系统表现出更高的热效率，平均热效率为 70.54%。此外，人工神经网络（ANN）是这三个模型中最准确的。它的表现非常出色，显示出令人震惊的 99.99% 的 R 值、4.8x10 的平均绝对百分比误差 (MAPE)、0.012 的均方根误差 (RSME) 和 0.0057 的平均绝对误差 (MAE)。