This study aims to optimize a solar Photovoltaic (PV) and thermoelectric (TE) unit utilizing the Non-dominated Sorting Genetic Algorithm II (NSGA-II). The system incorporates a hybrid nanofluid jet, composed of water and ND-Co3O4 nanoparticles. Optimization, conducted in Python, utilizes data from an extensive 3D numerical model. Key factors under consideration include solar irradiation, the jet’s injection location, tube and jet inlet velocities, and the proportion of hybrid nanoparticles. The primary goals are to reduce pumping power (Ep), maximize the system’s overall gain over a 10-year span, and improve CO2 reduction. This research is significant for its comprehensive approach to enhancing solar energy technology, boosting system performance and efficiency, while addressing environmental concerns by lowering CO2 emissions. By combining advanced numerical simulations with NSGA-II optimization, this work advances sustainable energy solutions, providing valuable insights for the design of well-organized and environmentally friendly solar energy units. The optimization successfully balanced system gain, CO2 reduction, and pumping power, achieving optimal results of $12,508.8 for system gain, 431.59 tons for CO2 reduction, and 0.2097 for pumping power. The Mean Squared Error (MSE) percentages for the training data are under 1 % for system gain, approximately 1.6 % for CO2 reduction, and around 1.1 % for pumping power, underscoring the effectiveness of the optimization process.

中文翻译：

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优化配备混合纳米流体通道的光伏热电系统的效率：环境和经济考虑


本研究旨在利用非支配排序遗传算法 II （NSGA-II） 优化太阳能光伏 （PV） 和热电 （TE） 单元。该系统包含一个由水和 ND-Co3O4 纳米颗粒组成的混合纳米流体射流。在 Python 中进行的优化利用了来自大量 3D 数值模型的数据。考虑的关键因素包括太阳照射、射流的注射位置、管和射流入口速度以及混合纳米颗粒的比例。主要目标是降低泵送功率 （Ep），在 10 年内最大限度地提高系统的整体增益，并改善 CO2 减排。这项研究具有重要意义，因为它采用了增强太阳能技术、提高系统性能和效率的综合方法，同时通过降低二氧化碳排放来解决环境问题。通过将高级数值模拟与 NSGA-II 优化相结合，这项工作推进了可持续能源解决方案，为设计组织良好且环保的太阳能装置提供了有价值的见解。优化成功地平衡了系统增益、二氧化碳减排和泵送功率，实现了系统增益 12,508.8 美元、二氧化碳减排 431.59 吨和泵送功率 0.2097 吨的最佳结果。训练数据的均方误差 （MSE） 百分比低于系统增益的 1%，CO2 减少的均方误差 （MSE） 百分比约为 1.6%，泵送功率的均方误差 （MSE） 百分比约为 1.1%，这凸显了优化过程的有效性。