ABSTRACT The present study mainly focuses on enhancing the performance of solar still unit using solar energy through cylindrical parabolic collector and solar panels. A 300 W solar panel is used to heat saline water by thermal elements outside the solar still unit. Solar panels are cooled during the hot hours of the day; thus, reducing their temperature may lead to an increase in solar panel efficiency followed by an increase in the efficiency of the solar still unit. The maximum amount of freshwater used in the experiment was 2.132 kg/day. The experiments were modelled using ANNs. Based on neural network simulation results, there is a significant correlation between experimental data and neural network modelling. This paper compares experimental data with data obtained from mathematical modelling and ANNs. As a conclusion, the artificial neural network prediction has been more accurate than the simplified first principles model presented.

中文翻译：

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具有实验验证的太阳能海水淡化系统的第一原理与人工神经网络建模

摘要目前的研究主要集中在通过圆柱形抛物面集热器和太阳能电池板提高太阳能蒸馏器的性能。一个 300 W 的太阳能电池板用于通过太阳能蒸馏装置外部的热元件加热盐水。太阳能电池板在一天中的炎热时段冷却；因此，降低它们的温度可能会导致太阳能电池板效率的增加，随后太阳能蒸馏器单元的效率也会增加。实验中使用的最大淡水量为2.132公斤/天。实验是使用人工神经网络建模的。基于神经网络仿真结果，实验数据与神经网络建模之间存在显着相关性。本文将实验数据与从数学建模和人工神经网络获得的数据进行比较。作为结论，