This study investigated a novel method for increasing desalinated water mass in solar desalination plants. For this purpose, solar panels and a cylindrical parabolic collector (CPC) were used to raise basin water temperature. The effect of different components of basin solar still on freshwater mass was also investigated. The aluminum basin has been associated with maximum water desalination among the different materials constituting a basin. The effects of different colors (e.g. black, brown, and red) on the basin, as well as different water depths (5, 10, and 15 mm), were also explored. The highest amount of freshwater in the black aluminum basin at a 5-mm water depth was 2.97 kg/day. ANN modeling was employed to validate the experimental data, indicating good compliance of experimental data with ANN prediction. According to the results of the simulation with varying numbers of neurons (n = 2–25), the highest and lowest agreement between experimental data and ANN prediction data were related to 24 and 10 neurons, respectively. Under optimum conditions, R² and %AAD error were 0.993 and 2.654, respectively.

这项研究研究了一种增加太阳能淡化厂中淡化水质量的新方法。为此，太阳能电池板和圆柱形抛物线收集器（CPC）用于提高水盆水温。还研究了流域太阳能蒸馏器的不同成分对淡水质量的影响。铝盆与构成盆的不同材料之间最大程度的淡化海水有关。还探讨了不同颜色（例如黑色，棕色和红色）对盆地的影响以及不同水深（5、10和15毫米）的影响。黑色铝盆中5毫米水深处的最高淡水量为2.97千克/天。人工神经网络模型被用来验证实验数据，表明实验数据与人工神经网络预测的良好一致性。n = 2–25），实验数据和ANN预测数据之间的最高和最低一致性分别与24和10个神经元有关。在最佳条件下，R ²和％AAD误差分别为0.993和2.654。