The solar distillation process utilizes the abundantly available solar energy to separate pure water from the contaminants. The process takes place in a device called the solar distillation still (SDS). The thermal performance delivered by the SDS mainly depends on the distillate formation rate inside the basin. The distillate formation inside the SDS depends on its basin temperature (BT), basin water temperature (BWT), glass cover inside temperature (GCIT) and glass cover outside temperature (GCOT). The thermal performance delivered by the SDS is non-linear and fluctuating. The variation in thermal performance is mainly due to the sudden changes in ambient conditions (solar irradiance and wind speed). The fluctuation in performance demands a forecast model with higher prediction accuracy to monitor the deviations in the system performance. In this study, a fuzzy inference system (FIS) is proposed for predicting the thermal performance delivered by the SDS. The real-time experimental results are used to train and test the model. The FIS proposed in this study is simple, robust, stable and effective in comparison with the available quantitative models. The newly proposed FIS is capable of predicting the performance of SDS with an accuracy of 94.5%.

太阳蒸馏过程利用大量可用的太阳能将纯净水与污染物分离。该过程在称为太阳能蒸馏釜（SDS）的设备中进行。SDS提供的热力性能主要取决于盆地内部的馏出物形成速率。SDS内部的馏出物形成取决于其水盆温度（BT），水盆水温（BWT），玻璃罩内部温度（GCIT）和玻璃罩外部温度（GCOT）。SDS提供的热性能是非线性的并且波动的。热性能的变化主要归因于周围环境条件的突然变化（太阳辐照度和风速）。性能的波动要求具有更高预测精度的预测模型来监视系统性能的偏差。在这项研究中，提出了一种模糊推理系统（FIS）来预测SDS传递的热性能。实时实验结果用于训练和测试模型。与现有的定量模型相比，本研究提出的FIS简单，稳健，稳定且有效。新提出的FIS能够以94.5％的精度预测SDS的性能。