Artificial intelligence vision technology application in sustainability evaluation of solar-driven distillation device

Abstract

The process of transforming brackish water into freshwater utilizing solar thermal energy is referred to as solar-driven distillation device, or solar still. Such device without fossil fuel provides significant environmental and health benefits by reducing air pollutants and remedying brackish water. The yield of purified water from conventional solar stills remains insufficient, prompting the necessity for research to enhance it by understanding the coupling effect between steam flow, heat transfer, and mass transfer. As such, computer-aided brackish water treatment comparison of the hemispherical solar still and other multi-slope solar stills is conducted in this paper, and an automatic steam&heat flow detection algorithm is developed to solve the problem of difficult data acquisition for gas-liquid transport processes. Firstly, double slope, four slope, and hemispherical solar stills are exposed to the sun in outdoor experiments, therefore the solar thermal performance of each still is analyzed through pairwise comparisons. Secondly, a large amount of experimental image data is input into neural network for training, and by continuously adjusting network parameters, the network can accurately recognize different types of images. Finally, the image to be recognized is input into the trained neural network, which outputs the category labels of the image to achieve automatic image recognition. Based on the data above, the best structure of solar-driven device is hemispherical structure, due to that the hemispherical solar still possesses the best performance in terms of distilled water yield, energy efficiency, exergy efficiency and energy payback.