الفهرس 
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Abstract
This work presents on the ongoing development of a solar thermal driven, modular tubular solar still integrated with film cooling and parabolic concentrators desalination. The objective was to develop a system which is completely driven by solar energy which is low maintenance and easy to operate. First case study the effect of changing the levels of saline water filling inside the solar evaporation collector on freshwater production, efficiency and cost. To develop and evaluate the effectiveness of a new solar powered desalination technology, raising degree temperature water of parabolic tube, raising the condensation rate of water desalination. Increase the Efficiency of the system, ow cost of production, developing a theoretical model for the concept to simulate the performance of the unit. using the model to design a pilot scale system based on the concept, conducting an experimental study of the water distillation system, and compare the experimental results with the theoretical ones, and Conducting an economic analysis of the system. In this study, different basin water depth (0.5., 1, 2, and 3 cm) were investigated to obtain the best water depth. Furthermore, trying various water cover cooling flow rate (1, 2, 3, and 4 L/h) to get the optimal watercooling flow rate. The experimental results show that the TSS with lower water depth gives the best performance, where the productivity was reached to 4.5 L/m2 , while with the large water depth at 3 cm give a daily productively of 3 L/m2 . Also, the best cooling water flow rate was obtained at 2 L/h. Under the best conditions corresponding to 0.5 cm water depth and 2 L/h cooling water flow rate, the maximum efficiency was achieved, which was about 54.9%. In comparison with TSS without cooling, the proposed TSS successfully improved the yield and efficiency of the TSS by about 31.4% and 32.6%, respectively. Moreover, the daily thermal exergy efficiency< increased by about 9%. from an economic point of view, cost per liter of clean water output was found to be 0.023 $ and 0.019 $ for TSS without and with cover cooling, respectively. The second study, accurate and convenient prediction models of tubular solar still performance, expressed as hourly production, were developed by utilizing machine learning. Based on experimental data recorded in Egypt climate, three models were generated and compared; namely: classical artificial neural network, random forest, and traditional multilinear regression. For hyper parameters adjustment, both artificial neural network and random forest models were optimized by Bayesian optimization algorithm. The experimental results revealed that the average accumulated productivity was 4.3 L/(m2day). In addition, before applying Bayesian optimization algorithm, both random forest and artificial neural network predict hourly production effectively, but the superiority was for random forest well behaved with insignificant error. The prediction performance of random forest, artificial neural network and multilinear regression were calculated as 0.9758, 0.9614, 0.9267 for determination coefficients, and 5.21%, 7.697%, 10.911% for mean absolute percentage error, respectively. Additionally, via Bayesian optimization algorithm for searching most appropriate hyper parameters, the performance of artificial neural network was significantly improved by 35%. Moreover, optimization findings revealed that random forest was less sensitive to hyper parameters than artificial neural network. Based on the robustness performance and high accuracy, random forest is recommended in predicting productivity of tubular solar still.