article · International Journal of Environmental Science and Technology
Modifying wick solar stills and forecasting freshwater yield can be achieved through combined hardware design and computational modelling. Replacing the steel basin of a conventional wick solar still with a copper basin in a developed design enhances freshwater productivity by approximately 50 percent. To accurately forecast hourly water production, an artificial neural network was enhanced using the tree-seed algorithm to identify optimal neuron weights. Real operational data collected from both the conventional and developed still configurations were used to train and test the predictive models. When compared against experimental results, the integrated model demonstrated superior accuracy over a standard neural network across several statistical metrics, confirming its effectiveness in monitoring and predicting solar still output.
Solar desalination provides a renewable method for producing drinking water, but system output can be variable and difficult to forecast. Demonstrating a 50 percent yield boost through basin material selection directly improves clean water output. Concurrently, deploying improved prediction algorithms allows operators to accurately assess hourly performance, supporting the broader adoption and reliability of small-scale solar water treatment systems.
This work applies directly to solar water purification equipment, targeting water technology developers and communities requiring decentralised desalination. The copper basin enhancement represents an applied and tested hardware modification ready for manufacturing evaluation, while the prediction model is at an experimental stage, tested on real operating data to support future performance-monitoring software.
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Abstract This study introduces a modified artificial neural network (ANN) model based on the tree–seed algorithm (ANN-TSA) to predict the freshwater yield of conventional and developed wick solar stills. The proposed method depends on improving the performance of the ANN through finding the optimal weights of the neurons (elementary units in an ANN) using the TSA. The use of developed wick solar still (DWSS) with copper basin results in increasing the freshwater productivity by about 50% compared with that of conventional wick solar still (CWSS) with steel basin. Then, the proposed ANN-TSA method is utilized to predict the hourly productivity (HP) of CWSS with steel basin and DWSS with copper basin. The real recorded data of the system were used to train the developed models. The predicted HP results of the CWSS and DWSS using ANN-TSA as well as ANN were compared with the experimental results obtained. The present study proves that ANN-TSA can be used as an effective tool to predict the HP of the CWSS and DWSS better than the ANN based on different statistical criteria ( R 2 , RMSE, MRE, and MAE).
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DOI: 10.1007/s13762-022-04414-2
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