Productivity Enhancement of Solar Still using Adaptive Deep Neural Network Model

Abstract

Water is considered as one among the most superabundant resources on the earth that cover 75% of the entire earth’s surface yet numerous countries faces problem due to water shortage. Desalination is considered as the most efficient process to overcome this raising clean water demand. The solar energy is considered as one of the efficient and finest resources to refine the brackish water. Therefore, this paper proposes a novel black widow particle swarm optimization based deep neural network approach to enhance the water productivity from the solar still. The main intension of the proposed BWPSO based DNN approach is to enhance the performances of DNN by employing BWPSO for optimal water production. Here, the optimal weight of the DNN is determined by utilizing BWPSO algorithm. The solar still is incorporated with a straight tube and spiral tube solar water collector. In addition to this, the study based on solar still and their experimental analysis are carried out in Coimbatore city located in Tamilnadu. The evaluation is conducted for various parameters namely glass temperature, average evaporation temperature, inlet and outlet temperature, water temperature, air temperature, yield, solar intensity, wind velocity, RMSE, MAE, MRE and EC to determine the effectiveness of the system. Also, the comparative analysis is made and the evaluation results reveal that the proposed approach outperforms various other approaches.