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Atmospheric Water Generation (AWG) with thermoelectric cooling (TEC) is a potential solution for decentralized freshwater provision, especially for dry climates. However, forecasting performance of such a system under different operating and ambient conditions is a difficult problem due to the nonlinear relationship involved. The present study aims to put forward a data-driven modeling strategy with a deep neural network (DNN) to predict water condensation rate ( m wc ) and effectiveness of the system ( Eff ). A dataset derived from validated thermodynamic simulations with a refined input space was used for building and training the model, involving inputs of ambient temperature, relative humidity, voltage, time of the day, and monthly encoding. With a highly accurate performance, a trained DNN has R 2 of 0.982 for m wc and 0.975 for Eff and respective Root Mean Squared Errors of 3.12 g/h and 0.064 g/Wh for m wc Eff , respectively. Model generalization has been confirmed through comparative validation across all months with no indication of over-fitting. Additionally, the research has also shown that voltage optimization using composite scores is viable, and that dynamic voltage management has the potential to increase effectiveness by up to 115 % whilst compared to using a constant 12 V input. These results support the implementation of deep models for intelligent control and optimization of AWG equipment to provide for responsive actions to dynamically varying ambient conditions.
Ashour et al. (Thu,) studied this question.