A hybrid study of a 4-stage compressed solar distiller based on experimental, computational and deep learning methods

Abstract

In the present study, a hybrid technique is employed to demonstrate the thermal performance of a 4-stage compressed solar distiller with hydrophilic evaporators (CSDHE) regarding the role of natural convection in the air gap domain. Initially, the test is conducted under simulated solar radiation of 2.13 W·m−2 over 5400 s. A thermodynamic model incorporating diffusion is developed for the next step to analyze internal transient temperatures. In the third step, a 3D single-phase natural convection model of the CSDHE is simulated to achieve the research objectives. Based on experimental, thermal modeling, and computational results, a dual deep neural network is developed to predict the thermal behavior of the system and the variation of convection heat transfer coefficient for different air gap thicknesses over 10,000 s. The experimental results revealed a distilled water production rate of 3.806 kg·m−2h−1 with a gained output ratio (GOR) of 112 %. Moreover, the findings demonstrate that conduction accounts for 84.23 % of total heat transfer, while convection accounts for 6.36 % during 5400 s. We also found that, by employing a convolutional neural network (CNN) and directly harvesting data from pre-defined contours, the hybrid training dataset for deep forward neural network (DFNN) is trained 28.35 % faster than conventional methods.