Modeling of Drying and Drying Values of Apple Product with Solar Energy Assisted Drying System Using Artificial Neural Network

In this study, a solar energy assisted drying system was designed and the apples were dried. Dry base moisture content (MCd), removable moisture content (MR), drying rate (DR) and convective heat transfer coefficient (hc) values were calculated from the drying experiments. The drying performances of the apple drying process in the open air under the sun and the solar energy assisted drying system were compared. It has been achieved that the system-dried hand is more advantageous than under the sun in normal conditions. In the drying experiments, the hc values of the hands were calculated between 15.5 - 13.5 (W / m2 ° C) in the solar energy assisted drying system. For the hc values obtained from the experiment works done in the solar energy supported drying system, an predictive model was created by using artificial neural network (YSA). Estimated hC values are shown with the applied YSA. Mean squared error (MSE), root mean square error (RMSE) and relative absolute error (RAE) analyzes were performed to determine the validity of the predicted model obtained. As a result, a predictive model for hC values has been obtained and the solar energy assisted drying system has resulted in more efficient drying