Mushroom Drying in Air Heated Solar Collector Drying System and Modeling of Drying Performance with Artificial Neural Network

Abstract

In this study, an air heated solar collector (AHSC) dryer was designed to determine the drying characteristics of the mushroom. In the experiments thinly sliced mushroom samples were used. Collector inlet and outlet air temperatures, drying chamber inlet and outlet air temperatures, ambient temperature, radiation, air velocity and drying rate were considered as parameters affecting the drying feature. The results obtained were presented as a function of drying time. Moisture content (MC), moisture ratio (MR) and drying rate (DR) values obtained from the experiments were modeled with 3-layer artificial neural network (ANN) using Logsig Activation function and Backpropagation learning function. Mean square error (MSE) was used to determine of the statistical validity of the developed model. As a result, drying behavior of mushroom was successfully predicted by ANN for existing drying conditions.