Modeling of a convective-infrared kiwifruit drying process

This paper aims to evaluate the experimental performance of a convective-infrared system with heat recovery (CIRHR) at different drying temperatures (40, 45, 50 and 55 °C) and 0.5 m/s air velocity and also to discuss and predict the performance of system on energy consumption and drying kinetics of sliced kiwifruit using artificial neural networks (ANNs). The energy efficiency values were obtained between 2.85% and 32.17%. The ANN model was used to predict the energy consumption of the system and moisture content of the kiwifruit. The back-propagation learning algorithm with Levenberg–Marquardt (LM) and Fermi transfer function were used in the network. The coefficient of determination (R²), the root means square error (RMSE) and the mean absolute percentage error (MAPE) were calculated as 0.99, 0.001 and 0.34, respectively. It can be concluded that predicted values are in good agreement with experimental results.