Güneş destekli ısı pompalı bir kurutucuda mantarın kuruma davranışlarının yapay sinir ağı kullanılarak modellenmesi

Abstract

Dryer was tested by drying mushroom with solar energy and solar assisted heat pump separately at 45 °C and 55 °C drying air temperature and 0.9 m s -1 and 1.2 m s -1 drying air velocities. Moisture content (MC), moisture ratio (MR) and drying rate (DR) which were obtained from experiments were modeled by using Levenberg-Marquardt (LM) the backpropagation learning algorithm and fermi transfer function with artificial neural networks (ANNs). The coefficient of multiple determination (R 2 ), the root means square error (RMSE) and the mean absolute percentage error (MAPE) were used for the determination of statistical validity of the developed model. R 2 , RMSE and MAPE were determined for MC