سال انتشار: ۱۳۸۸

محل انتشار: ششمین کنگره بین المللی مهندسی شیمی

تعداد صفحات: ۶

نویسنده(ها):

M Nikzad – Faculty of Chemical Engineering, Mazandaran University,
K Movagharnejad – Faculty of Chemical Engineering, Mazandaran University,
F Asghari Katisari – Faculty of Chemical Engineering, Mazandaran University,
S Fatemi – Faculty of Chemical Engineering, Mazandaran University,

چکیده:

In this study drying of apple was studied at different thickness and type of tray. Page model was tested to fit the moisture ratio of apple. Artificial neural network (ANN) is a technique with flexible mathematical structure which is capable of identifying complex non-linear relationship between input and output data. A multi layer perceptron (MLP) neural network was used to predict the moisture ratio of apple during drying. A 3-18-1 structure provided the least errors. In addition a three-layer feed-forward neural network was used to estimate the moisture ratio of apple. A backpropagation algorithm was developed (using MATLAB ) and applied to training and testing the network. It was found that the estimated moisture ratio by multi layer perceptron neural network is more accurate than Page’s model. The results were compared with experimental data. It was also found that moisture ratio decreased with increasing of drying time.