Abstract
Grain drying is a vital unit operation in many processing plants. An undesirable change associated with this operation is shrinkage of dried product which results in decreased quality. Recently many attempts have been made to decrease the shrinkage of food stuff during drying. Microwave-assisted fluidized bed drying has particularly been proposed as a potentially effective method. In the present study, at each drying operating condition, the volume of shelled corn was calculated by measuring the three principal characteristic dimensions. The varia- tion of the ratio of mean diameter of the kernel to its initial mean diameter was investigated for different operating conditions. It has been shown that employing microwave in fluidized bed drying reduces the shrinkage of particles considerably. Also, in this study, Artificial Neural Networks (ANN) analysis was employed to predict the extent of shelled corn shrinkage. In the construction of the network, three independent variables: microwave heat source, drying air temperature and moisture content were chosen as the input parameters and shrinkage of dried sample was set as the output parameter (dependent variable). The ANN model with 5 neurons was selected for studying the influence of transfer functions and training algorithms. It has been observed that back-propagation networks with logsig transfer function and trainlm algo- rithm were the most appropriate ANN configuration for predicting shrinkage. Results from the experiments and modeling showed good agree- ment. In order to test the ANN model the random errors were within an acceptable range of ±5% with a correlation coefficient (R2) of 98%.
Highlights
Drying of agricultural products is an important process in terms of storing and use in the food industry and post-harvest processing [7]
Shrinkage Effect In order to show the effects of various parameters on the rate of shrinkage of shelled corn, several experiments were carried out under different operating conditions
Analysis of the experimental data revealed that the variations of D/D0 for the samples in a fluidized bed in both cases of drying with and without microwave heat source were well correlated as linear functions of the moisture content of drying samples
Summary
Drying of agricultural products is an important process in terms of storing and use in the food industry and post-harvest processing [7]. Drying is a process in which moisture migrates from interior of the drying object to the surface, resulting in some changes in physical properties of the dried sample [9]. Shrinkage of food stuff during drying processes has been of special interest during recent decades [1, 2, 4,5,6, 8, 10,11,12,13, 15, 17,18,19] Several methods such as mathematical modeling, regression analysis, artificial neural networks and etc. Artificial Neural Networks (ANN) is effective tools for modeling, International Journal of Agriculture Sciences ISSN: 0975-3710 & E-ISSN: 0975–9107, Volume 4, Issue 1, 2012
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