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STUDY OF SHELLED CORN SHRINKAGE IN A MICROWAVE-ASSISTED FLUIDIZED BED DRYER USING ARTIFICIAL NEURAL NETWORK

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Author(s): MOMENZADEH L. AND ZOMORODIAN A.

Journal: International Journal of Agriculture Sciences
ISSN 0975-3710

Volume: 3;
Issue: 3;
Start page: 150;
Date: 2011;
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Keywords: Shelled corn | shrinking effect | Artificial Neural Network

ABSTRACT
Grain drying is a vital unit operation in many processing plants. An undesirable change associated with thisoperation is shrinkage of dried product which results in decreased quality. Recently many attempts have been made todecrease the shrinkage of food stuff during drying. Microwave-assisted fluidized bed drying has particularly been proposedas a potentially effective method. In the present study, at each drying operating condition, the volume of shelled corn wascalculated by measuring the three principal characteristic dimensions. The variation of the ratio of mean diameter of thekernel to its initial mean diameter was investigated for different operating conditions. It has been shown that employingmicrowave in fluidized bed drying reduces the shrinkage of particles considerably. Also, in this study, Artificial NeuralNetworks (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 inputparameters and shrinkage of dried sample was set as the output parameter (dependent variable). The ANN model with 5neurons was selected for studying the influence of transfer functions and training algorithms. It has been observed thatback-propagation networks with logsig transfer function and trainlm algorithm were the most appropriate ANN configurationfor predicting shrinkage. Results from the experiments and modeling showed good agreement. In order to test the ANNmodel the random errors were within an acceptable range of ±5% with a correlation coefficient (R2) of 98%.
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