Res. Agr. Eng., 2015, 61(2):55-65 | DOI: 10.17221/56/2013-RAE

Mathematical and neural network modelling of terebinth fruit under fluidized bed dryingOriginal Paper

M. Kaveh, R. Amiri Chayjan
Department of Biosystems Engineering, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Iran

The paper presents an application which uses Feed Forward Neural Networks (FFNNs) to model the non-linear behaviour of the terebinth fruit drying. Mathematical models and Artificial Neural Networks (ANNs) were used for prediction of effective moisture diffusivity, specific energy consumption, shrinkage, drying rate and moisture ratio in terebinth fruit. Feed Forward Neural Network (FFBP) and Cascade Forward Neural Network (CFNN) as well as training algorithms of Levenberg-Marquardt (LM) and Bayesian regularization (BR) were used. Air temperature and velocity limits were 40-80°C and 0.81-4.35 m/s, respectively. The best outcome for the use of ANN for the effective moisture diffusivity appertained to CFNN network with BR training algorithm, topology of 2-3-1 and threshold function of TANSIG. Similarly, the best outcome for the use of ANN for drying rate and moisture ratio also appertained to CFNN network with LM training algorithm, topology of 3-2-4-2 and threshold function of TANSIG.

Keywords: MLP; moisture diffusivity; drying rate; shrinkage; multi-layer perceptron

Published: June 30, 2015  Show citation

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Kaveh M, Amiri Chayjan R. Mathematical and neural network modelling of terebinth fruit under fluidized bed drying. Res. Agr. Eng. 2015;61(2):55-65. doi: 10.17221/56/2013-RAE.
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