Artificial neural network modeling of process and product indices in deep bed drying of rough rice

Authors : Mojtaba TOHIDI, Morteza SADEGHI, Seyed Rasoul MOUSAVI, Seyed Ahmad MIREEI

Pages : 738-748

Doi:10.3906/sag-1206-8

View : 16 | Download : 7

Publication Date : 2012-12-01

Article Type : Research Paper

Abstract :This study aimed to model the performance indices of deep bed drying of rough rice using artificial neural networks insert ignore into journalissuearticles values(ANNs);, compare the ANN approach to the multivariate regression method, and determine the sensitivity of the ANN model to the input variables. The effects of air temperature, air velocity, and air relative humidity on drying kinetics, product output rate insert ignore into journalissuearticles values(POR);, evaporation rate insert ignore into journalissuearticles values(ER);, and percentage of kernel cracking insert ignore into journalissuearticles values(KC); were investigated. To predict the dependent parameters, 3 well-known networks, namely the multilayer perceptron, generalized feed forward insert ignore into journalissuearticles values(GFF);, and modular neural network, were examined. The GFF networks with the Levenberg-Marquardt learning algorithm, hyperbolic tangent activation function, and 4-15-1, 3-4-4-1, 3-7-1, and 3-11-1 topologies provided superior results, respectively, for predicting moisture content, POR, ER, and CK. The values of all of the drying indices predicted by the ANN were closer to the experimental data than linear and logarithmic regression models. The output variables were significantly affected by the dependent variables. However, air temperature and air relative humidity showed the maximum and the minimum influence on the network outputs, respectively.
Keywords : Key words Artificial neural network, drying kinetics, performance indices, regression, rice, sensitivity analysis