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

Mojtaba Tohidi, Morteza Sadeghi, Seyed Rasoul Mousavi, Seyed Ahmad Mireei

Research output: Contribution to journalArticlepeer-review

20 Citations (Scopus)


This study aimed to model the performance indices of deep bed drying of rough rice using artificial neural networks (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 (POR), evaporation rate (ER), and percentage of kernel cracking (KC) were investigated. To predict the dependent parameters, 3 well-known networks, namely the multilayer perceptron, generalized feed forward (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.

Original languageEnglish
Pages (from-to)738-748
Number of pages11
JournalTurkish Journal of Agriculture and Forestry
Issue number6
Publication statusPublished - 2012
Externally publishedYes


Isfahan University of


  • Artificial neural network
  • Drying kinetics
  • Performance indices
  • Regression
  • Rice
  • Sensitivity analysis

ASJC Scopus subject areas

  • Forestry
  • Food Science
  • Ecology


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