PREDICTION OF PADDY DRYING KINETICS: A COMPARATIVE STUDY BETWEEN MATHEMATICAL AND ARTIFICIAL NEURAL NETWORK MODELING

The present study aimed at investigation of deep bed doting of rough rice kernels at various thin layers at different doting air temperatures and flow rates. A comparative study was performed between mathematical thin layer models and artificial neural networks to estimate the doting curves of rough rice. The suitability of nine mathematical models in simulating the doting kinetics was examined and the Midilli model was determined as the best approach for describing doting curves. Different feed forward-back propagation artificial neural networks were examined to predict the moisture content variations of the grains. The ANN with 4-18-18-1 topology, transfer function of hyperbolic tangent sigmoid and a Levenberg-Marquardt back propagation training algorithm provided the best results with the maximum correlation coefficient and the minimum mean square error values. Furthermore, it was revealed that ANN modeling had better performance in prediction of drying curves with lower root mean square error values.