The study of drying kinetics and characteristics of agricultural products is
essential for drying time estimation, designing dryers, and optimizing the
drying process. Moisture diffusivity under different drying conditions is
crucial to process and equipment design. The drying kinetics of paddy using
a cabinet tray dryer was modeled using an Artificial Neural Network (ANN)
technique. For predicting moisture ratio and drying rate, the
Levenberg-Marquardt (L.M.) training algorithm with TANSIGMOID and TANSIGMOID
hidden layer activation function provided superior results. A comparative
evaluation of the predicting abilities of ANN and 12 different mathematical
drying models was also carried out. The Midilli model was found to be
adequate for fitting the experimental data with an R2 comparable to that of
the ANN. However, the RMSE observed for ANN (0.0360) was significantly lower
than that of the midilli model (0.1673 to 0.712). Effective moisture
diffusivity increased with an increase in temperature from 15.05 to 28.5 x
10-9 m2/s. The activation energy for drying paddy grains varied between 6.8
to 7.3 kJ/mol, which showed a moderate energy requirement for moisture
diffusion.