Application of artificial neural networks to investigate the drying of cooked rice

Commercially available neural networks software was applied for prediction of processing parameters with reference to the product quality of the dehydrated cooked rice. These results were verified with experimental data. The experimental results indicate good concurrence with the predicted data. A small capacity vibrofluidized bed drier was used to conduct the experimental studies. The inlet air temperature of 160C to 180C, the inlet air velocity of 3 to 6 ms(-1) and the resident time of 5 to 8 min were used in the study. The optimized process parameters were identified. The operation of the neural network model is discussed. The trained model can be applied in a nonlinear model predictive scheme to control the product moisture content.