Dynamic Modeling of the Drying Process of Corn Grains using Neural Networks

This study explored a dynamic modelling of corn drying process. The appropriate use of a dynamic model for the drying process of corn grains could lead to an effective method for optimizing the system. The optimal control strategy can be determined by predicting the future behaviors of the process using a dynamic model. In this work, the dynamic characteristic of the corn grains' water loss during a temperature dynamics treatment in the drying process was measured in a continuous manner using a precise load cell. The nonlinear autoregressive with external input (NARX) neural network was applied to identify and to develop a model of dynamic characteristics of the corn grains drying process. Then, for model training and validation, the dynamic responses of the corn grains' water loss rate to drying temperature were used. A three-layered NARX neural network model, which consists of the 1-10-1 neuron number of each layer with two times delay was successfully developed to identify and to make a model for such a complex system. The developed model showed the accuracy of the corn grains' water loss rate during the drying process with the mean square error (MSE), and determination coefficient (R-2) values of 1.89 x 10-4 and 0.89 consecutively.