Modelling of a Fixed Bed Grain Dryer Using Neural Network

Abstract This paper deals with a neural network application concerning to fixed bed grain dryer. Aim of the study is to set up a relationship between material moisture distribution and physical parameters of drying air, such as temperature and humidity. Five different neural network structures were studied on two different series input data containing inlet and outlet air temperatures and humidities and air flow. Randomly changed input data was used for training the neural network. The data were taken from a physically based model instead of real measurements. The result show that moisture content of the drying bed can be calculated from air parameters using neural network.