Modelling of a fixed bed grain dryer using neural network

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 temper ature and humidity. Five different neural network structures were studied o n two different series input data containing inlet and outlet air temperatu res and humidities and air Bow. Randomly changed input data was used for tr aining the neural network. The data were taken from a physically based mode l instead of real measurements. The result show that moisture content of th e drying bed can be calculated from air parameters using neural network Cop yright (C) 1998 IFAC.