A neural network topology for modelling grain drying

This paper is concerned with modelling moisture distribution in agricultura l fixed-bed dryers using a neural network (NN). Ten different NN topologies were studied for modelling and the most appropriate one was selected to us e. Inlet and outlet air temperatures, absolute humidities and air flow were considered as the input variables to the layers of the drying bed. Some to pologies include also grain temperature for better performance. Randomly va rying time series data simulating inlet conditions were used for training t he neural network. The data were taken from a physically-based simulation m odel instead of real measurements. The simulation of three scenarios corres ponding to constant, slow and fast input dynamics were compared. Average an d maximum deviations were used as performance measures to evaluate and comp are the models. On the basis of the comparisons, the topology of the best m odel was identified. The results show that moisture distribution in the dry ing bed could be well modelled using a neural network. (C) 2000 Elsevier Sc ience B.V. All rights reserved.