NEURAL NETWORK MODEL FOR FLUIDISED BED DRYERS

Abstract

The application of an artificial neural network (ANN) to model a continuous fluidised bed dryer is explored. The ANN predicts the moisture and temperature of the output solid. A three-layer network with sigmoid transfer function is used. The ANN learning is made by using a set of data that were obtained by simulating the operation by a classical model of dryer. The number of hidden nodes, learning coefficient, size of learning data set and number of iterations in the learning of the ANN were optimised. The optimal ANN has five input nodes and six hidden nodes. It is able to predict, with an error less than 10%, the moisture and temperature of the output dried solid in a small pilot plant that can treat up to 5 kg/h of wet alpeorujo. This is a wet solid waste that is generated in the two-phase decanters used to obtain olive oil.

Keywords:

• Drying
• Dryer modelling
• Artificial neural networks

ACKNOWLEDGMENTS

The study has been carried out with financial support from the Commission of the European Communities, FAIR RTD programme, CT96-1420, and from the Spanish Ministry of Education, National Plan of R&D, AMB97-1293-CE.