Modeling and Simulation of Biomass Drying Using Artificial Neural Networks

Willow (Salix viminalis) is a moist material after the crops. Therefore, the content of water in this type of material has to be lowered by drying before any further mechanical or thermal processing, in order to increase its calorific value. The process of drying is energy-intensive. Thus it is advisable to search for optimal methods and parameters of drying. The optimisation requires evolving a model that is based on the crucial parameters of the process. One of the possible solutions is to apply models of Artificial Neural Networks. Artificial Neural Networks belong to the group of methods of artificial computational intelligence and are often used in modelling various phenomena and processes. The aim of this work was to develop models using Artificial Neural Networks to describe the process of convective drying of the willow woodchips. As a result of presented work we obtained neural models describing alterations of water content, changes of the temperature and the mass of the chips. The presented models are highly accurate. We used experimentally obtained data in order to validate the models. It is important to underline that the data were not applied while the artificial neural networks were being developed. Subsequently, the models were used to simulate the process of drying what allowed us to define the optimal parameters of drying willow woodchips characterised by different moisture content.