ABSTRACT
Drying of wood is necessary for its use and moisture control is important during this process. The aim of this study was to use artificial neural networks to evaluate and monitor the wood moisture content during drying. Wood samples of 2 × 2 × 4 cm were taken at 1.3 m above the ground, outside of radial direction, from seven 2-year-old materials and three 7-year-old materials. These samples were saturated and drying was evaluated until the equilibrium moisture content, then, the artificial neural networks were created. The materials with higher initial moisture reached equilibrium moisture content faster due to its higher drying rate. The basic density of all wood materials was inversely proportional at the beginning and directly proportional to the moisture at the end of drying. All artificial neural networks used in this work showed high accuracy to estimate the moisture, however, the neural network based on the basic density and drying days was the best. Therefore, artificial neural networks can be used to control the moisture content of wood during drying.
Keywords:
Eucalyptus urophylla × Eucalyptus grandis; Drying; Modeling
