ARTIFICIAL NEURAL NETWORKS AS A NEW TOOL FOR ASSESSING AND MONITORING WOOD MOISTURE CONTENT

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

Substitution of K+ by Na+ in Eucalyptus grandis plantations : consequences on the biological cycle of nutrients

International audienc

RELATIONSHIP BETWEEN THE ANATOMY AND DRYING IN Eucalyptus grandis X Eucalyptus urophylla WOOD

ABSTRACT Drying is an important step to using wood and anatomical characteristics influence this process. Thus, the objective of this work was to evaluate the relationship between anatomy and wood drying. Samples with 2 x 2 x 4 cm were obtained from eight Eucalyptus urophylla x Eucalyptus grandis clones at 1.3 m height to evaluate the anatomy and drying in climate chamber during 15 days. Cell wall fraction was established as the parameter that best correlated with drying, the Pearson correlation coefficient between this parameter and moisture was -0.8986, -0.6580, 0.9216, 0.8743, 0.7131 and 0.8727 for saturated wood, and after 1, 2.5, 5, 10 and 15 days of drying, respectively. The frequency and vessel size, as well as the height and width of the rays showed low relation with wood drying. Wood anatomy, mainly the cell wall fraction, influences moisture losses and should be considered in wood drying programs