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.Keywords: Eucalyptus urophylla × Eucalyptus grandis; Drying; Modeling.REDES NEURAIS ARTIFICIAIS COMO NOVA FERRAMENTA PARA AVALIAÇÃO E MONITORAMENTO DA UMIDADE DA MADEIRA RESUMO -A secagem da madeira é necessária para sua utilização e o controle da umidade é fundamental durante este processo. O objetivo deste trabalho foi utilizar redes neurais artificiais para avaliar e monitorar a umidade da madeira durante a secagem. Amostras de 2 x 2 x 4 cm foram retiradas a 1,3 metros acima do solo na parte externa no sentido radial de sete materiais de dois anos e três de sete anos de Eucalyptus urophylla x Eucalyptus grandis. Essas amostras foram saturadas e sua secagem avaliada até a umidade de equilíbrio, logo após, foram criadas as redes neurais artificiais. Os materiais com maior umidade inicial atingiram a umidade de equilíbrio mais rápido, devido a maior taxa de secagem. A densidade básica da madeira de todos os materiais foi inversamente proporcional no inicio e diretamente proporcional à umidade no final da secagem. A rede neural artificial baseada na densidade básica e nos dias de secagem apresentou alta precisão para avaliar e monitorar a umidade da madeira. Portanto, as redes neurais artificiais podem ser utilizadas para controle da umidade da madeira durante a secagem.Palavras-chave: Eucalyptus urophylla × Eucalyptus grandis; Modelagem; Secagem. 544Revista Árvore, Viçosa-MG, v.40, n.3, p.543-549, 2016 ZANUNCIO, A.J.V. et al.

Artificial Neural Networks To Distinguish Charcoal from Eucalyptus and Native Forests Based on Their Mineral Components

“…Some studies have developed ANN models to estimate wood density, 35,36 wood stiffness, 37 and wood strength, 38 as well as to assess the surface quality of wood 39 and to predict the moisture content of wood during drying. 40,41 With respect to the application of the ANN approach in classifications, most studies have shown promising findings as well as ours. For instance, Cui et al 26 have used laser-induced breakdown spectroscopy (LIBS) combined with ANN to classify four wood species and reported a correct specimen classification rate of 100% in the test set, using a model with a multilayer perceptron network and the Broyden−Fletcher− Goldfarb−Shanno iterative algorithm.…”

“…ANN has been applied as a modeling tool to overcome various challenges in many timber forestry sectors. Some studies have developed ANN models to estimate wood density, , wood stiffness, and wood strength, as well as to assess the surface quality of wood and to predict the moisture content of wood during drying. , …”

“…ANN has been applied as a modeling tool to overcome various challenges in a number of timber forestry sectors. Some studies have developed ANN models to estimate the wood density (Leite et al, 2016;Demertzis et al, 2017), wood stiffness (García-Iruela et al, 2016), wood strenght (Zanuncio et al, 2017), to assess the surface quality of wood (Hazir and Koc 2018) and to predict the moisture content of wood during drying (Chai et al, 2018;Zanuncio et al, 2016).…”

“…The data used as input variables in ANN for evaluating wood can be physical and mechanical characteristics (Nasir et al, 2018), heat treatment temperature (Van Nguyen et al, 2018, Zanuncio et al, 2017, tree age (Leite et al, 2016), wood species (Van Nguyen et al, 2018), basic density (Zanuncio et al, 2016), basal area (in m 2 / ha), annual average increment (in m³/ ha / year), total height and diameter at 1.3 m from the ground (Leite et al, 2016). This study is pioneering in using mineral elements contained in charcoals as predictive variable in ANN modeling.…”

Ecophysiology modeling by artificial neural networks for different spacings in eucalypt

“…As alternative to traditional regression modeling, Artificial Neural Networks (ANNs) are composed of a massive parallel system integrated of simple processing units (artificial neurons), which calculate certain mathematical functions and allow to generalize assimilated knowledge to unknown data (Gorgens et al, 2009;Binoti, 2010;Binoti et al, 2015;Zanuncio et al, 2016).…”