Liniker Fernandes da Silva

Predicting moisture content from basic density and diameter during air drying of Eucalyptus and corymbia logs

In air drying of Eucalyptus urophylla and Corymbia citriodora logs for the production of charcoal it is necessary to be able to predict when logs have reached the required moisture content of ≤ 35%.This study is aimed to produce models using basic density and diameter to predict the moisture content of Eucalyptus urophylla and Corymbia citriodora logs after 30, 60 and 90 days drying. 1,2 m long logs were taken at three different heights from three C. citriodora trees and three trees each from two E. urophylla clones (VM4 and Mn463). The 27 debarked, end sealed logs were air dried under cover for 90 days during which the change in moisture content was monitored. The relationship between density and drying was analyzed by Pearson’s correlation coefficient and the models for predicting the moisture content based on the basic density and diameter were produced. The density and the drying showed a high correlation coefficient. The coefficient of determination of the models was above 0,89 with a standard error lower than 6%. The use of the density and diameter to estimate the wood moisture content simplifies the production of the models, which can be used for Eucalyptus and Corymbia genetic materials.

Equações Hipsométricas, Volumétricas e de Crescimento para Khaya ivorensis Plantada em Pirapora

The present study aimed to provide hipsometric, volumetric and growth prediction equations for Khaya ivorensis species. To adjust the height equations we used data with total height and diameter at 1.3 meters collected in the city of Pirapora. For volumetric equations, we performed the scaling of 21 standing trees in the same planting. The growth equations were estimated with volume per hectare of 10 parcels, with age between 30 and 59 months. The volume calculation was performed following the method of Smalian. The adjusted equations showed satisfactory statistics, however lower than the other most widespread crops such as Eucalyptus. But in the face of the lack of technical information about the African Mahogany, this work has contributed significantly to the state of the art of the mentioned plant.

Prediction of the physical, mechanical and colorimetric properties of Eucalyptus grandis heat-treated wood using artificial neural networks

Heat treatment is important to change wood color and increase its dimensional stability, though the quality control and modeling during this process need to be further studied. The aim of this study was to evaluate the efficiency of artificial neural network based in temperature to predict the Eucalyptus grandis wood properties after heat treatment. The physical, mechanical and colorimetric properties of the wood samples after heat treatment at 140, 170, 200 and 230°C for three hours under atmospheric pressure and the presence of air were evaluated. An artificial neural network based on the heat treatment temperature was established to predict the equilibrium moisture content, total volumetric swelling, modulus of rupture, modulus of elasticity, lightness (L*), green-red coordinate (a*) and blue-yellow coordinate (b*) of the wood. The equilibrium moisture content, total volumetric swelling and modulus of rupture (MOR) were affected from 170°C, the modulus of elasticity from 200°C, the oven-dry density from 230°C and colorimetric parameters from 140°C onward. The artificial neural network estimated results with precision, showing its potential for quality control in the wood heat treatment process.

Influência de diferentes funções objetivo em modelos de regulação florestal

A programacao linear e considerada uma das ferramentas basicas da pesquisa operacional, sendo baseada em uma funcao linear de variaveis, chamada de funcao objetivo. Assim, objetivou-se com este trabalho demonstrar a influencia de diferentes funcoes objetivo e suas implicacoes nos modelos de regulacao florestal e suas solucoes. Para isso, foi utilizado o Modelo I de programacao linear em um estudo de caso, no qual foram empregados dados de 10 florestas com estruturas inicias de povoamentos comumente encontrados no setor florestal. A sua solucao foi obtida empregando o software RPF (Regulacao da Producao Florestal), que foi utilizado tambem para simular diferentes cenarios de manejo e planejamento. Utilizaram-se as seguintes funcoes objetivo: maximizacao da producao, maximizacao do valor presente liquido (VPL), minimizacao dos custos e minimizacao do custo medio de producao (CMP). Todos os metodos se mostraram viaveis e conseguiram regular as florestas. Os metodos de minimizacao foram mais eficientes na reducao dos custos, tendo como consequencia tanto a reducao da area de corte anual e da producao anual quanto o aumento da idade de corte e da produtividade. Os metodos de maximizacao foram os mais dispendiosos, com maior producao e maior area de corte anual, menor idade de corte e menor produtividade.

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

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.

MODELLING AND FORECAST OF CHARCOAL PRICES USING A NEURO-FUZZY SYSTEM

Using a monthly time series of charcoal prices in Minas Gerais from January 2000 to September 2014, this study aimed to evaluate the use of neuro-fuzzy system to model the series and forecasting prices. We used four modeling structures for different prices lags (1, 2, 3, 4 and 5 lags). The structure most appropriate for neuro-fuzzy system was chosen based on the root mean square error, mean absolute error, mean squared error, mean absolute percentage error and maximum absolute percentage error for the forecasted period. With the results found, it is possible to conclude that a neuro-fuzzy system can be used properly to predict the charcoal prices.

AVALIAÇÃO DE MÉTODOS DE SELEÇÃO DE COMPETIDORES EM NÍVEL DE ÁRVORE INDIVIDUAL EM UMA FLORESTA ESTACIONAL SEMIDECIDUAL1

Este estudo teve por objetivo avaliar a selecao de arvores competidoras com base em uma parcela circular com raio de 6 m e diâmetro das arvores superior ou igual a 5 cm, no uso do fator de area basal igual a 1, de acordo com o metodo de Bitterlich, e na disputa por espaco e luz observados em campo. Para isso, fez-se a selecao dos fustes competidores ao redor de 24 arvores objeto, escolhidas em funcao de sua importância, no que concerne ao estoque de carbono e ao valor de seus produtos madeireiros e nao madeireiros. Apos as analises, observou-se, pelo teste L&O, que ambos os metodos avaliados apresentaram baixa correlacao em uma classe de diâmetro. Isso indica que a selecao dos competidores precisa ser mais estudada.