Some investigations on moisture injection, moisture diffusivity and thermal conductivity using a three-dimensional computation of wood heat treatment at high temperature

Oumarou, Noura; Kocaefe, Duygu; Kocaefe, Yasar S.

doi:10.1016/j.icheatmasstransfer.2014.12.014

Cited by 6 publications

(4 citation statements)

References 41 publications

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“…The model can be well combined with an intelligent adaptive kiln controller for an online optimized drying schedule. Oumarou et al (2015) proposed the 3D modeling of high-temperature heat-treated wood. Neural networks are the primary branch of intelligent control technology research in recent years.…”

Section: Introductionmentioning

confidence: 99%

Prediction of the equilibrium moisture content and specific gravity of thermally modified wood via an Aquila optimization algorithm back-propagation neural network model

Chen

Wang

2022

BioRes

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The equilibrium moisture content and specific gravity of Uludag fir (Abies bornmüelleriana Mattf.) and hornbeam (Carpinus betulus L.) woods were investigated following heat treatment at different temperatures and times. Two prediction models were established based on the Aquila optimization algorithm back-propagation neural network model. To demonstrate the effectiveness and accuracy of the proposed model, it was compared with a tent sparrow search algorithm-back-propagation network model, a back-propagation network model, and an artificial neural network. The results showed that the Aquila optimization algorithm back-propagation model reduced the root mean square error value of the original back-propagation model by 87% and 97%, respectively, and the decision coefficients (R2) of the equilibrium moisture content and specific gravity were 0.99 and 0.98; as such, the model optimization effect was obvious. Therefore, this paper provides an effective method for the optimization of the process parameters (such as heat treatment time, temperature, and air pressure) in wood heat treatment and related fields.

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Section: Introductionmentioning

confidence: 99%

Prediction of the equilibrium moisture content and specific gravity of thermally modified wood via an Aquila optimization algorithm back-propagation neural network model

Chen

Wang

2022

BioRes

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“…The literature reports reduction from 6.8% to 55.21% for total volumetric swelling at 180°C and 240°C for E. grandis wood treated during 4 and 8 h (CALONEGO et al 2012). The heat treatment involves the application of temperature between 160°C and 260°C (ESTE-VES; PEREIRA, 2009), combined with other variables, such as time (KESIK et al, 2014;YALCIN;SAHIN, 2015), pH (WANG et al, 2012), presence of atmosphere (CANDELIER et al, 2013;KUZ-MAN et al, 2015) and relative humidity (OUMAROU et al, 2015). This process degrades the hemicellulose and cellulose of the wood (BRITO et al, 2008), limiting moisture adsorption capacity and increasing the wood dimensional stability (AYTIN et al, 2015;BASTANI et al, 2015;ESTEVES et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

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

Zanuncio¹,

Carvalho²,

Silva³

et al. 2017

Sci. For.

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“…mosfera [4] e umidade [5]. Este processo, de fácil aplicação e sem utilização de produtos químicos [1,6], visa, principalmente, degradar os carboidratos da madeira [7], limitando a adsorção da umidade e aumentando a estabilidade dimensional [8].…”

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Método de ressonância para predição das propriedades mecânicas das madeiras de Eucalyptus urophylla e Pinus oocarpa termorretificadas

Carvalho¹,

Zanuncio

Silva³

et al. 2017

Matéria (Rio J.)

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RESUMOVibrações transversais apresentam potencial para avaliação da madeira devido a alta precisão, baixo custo e rapidez, mas sua aplicação em madeira termorretificada ainda é pouco estudada. O objetivo deste trabalho foi avaliar as propriedades físicas e mecânicas de madeira termorretificada de Eucalyptus urophylla e Pinus oocarpa e relacionar a análise de ressonância (Bing) com o modulo de elasticidade estático e de ruptura da madeira.Corpos de prova foram confeccionados de três árvores de Eucalyptus urophylla e três de Pinus oocarpa e termorretificados a 150, 175, 200 e 225°C, com taxa de aquecimento de 5°C/min, tempo de residência de quatro horas com pressão atmosférica e presença de ar.A termorretificação reduziu a densidade básica, inchamento volumétrico, inchamento linear, coeficiente de anisotropia, umidade de equilíbrio e resistência mecânica da madeira de ambas as espécies. A análise de ressonância (Bing) apresentou alta relação com o modulo de elasticidade estático e o módulo de ruptura da madeira termorretificada de ambas as espécies e, portanto, esta técnica pode ser utilizada para avaliar as propriedades mecânicas da madeira submetidas a este processo. Palavras-chave: Bing, Eucalyptus urophylla; Pinus oocarpa; Tratamento térmico. ABSTRACTThe analysis of the transverse vibrations has the potential to evaluate the wood due to its high precision, low cost and quickly, however there are few studies about its use in heat treated wood. The aim of this study was to evaluate the physical and mechanical properties of heat-treated Eucalyptus urophylla and Pinus oocarpa wood and relate the analysis of resonance (Bing) with the modulus of elasticity and modulus of rupture of wood. Three Eucalyptus urophylla and Pinus oocarpa trees were used and specimens were prepared and heat-treated at 150, 175, 200 and 225°C with a heating rate of 5°C/ min, four hours of residence time at atmospheric pressure and the presence of air. The heat treatment reduced the volumetric swelling, linear swelling, anisotropy factor, equilibrium moisture content and mechanical strength of the wood in both species. The resonance analysis (Bing) showed a high correlation with the static modulus of elasticity and modulus of rupture in heat-treated wood for both species, thus, this technique can be used to evaluate the mechanical properties during this process.

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Some investigations on moisture injection, moisture diffusivity and thermal conductivity using a three-dimensional computation of wood heat treatment at high temperature

Cited by 6 publications

References 41 publications

Prediction of the equilibrium moisture content and specific gravity of thermally modified wood via an Aquila optimization algorithm back-propagation neural network model

Prediction of the equilibrium moisture content and specific gravity of thermally modified wood via an Aquila optimization algorithm back-propagation neural network model

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

Método de ressonância para predição das propriedades mecânicas das madeiras de Eucalyptus urophylla e Pinus oocarpa termorretificadas

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