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

Zanuncio, Antônio José Vinha; Carvalho, Amélia Guimarães; Silva, Liniker Fernandes da; Silva, Marcela Gomes da; Carneiro, Angélica de Cássia Oliveira; Colodette, Jorge Luiz

doi:10.18671/scifor.v45n113.10

Cited by 8 publications

(9 citation statements)

References 30 publications

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“…The energy resolution of a detector (thermoelectrically cooled silicon-drift-detector) was of 142 eV for 5.9 keV (Mn-Kα). Measurements were carried out under 20 mbar vacuum conditions [Silva et al 2017].…”

Section: M4 Tornadomentioning

confidence: 99%

“…Data output was obtained through the X-ray intensity of specific X-ray peaks corresponding to the element signals measured in each point defined by its X and Y coordinate (μm). The data were converted using the software's function into a data matrix, from which XY contour maps (2-dimensional maps) of the data were generated for each element [Silva et al 2017].…”

Section: M4 Tornadomentioning

confidence: 99%

“…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).…”

Section: Identificating the Charcoal Originmentioning

confidence: 99%

“…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.…”

Section: Identificating the Charcoal Originmentioning

confidence: 99%

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

et al. 2020

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Charcoal has been used as a renewable energy source in many countries. However, the indiscriminate use of wood from native forests is detrimental to sustainability. The development of rapid and efficient methodologies for distinguishing charcoal produced from native Forest or Eucalyptus plantations is essential to curb illegal coal transport and trade. The aim of this study was to distinguish charcoal from native and Eucalyptus woods by Artificial Neural Network (ANN) based on their mineral composition. Specimens from native woods (Apuleia sp., Cedrela sp., Aspidosperma sp., Jacaranda sp., Peltogyne sp., Dipteryx sp. and Gochnatia sp.) and from Eucalyptus sp. hybrid woods from commercial forest plantations were pyrolysed at temperatures from 300 to 700°C in order to simulate the actual pyrolysis conditions and species widely used illegally in southeastern Brazil. The composition and proportion of the mineral elements of charcoal were determined by X-ray fluorescence (XRF).ANNs were trained based on the elemental composition of the charcoal specimens to classify the species and origin of the charcoals (native forest and Eucalyptus). ANNs based on mineral element content yielded high percentage of correct classification for charcoal specimens by species (72% accuracy) or origin (97% accuracy) from an independent validation sample set.

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Section: M4 Tornadomentioning

confidence: 99%

Section: M4 Tornadomentioning

confidence: 99%

Section: Identificating the Charcoal Originmentioning

confidence: 99%

Section: Identificating the Charcoal Originmentioning

confidence: 99%

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

et al. 2020

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“…The definition of the age at which the species makes the transition from juvenile wood to adult wood has been evaluated in several studies (Bal & Bektas, 2013, Palermo et al, 2015, Zanuncio et al, 2017. Evidently, adult wood is the aim of study of this research.…”

Section: Introductionmentioning

confidence: 99%

WOOD UTILIZATION OF Eucalyptus grandis IN STRUCTURAL ELEMENTS: DENSITIES AND MECHANICAL PROPERTIES

Lahr

Nogueira

Araújo³

et al. 2018

Eng. Agríc.

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Over the years, the species of eucalyptus has become a multipurpose raw material. In addition, the most relevant aspect of the use for various purposes is related to the production of a high quality wood, coming from short duration plantations, which is fundamental to the current demand of the industries. However, its use in civil construction has not yet reached a level of importance, due to the low knowledge of many of its resistance properties and the consequent popular fear in the use of reforestation woods, in particular the Eucalyptus grandis. This research investigated its main mechanical properties, aiming to reinforce its constructive applications in wood structures. For this, two physical properties and fourteen mechanical properties, in two different moisture conditions of the samples were evaluated, according to the norm NBR 7190 (1997). In the first moisture content, the samples were stabilized at 30%, while the second level considered the content of 12%. It was obtained 3580 determinations for the sixteen properties. From the 14 mechanical properties, only 7 had significant increases with the moisture reduction (30% to 12%), consisting of the rupture modulus in the parallel and normal compressions, normal traction and static bending; modulus of elasticity in normal compression and static bending and in shear strength.

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Prediction of water absorption and swelling of thermally modified fir wood by artificial neural network models

et al. 2022

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

Cited by 8 publications

References 30 publications

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

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

WOOD UTILIZATION OF Eucalyptus grandis IN STRUCTURAL ELEMENTS: DENSITIES AND MECHANICAL PROPERTIES

Prediction of water absorption and swelling of thermally modified fir wood by artificial neural network models

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