Soybean waste in particleboard production

Martins, Evelyn Hoffmamm; Vilela, Alan Pereira; Mendes, Rafael Farinassi; Mendes, Lourival Marin; Vaz, Lívia Elisabeth Vasconcellos de Siqueira Brandão; Guimarães, Mário

doi:10.1590/1413-70542018422015817

Cited by 28 publications

(19 citation statements)

References 8 publications

(11 reference statements)

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“…If biodiesels are used effectively for the generation of energy, it would be extremely beneficial for the environment as well as for the job creation of local people. Since many biodiesel sources are indigenous (for example, Calophyllum inophyllum is native to Asia [4]) or abundant (for example, Brazil and US leads in soybean production [5]) to specific regions, its usage will motivate the local farmers to cultivate such sources. In recent years, various processes have been used to produce biodiesel from different oils like soybean oil, canola oil, palm oil, sunflower seed oil etc.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Predictive Modelling of Biodiesel Production—A Comparative Perspective

et al. 2021

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Owing to the ever-growing impetus towards the development of eco-friendly and low carbon footprint energy solutions, biodiesel production and usage have been the subject of tremendous research efforts. The biodiesel production process is driven by several process parameters, which must be maintained at optimum levels to ensure high productivity. Since biodiesel productivity and quality are also dependent on the various raw materials involved in transesterification, physical experiments are necessary to make any estimation regarding them. However, a brute force approach of carrying out physical experiments until the optimal process parameters have been achieved will not succeed, due to a large number of process parameters and the underlying non-linear relation between the process parameters and responses. In this regard, a machine learning-based prediction approach is used in this paper to quantify the response features of the biodiesel production process as a function of the process parameters. Three powerful machine learning algorithms—linear regression, random forest regression and AdaBoost regression are comprehensively studied in this work. Furthermore, two separate examples—one involving biodiesel yield, the other regarding biodiesel free fatty acid conversion percentage—are illustrated. It is seen that both random forest regression and AdaBoost regression can achieve high accuracy in predictive modelling of biodiesel yield and free fatty acid conversion percentage. However, AdaBoost may be a more suitable approach for biodiesel production modelling, as it achieves the best accuracy amongst the tested algorithms. Moreover, AdaBoost can be more quickly deployed, as it was seen to be insensitive to number of regressors used.

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Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Predictive Modelling of Biodiesel Production—A Comparative Perspective

et al. 2021

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“…In addition, in the search for higher quality boards, studies have been carried out prioritizing high values of mechanical strength and the replacement of wood by other lignocellulosic materials (Cravo et al, 2015). In this sense, the paper carried out with sugarcane bagasse panels (Mendes et al, 2012;Protásio et al, 2012;Kord et al, 2015;Buzo et al, 2019); corncobs (Protásio et al, 2012;Scatolino et al, 2015); Brazil nut hedgehog (Nogueira et al, 2018), rice husk (César et al, 2017;Souza et al, 2017), soybean pod (Martins et al, 2018), soybean husk (Guimarães et al, 2019) and sorghum bagasse (Guimarães Júnior et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Low-density particleboard properties produced with jupati particles and eucalyptus wood

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et al. 2020

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The use of jupati leaves and seeds, for ornamental and artisanal purposes, results in the generation of by-products such as the internal part of the petiole that, sometimes, are not used. Thus, the application of this waste for the production of materials assists the reduction of major environmental problems. In this sense, this paper aimed at using jupati petiole particles to replace eucalyptus wood to produce low density particleboards and to evaluate the effect of this incorporation on its physical and mechanical properties. The panels were composed with different percentages of waste in association with eucalyptus: 0, 10, 20 and 30%. The nominal panels' density was fixed at 0.60 g cm -3 and was bonded with urea-formaldehyde. To evaluate the properties, physical and chemical particle analysis and physical-mechanical panels tests were performed. Regarding the physical panel properties, all were classified as low density. There was an increase in the compression ratio and, consequently, in hygroscopicity as greater proportions of jupati particles were inserted. For the mechanical properties, it was observed that, in small quantities, the insertion of waste did not affect the panel's viability. Thus, it is concluded that the waste has the potential to be used as raw material in the production of panels, being included in up to 15 % for regulatory compliance.

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“…Several papers aim to associate the improvement in the production processes and composition of glulam (Calil Neto et al, 2014;Christoforo et al, 2014;Lahr et al, 2015;Icimoto et al, 2016), where the adhesives most used in industrial scale for glulam production and wood based panels have formaldehyde in their constitution, as resorcinol-formaldehyde, urea-formaldehyde; phenol-formaldehyde; phenol-resorcinol-formaldehyde; melamine-formaldehyde; melanine-urea-formaldehyde and tannin-formaldehyde (Guimarães et al, 2014;Scatolino et al, 2017;Martins et al, 2018;Martins et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Number of laminae on the mechanical behavior of glued laminated timber (glulam) of Toona ciliata produced with vegetable polyurethane adhesive

et al. 2019

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The glued laminated timber (glulam) is a composite material obtained from the bonding of wood laminae arranged with parallel fibers put together. The purpose of this paper was to evaluate the influence of the number of laminae on the mechanical behavior of glulam of Toona ciliata produced with vegetable-based polyurethane adhesive. The glulam beams were produced with lamellae in the dimensions of 6.0 x 1.5 x 120.0 cm, with 3 beams being made per treatment, which were structurally composed by 5, 7 and 9 laminae. The polyurethane adhesive based on castor oil was used, applying a grammage of 350 g m-². For the technological characterization of the wood the moisture in the dry base, bulk density, chemical characterization, compressive strength parallel to the fibers, shear and modulus of elasticity (MOE) and rupture (MOR) in the static bending test. While the properties of modulus of elasticity (MOE), modulus of rupture (MOR), shear strength, delamination and visualization of the glue line through photomicrographs of the laminated wood samples were determined for glulam beams. The results showed that the beams with 5 laminae presented values similar to solid wood. The increase in the number of laminae provided lower values for stiffness and strength of the beams. The photomicrographs showed the quality of the bonding and the satisfactory values for shear strength and delamination, indicating the viability of the species under study for situations with high moisture content.

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Soybean waste in particleboard production

Cited by 28 publications

References 8 publications

Machine Learning-Based Predictive Modelling of Biodiesel Production—A Comparative Perspective

Machine Learning-Based Predictive Modelling of Biodiesel Production—A Comparative Perspective

Low-density particleboard properties produced with jupati particles and eucalyptus wood

Number of laminae on the mechanical behavior of glued laminated timber (glulam) of Toona ciliata produced with vegetable polyurethane adhesive

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