Energy Density Model for Forest Species From Cerrado

Silva, Carlos José da; Vale, Ailton Teixeira do

doi:10.1590/1983-21252018v31n216rc

Cited by 4 publications

(4 citation statements)

References 10 publications

(12 reference statements)

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“…The Pe value per unit area estimated in this study is approximately 25% lower than the value of 1378.54 GJ ha -1 reported by Silva & Vale (2018) for the same area. This discrepancy may be related to the different models applied, which may have affected the results and errors in the estimation.…”

Section: Higher Heating Value and Energy Potentialcontrasting

confidence: 79%

Artificial intelligence associated with satellite data in predicting energy potential in the Brazilian savanna woodland area

Carrijo¹,

Miguel²,

Vale³

et al. 2020

iForest

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The use of artificial intelligence to generate information of the savanna's energy capacity may support sustainable management of those areas. We assessed the efficacy of artificial neural networks (ANNs) combined with satellite data to estimate the energy potential (Pe) for cerradão, a dense savannah-like vegetation type in Brazil. We conducted a forest inventory for measuring dendrometric variables and sampling woody materials and barks in a cerradão area in the state of Tocantins, Brazil. The Pe of cerradão biomass was estimated based on the observed higher calorific power and drier biomass values. Six vegetation indices were retrieved from a RapidEye image and tested for correlation to choose the optimum vegetation index for biomass modeling. The basal area and the Normalized Difference Vegetation Index were used as predictors in the Pe modeling. We estimated an average of 19.234 ± 0.411 GJ ton-1 and 19.878 ± 1.090 GJ ton-1 for higher heating values of the wood species and barks, respectively, and an average Pe of 1022.660 GJ ha-1. The best ANN showed an error of 11.3% by using a structure of two, eight, and one neurons in the input layer, in the hidden layer, and in the output layer, respectively, as well as activation functions of the tangential and sigmoidal types. The validation tests showed no significant difference between the observed and ANNpredicted values. Based on our results, we concluded that Pe can be efficiently predicted by combining ANNs and remotely sensed data, which ultimately is a promising tool for forest sustainable management of the cerrado ecosystems.

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Section: Higher Heating Value and Energy Potentialcontrasting

confidence: 79%

Artificial intelligence associated with satellite data in predicting energy potential in the Brazilian savanna woodland area

Carrijo¹,

Miguel²,

Vale³

et al. 2020

iForest

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“…Os resultados da análise química imediata da madeira de D. cearenses se assemelharam aos observados por Silva, Vale (2018), que constataram em 34 espécies características do cerrado teor de cinzas entre 0 e 1%, carbono fixo entre 14% e 23% e de materiais voláteis de 77 a 85,5%.…”

Section: Resultados E Discussõesunclassified

Propriedades energéticas da madeira e casca de Dalbergia cearensis Ducke

Oliveira

Sousa

Silva

et al. 2019

AGROPECUÁRIA NO SEMIÁRIDO

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A madeira é considerada o combustível mais antigo usado como fonte de energia. No entanto, com a crescente demanda, tornou-se necessário adotar fontes que promovam menores impactos ao meio ambiente. O presente trabalho teve como objetivo determinar as propriedades físicas, químicas e energéticas da madeira de Dalbergia cearensis. O estudo foi realizado em uma área de vegetação nativa em Cristino Castro-PI, selecionando três indivíduos da espécie, dos quais foram retirados discos de madeira nas alturas de 0, 25, 50, 75 e 100% da altura comercial. A densidade básica foi determinada conforme NBR 11941. O poder calorífico superior foi determinado conforme NBR 8633 e o teor de materiais voláteis, cinzas e de carbono fixo foi determinado de acordo com a NBR 8112. A densidade obtida para a espécie foi de 0,73 g/cm3, o poder calorífico foi de 4.327 kcal kg-1 para madeira e 4.419 kcal kg-1 para casca. O teor de materiais voláteis, cinzas e carbono fixo para a madeira foram 87,9%, 0,3% e 11,9%, respectivamente, e 85,1%, 1,6% e 13,3% para a casca. A espécie apresentou potencial para energia, mas ressalta-se que devido à ótima qualidade da madeira, o destino deverá visar usos mais nobres, como por exemplo serraria.

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“…The ash content also behaved as expected with the residence time; the degradation of the material resulted in a high ash content (33.66 to 53.79%). In general, the ash content of the wood is <1% (de Carvalho et al, 2020;da Silva et al, 2018). The high values of the ash content are related to the impurities in the material (Padilla et al, 2019).…”

Section: Proximate Analysis and Higher Heating Value (Hhv)mentioning

confidence: 99%

Biochar produced from poultry litter waste

Pereira

Varanda

Carvalho

et al. 2021

RSD

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Brazil generates substantial quantity of poultry litter waste because of its worldwide prominence in the production of broiler chickens. The volume of the poultry litter biomass generates considerable environmental impact. The objective was to characterize the biochar produced from poultry litter residue under different conditions with the aim of determining the best residence time and temperature. Poultry litter was collected after two batches of chicken breeding. Five treatments for biochar production were carried out at a temperature of 450 °C (defined by thermogravimetric analysis - TGA) and residence times of 0.5, 1, 2, 4, and 6h. The biochar produced was assessed using proximate analysis, moisture content, gravimetric yield, scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), and HHV (higher heating value). The results revealed that the best conditions for biochar production was 450 °C (pyrolysis) and residence time of 0.5h, with 37.21% gravimetric yield. We concluded that the biochar produced in this study is recommended for soil application but was not suitable for energy purposes because of its high ash content (up to 33.66%) and low HHV (18,907 J g-1).

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Energy Density Model for Forest Species From Cerrado

Cited by 4 publications

References 10 publications

Artificial intelligence associated with satellite data in predicting energy potential in the Brazilian savanna woodland area

Artificial intelligence associated with satellite data in predicting energy potential in the Brazilian savanna woodland area

Propriedades energéticas da madeira e casca de Dalbergia cearensis Ducke

Biochar produced from poultry litter waste

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