Relation between higher heating value and elemental and mineral biomass plant components
Autor(a) principal: | |
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Data de Publicação: | 2011 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | por |
Título da fonte: | Pesquisa Florestal Brasileira (Online) |
Texto Completo: | https://pfb.cnpf.embrapa.br/pfb/index.php/pfb/article/view/200 |
Resumo: | The aims of this work were to evaluate the correlation, to adjust and select simple and multiple linear statistical models between elemental components (carbon, hydrogen and oxygen) and ash content with higher heating value for plant biomass; to use the principle components analysis for the creation of an energetic development index and to adjust a linear model between energetic development index and higher heating value. Eight types of biomass were used. Three linear and nine multiple statistical models were adjusted. The best models were selected based on the significance of coefficients, adjusted determination coefficient, estimative standard error, coefficient of variation, linearity of parameters, normality, presence of heterocedasticity and lack of error correlation. The variance inflation factor was determined for linear multiple models. High correlation between the variables studied was found. Models 1, 3 and 11 were considered most adequate. Practical use of model 2 is not possible. Principle components analysis was efficient in obtaining an energetic development index of lignocellulosic residues and it may be used for solving multicollinearity found between variables considered.doi: 10.4336/2011.pfb.31.66.113 |
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Relation between higher heating value and elemental and mineral biomass plant componentsRelação entre o poder calorífico superior e os componentes elementares e minerais da biomassa vegetalModelagemRegressãoCorrelaçãoSeleçãoModelingRegressionCorrelationSelectionThe aims of this work were to evaluate the correlation, to adjust and select simple and multiple linear statistical models between elemental components (carbon, hydrogen and oxygen) and ash content with higher heating value for plant biomass; to use the principle components analysis for the creation of an energetic development index and to adjust a linear model between energetic development index and higher heating value. Eight types of biomass were used. Three linear and nine multiple statistical models were adjusted. The best models were selected based on the significance of coefficients, adjusted determination coefficient, estimative standard error, coefficient of variation, linearity of parameters, normality, presence of heterocedasticity and lack of error correlation. The variance inflation factor was determined for linear multiple models. High correlation between the variables studied was found. Models 1, 3 and 11 were considered most adequate. Practical use of model 2 is not possible. Principle components analysis was efficient in obtaining an energetic development index of lignocellulosic residues and it may be used for solving multicollinearity found between variables considered.doi: 10.4336/2011.pfb.31.66.113Os objetivos do trabalho foram avaliar a correlação, ajustar e selecionar modelos estatísticos lineares simples e múltiplos entre os componentes elementares (carbono, hidrogênio e oxigênio) e o teor de cinzas com o poder calorífico superior da biomassa vegetal; utilizar a análise de componentes principais para a criação de um índice de desempenho energético e ajustar um modelo linear entre o índice de desempenho energético e o poder calorífico superior. Utilizaram-se oito tipos de biomassa. Foram ajustadas equações referentes a três modelos estatísticos lineares simples e nove múltiplos. Os melhores modelos foram selecionados com base na significância dos seus coeficientes, no coeficiente de determinação ajustado, no erro padrão da estimativa, no coeficiente de variação, na linearidade dos parâmetros, na normalidade, na presença de heterocedasticidade e ausência de autocorrelação dos erros. Para os modelos lineares múltiplos, determinou-se o fator de inflação de variância. Encontrou-se alta correlação entre as variáveis. Os modelos 1, 3 e 11 foram considerados os mais adequados. A utilização prática do modelo 2 foi impossibilitada. A análise de componentes principais foi eficiente na obtenção de um índice de desempenho energético dos resíduos lignocelulósicos e pode ser utilizada para contornar a multicolinearidade encontrada entre as variáveis consideradas.doi: 10.4336/2011.pfb.31.66.113Embrapa Florestas2011-05-23info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/mswordhttps://pfb.cnpf.embrapa.br/pfb/index.php/pfb/article/view/200Pesquisa Florestal Brasileira; v. 31 n. 66 (2011): abr./jun.; 113Pesquisa Florestal Brasileira; Vol. 31 No. 66 (2011): abr./jun.; 1131983-26051809-3647reponame:Pesquisa Florestal Brasileira (Online)instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa)instacron:EMBRAPAporhttps://pfb.cnpf.embrapa.br/pfb/index.php/pfb/article/view/200/211https://pfb.cnpf.embrapa.br/pfb/index.php/pfb/article/view/200/970Protásio, Thiago de PaulaBufalino, LinaTonoli, Gustavo Henrique DenzinCouto, Allan MottaTrugilho, Paulo FernandoGuimarães Júnior, Márioinfo:eu-repo/semantics/openAccess2017-04-28T13:06:42Zoai:pfb.cnpf.embrapa.br/pfb:article/200Revistahttps://pfb.cnpf.embrapa.br/pfb/index.php/pfb/PUBhttps://pfb.cnpf.embrapa.br/pfb/index.php/pfb/oaipfb@embrapa.br || revista.pfb@gmail.com || patricia.mattos@embrapa.br1983-26051809-3647opendoar:2017-04-28T13:06:42Pesquisa Florestal Brasileira (Online) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa)false |
dc.title.none.fl_str_mv |
Relation between higher heating value and elemental and mineral biomass plant components Relação entre o poder calorífico superior e os componentes elementares e minerais da biomassa vegetal |
title |
Relation between higher heating value and elemental and mineral biomass plant components |
spellingShingle |
Relation between higher heating value and elemental and mineral biomass plant components Protásio, Thiago de Paula Modelagem Regressão Correlação Seleção Modeling Regression Correlation Selection |
title_short |
Relation between higher heating value and elemental and mineral biomass plant components |
title_full |
Relation between higher heating value and elemental and mineral biomass plant components |
title_fullStr |
Relation between higher heating value and elemental and mineral biomass plant components |
title_full_unstemmed |
Relation between higher heating value and elemental and mineral biomass plant components |
title_sort |
Relation between higher heating value and elemental and mineral biomass plant components |
author |
Protásio, Thiago de Paula |
author_facet |
Protásio, Thiago de Paula Bufalino, Lina Tonoli, Gustavo Henrique Denzin Couto, Allan Motta Trugilho, Paulo Fernando Guimarães Júnior, Mário |
author_role |
author |
author2 |
Bufalino, Lina Tonoli, Gustavo Henrique Denzin Couto, Allan Motta Trugilho, Paulo Fernando Guimarães Júnior, Mário |
author2_role |
author author author author author |
dc.contributor.author.fl_str_mv |
Protásio, Thiago de Paula Bufalino, Lina Tonoli, Gustavo Henrique Denzin Couto, Allan Motta Trugilho, Paulo Fernando Guimarães Júnior, Mário |
dc.subject.por.fl_str_mv |
Modelagem Regressão Correlação Seleção Modeling Regression Correlation Selection |
topic |
Modelagem Regressão Correlação Seleção Modeling Regression Correlation Selection |
description |
The aims of this work were to evaluate the correlation, to adjust and select simple and multiple linear statistical models between elemental components (carbon, hydrogen and oxygen) and ash content with higher heating value for plant biomass; to use the principle components analysis for the creation of an energetic development index and to adjust a linear model between energetic development index and higher heating value. Eight types of biomass were used. Three linear and nine multiple statistical models were adjusted. The best models were selected based on the significance of coefficients, adjusted determination coefficient, estimative standard error, coefficient of variation, linearity of parameters, normality, presence of heterocedasticity and lack of error correlation. The variance inflation factor was determined for linear multiple models. High correlation between the variables studied was found. Models 1, 3 and 11 were considered most adequate. Practical use of model 2 is not possible. Principle components analysis was efficient in obtaining an energetic development index of lignocellulosic residues and it may be used for solving multicollinearity found between variables considered.doi: 10.4336/2011.pfb.31.66.113 |
publishDate |
2011 |
dc.date.none.fl_str_mv |
2011-05-23 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://pfb.cnpf.embrapa.br/pfb/index.php/pfb/article/view/200 |
url |
https://pfb.cnpf.embrapa.br/pfb/index.php/pfb/article/view/200 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
https://pfb.cnpf.embrapa.br/pfb/index.php/pfb/article/view/200/211 https://pfb.cnpf.embrapa.br/pfb/index.php/pfb/article/view/200/970 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf application/msword |
dc.publisher.none.fl_str_mv |
Embrapa Florestas |
publisher.none.fl_str_mv |
Embrapa Florestas |
dc.source.none.fl_str_mv |
Pesquisa Florestal Brasileira; v. 31 n. 66 (2011): abr./jun.; 113 Pesquisa Florestal Brasileira; Vol. 31 No. 66 (2011): abr./jun.; 113 1983-2605 1809-3647 reponame:Pesquisa Florestal Brasileira (Online) instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa) instacron:EMBRAPA |
instname_str |
Empresa Brasileira de Pesquisa Agropecuária (Embrapa) |
instacron_str |
EMBRAPA |
institution |
EMBRAPA |
reponame_str |
Pesquisa Florestal Brasileira (Online) |
collection |
Pesquisa Florestal Brasileira (Online) |
repository.name.fl_str_mv |
Pesquisa Florestal Brasileira (Online) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa) |
repository.mail.fl_str_mv |
pfb@embrapa.br || revista.pfb@gmail.com || patricia.mattos@embrapa.br |
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1783370932600963072 |