Assessment of plant biomass for pellet production using multivariate statistics (PCA and HCA)
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1016/j.renene.2019.02.103 http://hdl.handle.net/11449/190160 |
Resumo: | Multivariate statistics can be a powerful tool in the assessment of energy properties of lignocellulosic materials and it is fundamental to estimate the theoretical, technical and economic potentials of these biomasses for bioenergy production. In this research, it was used to select the most favorable vegetable biomass for the production of biofuel pellets, through two techniques: Hierarchical Clustering Agglomerative and Principal Components. Six types of biomasses (pinus wood, eucalyptus wood, sugarcane bagasse, bamboo, sorghum, and elephant grass) and three blends were used. The immediate, elemental and thermochemical analyzes provided 16 variables of each of the 9 types of pellets. The dendrogram highlighted the group of forest biomass as the most suitable for the production of pellets and the principal component factors produced two bioenergetic indicators; one of general performance and other of combustibility. The forest biomass pellets was highlighted as potential for the production of biofuel pellets because they have energy properties with low levels of ash (0.54%) and nitrogen (0.83%), associated with high fixed carbon content (20.89%) and higher heating value (20.71 MJ kg−1), as well a higher energy density (13.95 GJ m−3). The multivariate analysis was efficient and can be used to classify lignocellulosic materials. |
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Assessment of plant biomass for pellet production using multivariate statistics (PCA and HCA)Agro-pelletsChemical compositionEnergy cropsFuel propertiesRenewable energyWood pelletsMultivariate statistics can be a powerful tool in the assessment of energy properties of lignocellulosic materials and it is fundamental to estimate the theoretical, technical and economic potentials of these biomasses for bioenergy production. In this research, it was used to select the most favorable vegetable biomass for the production of biofuel pellets, through two techniques: Hierarchical Clustering Agglomerative and Principal Components. Six types of biomasses (pinus wood, eucalyptus wood, sugarcane bagasse, bamboo, sorghum, and elephant grass) and three blends were used. The immediate, elemental and thermochemical analyzes provided 16 variables of each of the 9 types of pellets. The dendrogram highlighted the group of forest biomass as the most suitable for the production of pellets and the principal component factors produced two bioenergetic indicators; one of general performance and other of combustibility. The forest biomass pellets was highlighted as potential for the production of biofuel pellets because they have energy properties with low levels of ash (0.54%) and nitrogen (0.83%), associated with high fixed carbon content (20.89%) and higher heating value (20.71 MJ kg−1), as well a higher energy density (13.95 GJ m−3). The multivariate analysis was efficient and can be used to classify lignocellulosic materials.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)São Paulo State University (UNESP), Ariberto Per Cunha, 333São Paulo State University (UNESP), Geraldo Alckmin, 519Federal Rural University of Amazon (UFRA), 275, Km 13São Paulo State University (UNESP), Ariberto Per Cunha, 333São Paulo State University (UNESP), Geraldo Alckmin, 519Universidade Estadual Paulista (Unesp)Federal Rural University of Amazon (UFRA)Garcia, Dorival Pinheiro [UNESP]Caraschi, José Cláudio [UNESP]Ventorim, Gustavo [UNESP]Vieira, Fábio Henrique Antunes [UNESP]de Paula Protásio, Thiago2019-10-06T17:04:17Z2019-10-06T17:04:17Z2019-08-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article796-805http://dx.doi.org/10.1016/j.renene.2019.02.103Renewable Energy, v. 139, p. 796-805.1879-06820960-1481http://hdl.handle.net/11449/19016010.1016/j.renene.2019.02.1032-s2.0-85062348672Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengRenewable Energyinfo:eu-repo/semantics/openAccess2021-10-23T00:00:15Zoai:repositorio.unesp.br:11449/190160Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T20:57:57.046969Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Assessment of plant biomass for pellet production using multivariate statistics (PCA and HCA) |
title |
Assessment of plant biomass for pellet production using multivariate statistics (PCA and HCA) |
spellingShingle |
Assessment of plant biomass for pellet production using multivariate statistics (PCA and HCA) Garcia, Dorival Pinheiro [UNESP] Agro-pellets Chemical composition Energy crops Fuel properties Renewable energy Wood pellets |
title_short |
Assessment of plant biomass for pellet production using multivariate statistics (PCA and HCA) |
title_full |
Assessment of plant biomass for pellet production using multivariate statistics (PCA and HCA) |
title_fullStr |
Assessment of plant biomass for pellet production using multivariate statistics (PCA and HCA) |
title_full_unstemmed |
Assessment of plant biomass for pellet production using multivariate statistics (PCA and HCA) |
title_sort |
Assessment of plant biomass for pellet production using multivariate statistics (PCA and HCA) |
author |
Garcia, Dorival Pinheiro [UNESP] |
author_facet |
Garcia, Dorival Pinheiro [UNESP] Caraschi, José Cláudio [UNESP] Ventorim, Gustavo [UNESP] Vieira, Fábio Henrique Antunes [UNESP] de Paula Protásio, Thiago |
author_role |
author |
author2 |
Caraschi, José Cláudio [UNESP] Ventorim, Gustavo [UNESP] Vieira, Fábio Henrique Antunes [UNESP] de Paula Protásio, Thiago |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) Federal Rural University of Amazon (UFRA) |
dc.contributor.author.fl_str_mv |
Garcia, Dorival Pinheiro [UNESP] Caraschi, José Cláudio [UNESP] Ventorim, Gustavo [UNESP] Vieira, Fábio Henrique Antunes [UNESP] de Paula Protásio, Thiago |
dc.subject.por.fl_str_mv |
Agro-pellets Chemical composition Energy crops Fuel properties Renewable energy Wood pellets |
topic |
Agro-pellets Chemical composition Energy crops Fuel properties Renewable energy Wood pellets |
description |
Multivariate statistics can be a powerful tool in the assessment of energy properties of lignocellulosic materials and it is fundamental to estimate the theoretical, technical and economic potentials of these biomasses for bioenergy production. In this research, it was used to select the most favorable vegetable biomass for the production of biofuel pellets, through two techniques: Hierarchical Clustering Agglomerative and Principal Components. Six types of biomasses (pinus wood, eucalyptus wood, sugarcane bagasse, bamboo, sorghum, and elephant grass) and three blends were used. The immediate, elemental and thermochemical analyzes provided 16 variables of each of the 9 types of pellets. The dendrogram highlighted the group of forest biomass as the most suitable for the production of pellets and the principal component factors produced two bioenergetic indicators; one of general performance and other of combustibility. The forest biomass pellets was highlighted as potential for the production of biofuel pellets because they have energy properties with low levels of ash (0.54%) and nitrogen (0.83%), associated with high fixed carbon content (20.89%) and higher heating value (20.71 MJ kg−1), as well a higher energy density (13.95 GJ m−3). The multivariate analysis was efficient and can be used to classify lignocellulosic materials. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-10-06T17:04:17Z 2019-10-06T17:04:17Z 2019-08-01 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1016/j.renene.2019.02.103 Renewable Energy, v. 139, p. 796-805. 1879-0682 0960-1481 http://hdl.handle.net/11449/190160 10.1016/j.renene.2019.02.103 2-s2.0-85062348672 |
url |
http://dx.doi.org/10.1016/j.renene.2019.02.103 http://hdl.handle.net/11449/190160 |
identifier_str_mv |
Renewable Energy, v. 139, p. 796-805. 1879-0682 0960-1481 10.1016/j.renene.2019.02.103 2-s2.0-85062348672 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Renewable Energy |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
796-805 |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808129268949975040 |