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| 1. |  | QUEIROZ, A. A.; MARINHO, J. P.; CARANHATO, A. L. H.; BARBOSA, D. A.; MOLINARI, M.; ALMEIDA, L. C. M.; SANTOS, E. D.; SOUZA, K. R.; ANDREATA, E. C.; MERTZ-HENNING, L. M.; NEPOMUCENO, A. L. Avaliação de germinação dos genótipos de soja sob diferentes potenciais osmóticos induzidos por polietilenoglicol 8000. In: JORNADA ACADÊMICA DA EMBRAPA SOJA, 12., 2017, Londrina. Resumos expandidos... Londrina: Embrapa Soja, 2017. p. 177-184.| Biblioteca(s): Embrapa Soja. |
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| Registros recuperados : 1 | |
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Registro Completo
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Biblioteca(s): |
Embrapa Meio Ambiente. |
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Data corrente: |
03/12/2019 |
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Data da última atualização: |
05/12/2019 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
B - 1 |
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Autoria: |
MOREIRA, B. R. de A.; VIANA, R. da S.; LISBOA, L. A. M.; LOPES, P. R. M.; FIGUEIREDO, P. A. M. de; RAMOS, S. B.; BONINI, C. S. B.; TRINDADE, V. D. R.; ANDRADE, M. G. de O.; MAY, A. |
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Afiliação: |
BRUNO RAFAEL DE ALMEIRA MOREIRA, FEIS-UNESP; RONALDO DA SILVA VIANA, FCAT-UNESP; LUCAS APARECIDO MANZANI LISBOA, FCAT-UNESP; PAULO RENATO MATOS LOPES, FCAT-UNESP; PAULO ALEXANDRE MONTEIRO DE FIGUEIREDO, FCAT-UNESP; SÉRGIO BISPO RAMOS, FCAT-UNESP; CAROLINA DOS SANTOS BATISTA BONINI, FCAT-UNESP; V D R TRINDADE, UNESP; M G O ANDRADE, FEIS-UNESP; ANDRE MAY, CNPMA. |
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Título: |
Classifying hybrids of energy cane for production of bioethanol and cogeneration of biomass-based electricity by principal component analysis-linked fuzzy c-means clustering algorithm. |
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Ano de publicação: |
2019 |
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Fonte/Imprenta: |
Journal of Agricultural Science, Richmond Hill, v. 11, n. 14, p. 246-253, 2019. |
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ISSN: |
1916-9760 |
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Idioma: |
Inglês |
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Conteúdo: |
Abstract: The biggest challenge facing in sugar-energy plants is to move towards the biorefinery concept, without threatening the environment and health. Energy cane is the state-of-the-art of smart energy crops to provide suitable whole-raw material to produce upgraded biofuels, dehydrated alcohol for transportation, refined sugar, yeast- fermented alcoholic beverages, soft drinks, silage and high quality fodder, as well as to cogenerate heat and bioelectricity from burnt lignocellulose. We, accordingly, present fuzzy c-means (FCM) clustering algorithm interconnected with principal component analysis (PCA) as powerful exploratory data analysis tool to wisely classify hybrids of energy cane for production of first-generation ethanol and cogeneration of heat and bioelectricity. From the orthogonally-rotated factorial map, fuzzy cluster I aggregated the hybrids VX12-0277, VX12-1191, VX12-1356 and VX12-1658 composed of higher contents of soluble solids and sucrose, and larger productive yields of fermentable sugars. These parameters correlated with the X-axis component referring to technological quality of cane juice. Fuzzy cluster III aggregated the hybrids VX12-0180 and VX12-1022 consisted of higher fiber content. This parameter correlated with the Y-axis component referring to physicochemical quality of lignocellulose. From the PCA-FCM methodology, the conclusion is, therefore, hybrids from fuzzy cluster I prove to be type I energy cane (higher sucrose to fiber ratio) and could serve as energy supply pathways to produce bioethanol, while the hybrids from fuzzy cluster III are type II energy cane (lower sucrose to fiber ratio), denoting potential as higher fiber yield biomass sources to feed cogeneration of heat and bioelectricity in high temperature and pressure furnace-boiler system. MenosAbstract: The biggest challenge facing in sugar-energy plants is to move towards the biorefinery concept, without threatening the environment and health. Energy cane is the state-of-the-art of smart energy crops to provide suitable whole-raw material to produce upgraded biofuels, dehydrated alcohol for transportation, refined sugar, yeast- fermented alcoholic beverages, soft drinks, silage and high quality fodder, as well as to cogenerate heat and bioelectricity from burnt lignocellulose. We, accordingly, present fuzzy c-means (FCM) clustering algorithm interconnected with principal component analysis (PCA) as powerful exploratory data analysis tool to wisely classify hybrids of energy cane for production of first-generation ethanol and cogeneration of heat and bioelectricity. From the orthogonally-rotated factorial map, fuzzy cluster I aggregated the hybrids VX12-0277, VX12-1191, VX12-1356 and VX12-1658 composed of higher contents of soluble solids and sucrose, and larger productive yields of fermentable sugars. These parameters correlated with the X-axis component referring to technological quality of cane juice. Fuzzy cluster III aggregated the hybrids VX12-0180 and VX12-1022 consisted of higher fiber content. This parameter correlated with the Y-axis component referring to physicochemical quality of lignocellulose. From the PCA-FCM methodology, the conclusion is, therefore, hybrids from fuzzy cluster I prove to be type I energy cane (higher sucrose to fiber ratio) and co... Mostrar Tudo |
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Palavras-Chave: |
Alternative clean energy sources; Exploratory data analysis; FCM algorithm; Fiber-rich biomass; PCA. |
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Thesagro: |
Análise de Dados; Bioenergia; Biomassa; Cana de Açúcar; Etanol; Hibrido. |
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Thesaurus NAL: |
Bioenergy; Biomass; Data analysis; Ethanol; Fuzzy logic; Hybrids; Sugarcane. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/206044/1/May-Classifying-Hybrids-2019.pdf
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Marc: |
LEADER 03177naa a2200457 a 4500
001 2115795
005 2019-12-05
008 2019 bl uuuu u00u1 u #d
022 $a1916-9760
100 1 $aMOREIRA, B. R. de A.
245 $aClassifying hybrids of energy cane for production of bioethanol and cogeneration of biomass-based electricity by principal component analysis-linked fuzzy c-means clustering algorithm.$h[electronic resource]
260 $c2019
520 $aAbstract: The biggest challenge facing in sugar-energy plants is to move towards the biorefinery concept, without threatening the environment and health. Energy cane is the state-of-the-art of smart energy crops to provide suitable whole-raw material to produce upgraded biofuels, dehydrated alcohol for transportation, refined sugar, yeast- fermented alcoholic beverages, soft drinks, silage and high quality fodder, as well as to cogenerate heat and bioelectricity from burnt lignocellulose. We, accordingly, present fuzzy c-means (FCM) clustering algorithm interconnected with principal component analysis (PCA) as powerful exploratory data analysis tool to wisely classify hybrids of energy cane for production of first-generation ethanol and cogeneration of heat and bioelectricity. From the orthogonally-rotated factorial map, fuzzy cluster I aggregated the hybrids VX12-0277, VX12-1191, VX12-1356 and VX12-1658 composed of higher contents of soluble solids and sucrose, and larger productive yields of fermentable sugars. These parameters correlated with the X-axis component referring to technological quality of cane juice. Fuzzy cluster III aggregated the hybrids VX12-0180 and VX12-1022 consisted of higher fiber content. This parameter correlated with the Y-axis component referring to physicochemical quality of lignocellulose. From the PCA-FCM methodology, the conclusion is, therefore, hybrids from fuzzy cluster I prove to be type I energy cane (higher sucrose to fiber ratio) and could serve as energy supply pathways to produce bioethanol, while the hybrids from fuzzy cluster III are type II energy cane (lower sucrose to fiber ratio), denoting potential as higher fiber yield biomass sources to feed cogeneration of heat and bioelectricity in high temperature and pressure furnace-boiler system.
650 $aBioenergy
650 $aBiomass
650 $aData analysis
650 $aEthanol
650 $aFuzzy logic
650 $aHybrids
650 $aSugarcane
650 $aAnálise de Dados
650 $aBioenergia
650 $aBiomassa
650 $aCana de Açúcar
650 $aEtanol
650 $aHibrido
653 $aAlternative clean energy sources
653 $aExploratory data analysis
653 $aFCM algorithm
653 $aFiber-rich biomass
653 $aPCA
700 1 $aVIANA, R. da S.
700 1 $aLISBOA, L. A. M.
700 1 $aLOPES, P. R. M.
700 1 $aFIGUEIREDO, P. A. M. de
700 1 $aRAMOS, S. B.
700 1 $aBONINI, C. S. B.
700 1 $aTRINDADE, V. D. R.
700 1 $aANDRADE, M. G. de O.
700 1 $aMAY, A.
773 $tJournal of Agricultural Science, Richmond Hill$gv. 11, n. 14, p. 246-253, 2019.
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Embrapa Meio Ambiente (CNPMA) |
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