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 | Acesso ao texto completo restrito à biblioteca da Embrapa Gado de Leite. Para informações adicionais entre em contato com cnpgl.biblioteca@embrapa.br. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Gado de Leite. |
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Data corrente: |
22/09/2021 |
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Data da última atualização: |
22/09/2021 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
OLIVEIRA, A. C. L. de; RESENDE, M. de O.; SILVA, E. G. M.; RENATO, N. dos S.; MARTINS, M. A.; SEQUIEL, R.; MACHADO, J. C. |
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Afiliação: |
AUGUSTO CESAR LAVIOLA DE OLIVEIRA, Universidade Federal de Viçosa; MICHAEL DE OLIVEIRA RESENDE, Instituto Federal do Sudeste de Minas Gerais; ELIAS GABRIEL MAGALHAES SILVA, Universidade Federal do Paraná; NATALIA DOS SANTOS RENATO, Universidade Federal de Viçosa; MARCIO AREDES MARTINS, Universidade Federal de Viçosa; RODRIGO SEQUIEL, Universidade Federal do Paraná; JUAREZ CAMPOLINA MACHADO, CNPGL. |
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Título: |
Evaluation and optimization of electricity generation through manure obtained from animal production chains in two Brazilian mesoregions. |
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Ano de publicação: |
2021 |
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Fonte/Imprenta: |
Journal of Cleaner Production, v. 316, 128270, 2021. |
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DOI: |
https://doi.org/10.1016/j.jclepro.2021.128270 |
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Idioma: |
Inglês |
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Conteúdo: |
Biogas, obtained from the anaerobic digestion of animal waste, is being increasingly used worldwide, and is generating significant economic and environmental benefits. It is ideal for regions that are mainly involved in agricultural activities and animal husbandry. Thus, the objective of this study was to estimate the energy potential and to optimize the allocation of electric power generation plants that use biogas in the mesoregions of Minas Gerais, Brazil, based on the distribution generation standards and availability of resources. To calculate the energy potential, data from governmental platforms were used, referring to the number of animals in the study regions as well as theoretical conversion models in the literature. To optimize the allocation of biogas plants, QGis software and a constructive algorithm that was developed for this study were used to build maps that demonstrated the optimal allocation of these plants, based on their potential and geographical location. To convert biomethane into electricity using a combustion engine (28% efficiency), energy potentials of 791.8 and 218.3 GWh yr− 1 and 21 and 6 plants located in Zona da Mata and Campo das Vertentes, respectively, were estimated. With respect to the combine cycle (65% efficiency), energy potentials of 1838.2 and 506.7 GWh yr− 1 and 47 and 14 plants in Zona da Mata and Campo das Vertentes, respectively, were estimated. New plants were calculated to increase the energy generation in the evaluated regions by 8?19%. The average cost of energy generation using stationary engine and combined cycle were USD 0.2384 kWh− 1 and USD 0.0974 kWh− 1, respectively. A reduction in CO2 emissions per inhabitant is expected for the assessed region, ranging between 6.05% and 14.52%. A significant portion of the state?s rural electricity consumption could be supported by these new ventures, leading to an increase in energy security as well as reliability in meeting demand. In addition, the developed optimization algorithm can be used as a decision-making tool to determine locations for the construction of biogas plants in other regions of Brazil. MenosBiogas, obtained from the anaerobic digestion of animal waste, is being increasingly used worldwide, and is generating significant economic and environmental benefits. It is ideal for regions that are mainly involved in agricultural activities and animal husbandry. Thus, the objective of this study was to estimate the energy potential and to optimize the allocation of electric power generation plants that use biogas in the mesoregions of Minas Gerais, Brazil, based on the distribution generation standards and availability of resources. To calculate the energy potential, data from governmental platforms were used, referring to the number of animals in the study regions as well as theoretical conversion models in the literature. To optimize the allocation of biogas plants, QGis software and a constructive algorithm that was developed for this study were used to build maps that demonstrated the optimal allocation of these plants, based on their potential and geographical location. To convert biomethane into electricity using a combustion engine (28% efficiency), energy potentials of 791.8 and 218.3 GWh yr− 1 and 21 and 6 plants located in Zona da Mata and Campo das Vertentes, respectively, were estimated. With respect to the combine cycle (65% efficiency), energy potentials of 1838.2 and 506.7 GWh yr− 1 and 47 and 14 plants in Zona da Mata and Campo das Vertentes, respectively, were estimated. New plants were calculated to increase the energy generation in the evalu... Mostrar Tudo |
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Palavras-Chave: |
Alocação de planta; Otimização; Plant allocation; Sustentabilidade. |
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Thesagro: |
Biogás; Biomassa; Bovino; Produção Animal. |
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Thesaurus Nal: |
Biomass; System optimization. |
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Categoria do assunto: |
L Ciência Animal e Produtos de Origem Animal |
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Marc: |
LEADER 03158naa a2200325 a 4500
001 2134654
005 2021-09-22
008 2021 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1016/j.jclepro.2021.128270$2DOI
100 1 $aOLIVEIRA, A. C. L. de
245 $aEvaluation and optimization of electricity generation through manure obtained from animal production chains in two Brazilian mesoregions.$h[electronic resource]
260 $c2021
520 $aBiogas, obtained from the anaerobic digestion of animal waste, is being increasingly used worldwide, and is generating significant economic and environmental benefits. It is ideal for regions that are mainly involved in agricultural activities and animal husbandry. Thus, the objective of this study was to estimate the energy potential and to optimize the allocation of electric power generation plants that use biogas in the mesoregions of Minas Gerais, Brazil, based on the distribution generation standards and availability of resources. To calculate the energy potential, data from governmental platforms were used, referring to the number of animals in the study regions as well as theoretical conversion models in the literature. To optimize the allocation of biogas plants, QGis software and a constructive algorithm that was developed for this study were used to build maps that demonstrated the optimal allocation of these plants, based on their potential and geographical location. To convert biomethane into electricity using a combustion engine (28% efficiency), energy potentials of 791.8 and 218.3 GWh yr− 1 and 21 and 6 plants located in Zona da Mata and Campo das Vertentes, respectively, were estimated. With respect to the combine cycle (65% efficiency), energy potentials of 1838.2 and 506.7 GWh yr− 1 and 47 and 14 plants in Zona da Mata and Campo das Vertentes, respectively, were estimated. New plants were calculated to increase the energy generation in the evaluated regions by 8?19%. The average cost of energy generation using stationary engine and combined cycle were USD 0.2384 kWh− 1 and USD 0.0974 kWh− 1, respectively. A reduction in CO2 emissions per inhabitant is expected for the assessed region, ranging between 6.05% and 14.52%. A significant portion of the state?s rural electricity consumption could be supported by these new ventures, leading to an increase in energy security as well as reliability in meeting demand. In addition, the developed optimization algorithm can be used as a decision-making tool to determine locations for the construction of biogas plants in other regions of Brazil.
650 $aBiomass
650 $aSystem optimization
650 $aBiogás
650 $aBiomassa
650 $aBovino
650 $aProdução Animal
653 $aAlocação de planta
653 $aOtimização
653 $aPlant allocation
653 $aSustentabilidade
700 1 $aRESENDE, M. de O.
700 1 $aSILVA, E. G. M.
700 1 $aRENATO, N. dos S.
700 1 $aMARTINS, M. A.
700 1 $aSEQUIEL, R.
700 1 $aMACHADO, J. C.
773 $tJournal of Cleaner Production$gv. 316, 128270, 2021.
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Registro original: |
Embrapa Gado de Leite (CNPGL) |
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Cutter |
Registro |
Volume |
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URL |
Voltar
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Registro Completo
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Biblioteca(s): |
Embrapa Acre. |
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Data corrente: |
21/12/2022 |
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Data da última atualização: |
21/12/2022 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 2 |
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Autoria: |
VERAS, H. F. P.; FERREIRA, M. P.; CUNHA NETO, E. M. da; FIGUEIREDO, E. O.; DALLA CORTE, A. P.; SANQUETTA, C. R. |
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Afiliação: |
HUDSON FRANKLIN PESSOA VERAS, UNIVERSIDADE FEDERAL DO PARANÁ; MATHEUS PINHEIRO FERREIRA, INSTITUTO MILITAR DE ENGENHARIA; ERNANDES MACEDO DA CUNHA NETO, UNIVERSIDADE FEDERAL DO PARANÁ; EVANDRO ORFANO FIGUEIREDO, CPAF-AC; ANA PAULA DALLA CORTE, UNIVERSIDADE FEDERAL DO PARANÁ; CARLOS ROBERTO SANQUETTA, UNIVERSIDADE FEDERAL DO PARANÁ. |
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Título: |
Fusing multi-season UAS images with convolutional neural networks to map tree species in Amazonian forests. |
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Ano de publicação: |
2022 |
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Fonte/Imprenta: |
Ecological Informatics, v. 71, 101815, 2022. |
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ISSN: |
1574-9541 |
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DOI: |
https://doi.org/10.1016/j.ecoinf.2022.101815 |
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Idioma: |
Inglês |
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Conteúdo: |
Remote sensing images obtained by unoccupied aircraft systems (UAS) across different seasons enabled capturing of species-specific phenological patterns of tropical trees. The application of UAS multi-season images to classify tropical tree species is still poorly understood. In this study, we used RGB images from different seasons obtained by a low-cost UAS and convolutional neural networks (CNNs) to map tree species in an Amazonian forest. Individual tree crowns (ITC) were outlined in the UAS images and identified to the species level using forest inventory data. The CNN model was trained with images obtained in February, May, August, and November. The classification accuracy in the rainy season (November and February) was higher than in the dry season (May and August). Fusing images from multiple seasons improved the average accuracy of tree species classification by up to 21.1 percentage points, reaching 90.5%. The CNN model can learn species-specific phenological characteristics that impact the classification accuracy, such as leaf fall in the dry season, which highlights its potential to discriminate species in various conditions. We produced high-quality individual tree crown maps of the species using a post-processing procedure. The combination of multi-season UAS images and CNNs has the potential to map tree species in the Amazon, providing valuable insights for forest management and conservation initiatives. |
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Palavras-Chave: |
Acre; Amazonia Occidental; Amazônia Ocidental; Bosques experimentales; Bosques tropicales; Embrapa Acre; Fusão de imagens; Identificación de especies; Imagem multitemporada; Imagem RGB; Mapeamento de espécies; Modelo CNN; Rio Branco (AC); Teledetección; Western Amazon. |
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Thesagro: |
Campo Experimental; Espécie Nativa; Fenologia; Floresta Tropical; Identificação; Sensoriamento Remoto. |
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Thesaurus NAL: |
Experimental forests; Phenology; Remote sensing; Species identification; Tropical forests. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1150165/1/27406.pdf
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Marc: |
LEADER 02966naa a2200517 a 4500
001 2150165
005 2022-12-21
008 2022 bl uuuu u00u1 u #d
022 $a1574-9541
024 7 $ahttps://doi.org/10.1016/j.ecoinf.2022.101815$2DOI
100 1 $aVERAS, H. F. P.
245 $aFusing multi-season UAS images with convolutional neural networks to map tree species in Amazonian forests.$h[electronic resource]
260 $c2022
520 $aRemote sensing images obtained by unoccupied aircraft systems (UAS) across different seasons enabled capturing of species-specific phenological patterns of tropical trees. The application of UAS multi-season images to classify tropical tree species is still poorly understood. In this study, we used RGB images from different seasons obtained by a low-cost UAS and convolutional neural networks (CNNs) to map tree species in an Amazonian forest. Individual tree crowns (ITC) were outlined in the UAS images and identified to the species level using forest inventory data. The CNN model was trained with images obtained in February, May, August, and November. The classification accuracy in the rainy season (November and February) was higher than in the dry season (May and August). Fusing images from multiple seasons improved the average accuracy of tree species classification by up to 21.1 percentage points, reaching 90.5%. The CNN model can learn species-specific phenological characteristics that impact the classification accuracy, such as leaf fall in the dry season, which highlights its potential to discriminate species in various conditions. We produced high-quality individual tree crown maps of the species using a post-processing procedure. The combination of multi-season UAS images and CNNs has the potential to map tree species in the Amazon, providing valuable insights for forest management and conservation initiatives.
650 $aExperimental forests
650 $aPhenology
650 $aRemote sensing
650 $aSpecies identification
650 $aTropical forests
650 $aCampo Experimental
650 $aEspécie Nativa
650 $aFenologia
650 $aFloresta Tropical
650 $aIdentificação
650 $aSensoriamento Remoto
653 $aAcre
653 $aAmazonia Occidental
653 $aAmazônia Ocidental
653 $aBosques experimentales
653 $aBosques tropicales
653 $aEmbrapa Acre
653 $aFusão de imagens
653 $aIdentificación de especies
653 $aImagem multitemporada
653 $aImagem RGB
653 $aMapeamento de espécies
653 $aModelo CNN
653 $aRio Branco (AC)
653 $aTeledetección
653 $aWestern Amazon
700 1 $aFERREIRA, M. P.
700 1 $aCUNHA NETO, E. M. da
700 1 $aFIGUEIREDO, E. O.
700 1 $aDALLA CORTE, A. P.
700 1 $aSANQUETTA, C. R.
773 $tEcological Informatics$gv. 71, 101815, 2022.
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