Biomethane production modelling from third-generation biomass

Autores
Córdoba, Verónica Elizabeth; Bavio, Marcela Alejandra; Acosta, Gerardo Gabriel
Año de publicación
2024
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Third-generation biomass constitutes a renewable energy source that could substitute fossil fuels. This study evaluated the biomethane potential (BMP) of Ulva sp., Codium sp. and Undaria pinnatifida through anaerobic digestion. The daily production of biomethane was evaluated using different models, including Modified Gompertz, Chen and Hashimoto, First-order, Transfer and Cone models, as well as an Artificial Neural Network (ANN) model. The experimental BMP was 0.17, 0.26, and 0.32 Nm 3 /kg VS for Ulva sp., Codium sp., and U pinnatifida, respectively. Among the non-linear regression models, the Transfer model (R2 > 0.9915) and the First-order model (R2> 0.9889) are the ones that best fit the experimental data. However, the ANN shows a better fit (R2>0.999 and RMSE<4.537) to the data compared to the non-linear regression models. Furthermore, ANN can capture the complexity of biological systems, allowing for more accurate and detailed modelling of the processes involved. The identification and optimization of the biomethane potential of macroalgae contribute to developing sustainable energy alternatives, offering a renewable energy source that could mitigate the environmental stress associated with traditional fossil fuels.
Fil: Córdoba, Verónica Elizabeth. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnologico Conicet - Tandil. Centro de Investigaciones En Fisica E Ingenieria del Centro de la Provincia de Buenos Aires. Sede Olavarria del Centro de Investifaciones En Fisica E Ingenieria del Centro de la Provincia de Buenos Aires | Universidad Nacional del Centro de la Pcia.de Bs.as.. Centro de Investigaciones En Fisica E Ingenieria del Centro de la Provincia de Buenos Aires. Sede Olavarria del Centro de Investifaciones En Fisica E Ingenieria del Centro de la Provincia de Buenos Aires.; Argentina
Fil: Bavio, Marcela Alejandra. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnologico Conicet - Tandil. Centro de Investigaciones En Fisica E Ingenieria del Centro de la Provincia de Buenos Aires. Sede Olavarria del Centro de Investifaciones En Fisica E Ingenieria del Centro de la Provincia de Buenos Aires | Universidad Nacional del Centro de la Pcia.de Bs.as.. Centro de Investigaciones En Fisica E Ingenieria del Centro de la Provincia de Buenos Aires. Sede Olavarria del Centro de Investifaciones En Fisica E Ingenieria del Centro de la Provincia de Buenos Aires.; Argentina
Fil: Acosta, Gerardo Gabriel. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnologico Conicet - Tandil. Centro de Investigaciones En Fisica E Ingenieria del Centro de la Provincia de Buenos Aires. Sede Olavarria del Centro de Investifaciones En Fisica E Ingenieria del Centro de la Provincia de Buenos Aires | Universidad Nacional del Centro de la Pcia.de Bs.as.. Centro de Investigaciones En Fisica E Ingenieria del Centro de la Provincia de Buenos Aires. Sede Olavarria del Centro de Investifaciones En Fisica E Ingenieria del Centro de la Provincia de Buenos Aires.; Argentina
Materia
ANAEROBIC DIGESTION
BIOPROCESSES MODELLING
BIOMETHANE
MACROALGAE
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/260045
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/260045
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Biomethane production modelling from third-generation biomassCórdoba, Verónica ElizabethBavio, Marcela AlejandraAcosta, Gerardo GabrielANAEROBIC DIGESTIONBIOPROCESSES MODELLINGBIOMETHANEMACROALGAEhttps://purl.org/becyt/ford/2.4https://purl.org/becyt/ford/2https://purl.org/becyt/ford/1.4https://purl.org/becyt/ford/1Third-generation biomass constitutes a renewable energy source that could substitute fossil fuels. This study evaluated the biomethane potential (BMP) of Ulva sp., Codium sp. and Undaria pinnatifida through anaerobic digestion. The daily production of biomethane was evaluated using different models, including Modified Gompertz, Chen and Hashimoto, First-order, Transfer and Cone models, as well as an Artificial Neural Network (ANN) model. The experimental BMP was 0.17, 0.26, and 0.32 Nm 3 /kg VS for Ulva sp., Codium sp., and U pinnatifida, respectively. Among the non-linear regression models, the Transfer model (R2 > 0.9915) and the First-order model (R2> 0.9889) are the ones that best fit the experimental data. However, the ANN shows a better fit (R2>0.999 and RMSE<4.537) to the data compared to the non-linear regression models. Furthermore, ANN can capture the complexity of biological systems, allowing for more accurate and detailed modelling of the processes involved. The identification and optimization of the biomethane potential of macroalgae contribute to developing sustainable energy alternatives, offering a renewable energy source that could mitigate the environmental stress associated with traditional fossil fuels.Fil: Córdoba, Verónica Elizabeth. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnologico Conicet - Tandil. Centro de Investigaciones En Fisica E Ingenieria del Centro de la Provincia de Buenos Aires. Sede Olavarria del Centro de Investifaciones En Fisica E Ingenieria del Centro de la Provincia de Buenos Aires | Universidad Nacional del Centro de la Pcia.de Bs.as.. Centro de Investigaciones En Fisica E Ingenieria del Centro de la Provincia de Buenos Aires. Sede Olavarria del Centro de Investifaciones En Fisica E Ingenieria del Centro de la Provincia de Buenos Aires.; ArgentinaFil: Bavio, Marcela Alejandra. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnologico Conicet - Tandil. Centro de Investigaciones En Fisica E Ingenieria del Centro de la Provincia de Buenos Aires. Sede Olavarria del Centro de Investifaciones En Fisica E Ingenieria del Centro de la Provincia de Buenos Aires | Universidad Nacional del Centro de la Pcia.de Bs.as.. Centro de Investigaciones En Fisica E Ingenieria del Centro de la Provincia de Buenos Aires. Sede Olavarria del Centro de Investifaciones En Fisica E Ingenieria del Centro de la Provincia de Buenos Aires.; ArgentinaFil: Acosta, Gerardo Gabriel. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnologico Conicet - Tandil. Centro de Investigaciones En Fisica E Ingenieria del Centro de la Provincia de Buenos Aires. Sede Olavarria del Centro de Investifaciones En Fisica E Ingenieria del Centro de la Provincia de Buenos Aires | Universidad Nacional del Centro de la Pcia.de Bs.as.. Centro de Investigaciones En Fisica E Ingenieria del Centro de la Provincia de Buenos Aires. Sede Olavarria del Centro de Investifaciones En Fisica E Ingenieria del Centro de la Provincia de Buenos Aires.; ArgentinaPergamon-Elsevier Science Ltd2024-11info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/260045Córdoba, Verónica Elizabeth; Bavio, Marcela Alejandra; Acosta, Gerardo Gabriel; Biomethane production modelling from third-generation biomass; Pergamon-Elsevier Science Ltd; Renewable Energy; 234; 11-2024; 1-380960-1481CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://linkinghub.elsevier.com/retrieve/pii/S0960148124012795info:eu-repo/semantics/altIdentifier/doi/10.1016/j.renene.2024.121211info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:43:43Zoai:ri.conicet.gov.ar:11336/260045instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-03 09:43:43.676CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Biomethane production modelling from third-generation biomass
title Biomethane production modelling from third-generation biomass
spellingShingle Biomethane production modelling from third-generation biomass
Córdoba, Verónica Elizabeth
ANAEROBIC DIGESTION
BIOPROCESSES MODELLING
BIOMETHANE
MACROALGAE
title_short Biomethane production modelling from third-generation biomass
title_full Biomethane production modelling from third-generation biomass
title_fullStr Biomethane production modelling from third-generation biomass
title_full_unstemmed Biomethane production modelling from third-generation biomass
title_sort Biomethane production modelling from third-generation biomass
dc.creator.none.fl_str_mv Córdoba, Verónica Elizabeth
Bavio, Marcela Alejandra
Acosta, Gerardo Gabriel
author Córdoba, Verónica Elizabeth
author_facet Córdoba, Verónica Elizabeth
Bavio, Marcela Alejandra
Acosta, Gerardo Gabriel
author_role author
author2 Bavio, Marcela Alejandra
Acosta, Gerardo Gabriel
author2_role author
author
dc.subject.none.fl_str_mv ANAEROBIC DIGESTION
BIOPROCESSES MODELLING
BIOMETHANE
MACROALGAE
topic ANAEROBIC DIGESTION
BIOPROCESSES MODELLING
BIOMETHANE
MACROALGAE
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.4
https://purl.org/becyt/ford/2
https://purl.org/becyt/ford/1.4
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Third-generation biomass constitutes a renewable energy source that could substitute fossil fuels. This study evaluated the biomethane potential (BMP) of Ulva sp., Codium sp. and Undaria pinnatifida through anaerobic digestion. The daily production of biomethane was evaluated using different models, including Modified Gompertz, Chen and Hashimoto, First-order, Transfer and Cone models, as well as an Artificial Neural Network (ANN) model. The experimental BMP was 0.17, 0.26, and 0.32 Nm 3 /kg VS for Ulva sp., Codium sp., and U pinnatifida, respectively. Among the non-linear regression models, the Transfer model (R2 > 0.9915) and the First-order model (R2> 0.9889) are the ones that best fit the experimental data. However, the ANN shows a better fit (R2>0.999 and RMSE<4.537) to the data compared to the non-linear regression models. Furthermore, ANN can capture the complexity of biological systems, allowing for more accurate and detailed modelling of the processes involved. The identification and optimization of the biomethane potential of macroalgae contribute to developing sustainable energy alternatives, offering a renewable energy source that could mitigate the environmental stress associated with traditional fossil fuels.
Fil: Córdoba, Verónica Elizabeth. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnologico Conicet - Tandil. Centro de Investigaciones En Fisica E Ingenieria del Centro de la Provincia de Buenos Aires. Sede Olavarria del Centro de Investifaciones En Fisica E Ingenieria del Centro de la Provincia de Buenos Aires | Universidad Nacional del Centro de la Pcia.de Bs.as.. Centro de Investigaciones En Fisica E Ingenieria del Centro de la Provincia de Buenos Aires. Sede Olavarria del Centro de Investifaciones En Fisica E Ingenieria del Centro de la Provincia de Buenos Aires.; Argentina
Fil: Bavio, Marcela Alejandra. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnologico Conicet - Tandil. Centro de Investigaciones En Fisica E Ingenieria del Centro de la Provincia de Buenos Aires. Sede Olavarria del Centro de Investifaciones En Fisica E Ingenieria del Centro de la Provincia de Buenos Aires | Universidad Nacional del Centro de la Pcia.de Bs.as.. Centro de Investigaciones En Fisica E Ingenieria del Centro de la Provincia de Buenos Aires. Sede Olavarria del Centro de Investifaciones En Fisica E Ingenieria del Centro de la Provincia de Buenos Aires.; Argentina
Fil: Acosta, Gerardo Gabriel. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnologico Conicet - Tandil. Centro de Investigaciones En Fisica E Ingenieria del Centro de la Provincia de Buenos Aires. Sede Olavarria del Centro de Investifaciones En Fisica E Ingenieria del Centro de la Provincia de Buenos Aires | Universidad Nacional del Centro de la Pcia.de Bs.as.. Centro de Investigaciones En Fisica E Ingenieria del Centro de la Provincia de Buenos Aires. Sede Olavarria del Centro de Investifaciones En Fisica E Ingenieria del Centro de la Provincia de Buenos Aires.; Argentina
description Third-generation biomass constitutes a renewable energy source that could substitute fossil fuels. This study evaluated the biomethane potential (BMP) of Ulva sp., Codium sp. and Undaria pinnatifida through anaerobic digestion. The daily production of biomethane was evaluated using different models, including Modified Gompertz, Chen and Hashimoto, First-order, Transfer and Cone models, as well as an Artificial Neural Network (ANN) model. The experimental BMP was 0.17, 0.26, and 0.32 Nm 3 /kg VS for Ulva sp., Codium sp., and U pinnatifida, respectively. Among the non-linear regression models, the Transfer model (R2 > 0.9915) and the First-order model (R2> 0.9889) are the ones that best fit the experimental data. However, the ANN shows a better fit (R2>0.999 and RMSE<4.537) to the data compared to the non-linear regression models. Furthermore, ANN can capture the complexity of biological systems, allowing for more accurate and detailed modelling of the processes involved. The identification and optimization of the biomethane potential of macroalgae contribute to developing sustainable energy alternatives, offering a renewable energy source that could mitigate the environmental stress associated with traditional fossil fuels.
publishDate 2024
dc.date.none.fl_str_mv 2024-11
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/260045
Córdoba, Verónica Elizabeth; Bavio, Marcela Alejandra; Acosta, Gerardo Gabriel; Biomethane production modelling from third-generation biomass; Pergamon-Elsevier Science Ltd; Renewable Energy; 234; 11-2024; 1-38
0960-1481
CONICET Digital
CONICET
url http://hdl.handle.net/11336/260045
identifier_str_mv Córdoba, Verónica Elizabeth; Bavio, Marcela Alejandra; Acosta, Gerardo Gabriel; Biomethane production modelling from third-generation biomass; Pergamon-Elsevier Science Ltd; Renewable Energy; 234; 11-2024; 1-38
0960-1481
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://linkinghub.elsevier.com/retrieve/pii/S0960148124012795
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.renene.2024.121211
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Pergamon-Elsevier Science Ltd
publisher.none.fl_str_mv Pergamon-Elsevier Science Ltd
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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