Recurrent neural network modeling applied to expanded bed adsorption chromatography of chitosanases produced by Paenibacillus ehimensis

Detalhes bibliográficos
Autor(a) principal: Souza, Domingos Fabiano de Santana
Data de Publicação: 2017
Outros Autores: Padilha, Carlos Eduardo de Araújo, Padilha, Carlos Alberto de Araújo, Oliveira, Jackson Araújo de, Macedo, Gorete Ribeiro de, Santos, Everaldo Silvino dos
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UFRN
Texto Completo: https://repositorio.ufrn.br/handle/123456789/45192
Resumo: Nonlinear autoregressive networks with external input (NARX) and multilayer perceptron (MLP) has been used to predict the activity and protein content for flow-through, washing and elution steps during expanded bed adsorption chromatography of chitosanases produced by Paenibacillus ehimensis. Bed expansion as well as the influence of particulatecontaining feedstock study showed a stable bed operation without significant impact on the yield as well as purification factor when the cells were fed to the column. Also, NARXs showed a better performance to predict the chitosanolytic activity as well as total protein than MLPs
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spelling Souza, Domingos Fabiano de SantanaPadilha, Carlos Eduardo de AraújoPadilha, Carlos Alberto de AraújoOliveira, Jackson Araújo deMacedo, Gorete Ribeiro deSantos, Everaldo Silvino dos2021-12-06T18:18:18Z2021-12-06T18:18:18Z2017-01PADILHA, Carlos Eduardo de Araújo; PADILHA, Carlos Alberto de Araújo; SOUZA, Domingos Fabiano de Santana; OLIVEIRA, Jackson Araújo de; MACEDO, Gorete Ribeiro de; SANTOS, Everaldo Silvino dos. Recurrent neural network modeling applied to expanded bed adsorption chromatography of chitosanases produced by Paenibacillus ehimensis. Chemical Engineering Research And Design, [S.L.], v. 117, p. 24-33, jan. 2017. Elsevier BV. http://dx.doi.org/10.1016/j.cherd.2016.09.022. Disponível em: https://www.sciencedirect.com/science/article/pii/S0263876216303070?via%3Dihub. Acesso em: 05 nov. 2021.0263-8762https://repositorio.ufrn.br/handle/123456789/4519210.1016/j.cherd.2016.09.022ElsevierAdsorptionDownstream processingEnzyme activityChitosanasesModelingNARXRecurrent neural network modeling applied to expanded bed adsorption chromatography of chitosanases produced by Paenibacillus ehimensisinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleNonlinear autoregressive networks with external input (NARX) and multilayer perceptron (MLP) has been used to predict the activity and protein content for flow-through, washing and elution steps during expanded bed adsorption chromatography of chitosanases produced by Paenibacillus ehimensis. Bed expansion as well as the influence of particulatecontaining feedstock study showed a stable bed operation without significant impact on the yield as well as purification factor when the cells were fed to the column. Also, NARXs showed a better performance to predict the chitosanolytic activity as well as total protein than MLPsengreponame:Repositório Institucional da UFRNinstname:Universidade Federal do Rio Grande do Norte (UFRN)instacron:UFRNinfo:eu-repo/semantics/openAccessCC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8914https://repositorio.ufrn.br/bitstream/123456789/45192/2/license_rdf4d2950bda3d176f570a9f8b328dfbbefMD52LICENSElicense.txtlicense.txttext/plain; charset=utf-81484https://repositorio.ufrn.br/bitstream/123456789/45192/3/license.txte9597aa2854d128fd968be5edc8a28d9MD53123456789/451922023-02-01 17:22:09.458oai:https://repositorio.ufrn.br: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Repositório de PublicaçõesPUBhttp://repositorio.ufrn.br/oai/opendoar:2023-02-01T20:22:09Repositório Institucional da UFRN - Universidade Federal do Rio Grande do Norte (UFRN)false
dc.title.pt_BR.fl_str_mv Recurrent neural network modeling applied to expanded bed adsorption chromatography of chitosanases produced by Paenibacillus ehimensis
title Recurrent neural network modeling applied to expanded bed adsorption chromatography of chitosanases produced by Paenibacillus ehimensis
spellingShingle Recurrent neural network modeling applied to expanded bed adsorption chromatography of chitosanases produced by Paenibacillus ehimensis
Souza, Domingos Fabiano de Santana
Adsorption
Downstream processing
Enzyme activity
Chitosanases
Modeling
NARX
title_short Recurrent neural network modeling applied to expanded bed adsorption chromatography of chitosanases produced by Paenibacillus ehimensis
title_full Recurrent neural network modeling applied to expanded bed adsorption chromatography of chitosanases produced by Paenibacillus ehimensis
title_fullStr Recurrent neural network modeling applied to expanded bed adsorption chromatography of chitosanases produced by Paenibacillus ehimensis
title_full_unstemmed Recurrent neural network modeling applied to expanded bed adsorption chromatography of chitosanases produced by Paenibacillus ehimensis
title_sort Recurrent neural network modeling applied to expanded bed adsorption chromatography of chitosanases produced by Paenibacillus ehimensis
author Souza, Domingos Fabiano de Santana
author_facet Souza, Domingos Fabiano de Santana
Padilha, Carlos Eduardo de Araújo
Padilha, Carlos Alberto de Araújo
Oliveira, Jackson Araújo de
Macedo, Gorete Ribeiro de
Santos, Everaldo Silvino dos
author_role author
author2 Padilha, Carlos Eduardo de Araújo
Padilha, Carlos Alberto de Araújo
Oliveira, Jackson Araújo de
Macedo, Gorete Ribeiro de
Santos, Everaldo Silvino dos
author2_role author
author
author
author
author
dc.contributor.author.fl_str_mv Souza, Domingos Fabiano de Santana
Padilha, Carlos Eduardo de Araújo
Padilha, Carlos Alberto de Araújo
Oliveira, Jackson Araújo de
Macedo, Gorete Ribeiro de
Santos, Everaldo Silvino dos
dc.subject.por.fl_str_mv Adsorption
Downstream processing
Enzyme activity
Chitosanases
Modeling
NARX
topic Adsorption
Downstream processing
Enzyme activity
Chitosanases
Modeling
NARX
description Nonlinear autoregressive networks with external input (NARX) and multilayer perceptron (MLP) has been used to predict the activity and protein content for flow-through, washing and elution steps during expanded bed adsorption chromatography of chitosanases produced by Paenibacillus ehimensis. Bed expansion as well as the influence of particulatecontaining feedstock study showed a stable bed operation without significant impact on the yield as well as purification factor when the cells were fed to the column. Also, NARXs showed a better performance to predict the chitosanolytic activity as well as total protein than MLPs
publishDate 2017
dc.date.issued.fl_str_mv 2017-01
dc.date.accessioned.fl_str_mv 2021-12-06T18:18:18Z
dc.date.available.fl_str_mv 2021-12-06T18:18:18Z
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.citation.fl_str_mv PADILHA, Carlos Eduardo de Araújo; PADILHA, Carlos Alberto de Araújo; SOUZA, Domingos Fabiano de Santana; OLIVEIRA, Jackson Araújo de; MACEDO, Gorete Ribeiro de; SANTOS, Everaldo Silvino dos. Recurrent neural network modeling applied to expanded bed adsorption chromatography of chitosanases produced by Paenibacillus ehimensis. Chemical Engineering Research And Design, [S.L.], v. 117, p. 24-33, jan. 2017. Elsevier BV. http://dx.doi.org/10.1016/j.cherd.2016.09.022. Disponível em: https://www.sciencedirect.com/science/article/pii/S0263876216303070?via%3Dihub. Acesso em: 05 nov. 2021.
dc.identifier.uri.fl_str_mv https://repositorio.ufrn.br/handle/123456789/45192
dc.identifier.issn.none.fl_str_mv 0263-8762
dc.identifier.doi.none.fl_str_mv 10.1016/j.cherd.2016.09.022
identifier_str_mv PADILHA, Carlos Eduardo de Araújo; PADILHA, Carlos Alberto de Araújo; SOUZA, Domingos Fabiano de Santana; OLIVEIRA, Jackson Araújo de; MACEDO, Gorete Ribeiro de; SANTOS, Everaldo Silvino dos. Recurrent neural network modeling applied to expanded bed adsorption chromatography of chitosanases produced by Paenibacillus ehimensis. Chemical Engineering Research And Design, [S.L.], v. 117, p. 24-33, jan. 2017. Elsevier BV. http://dx.doi.org/10.1016/j.cherd.2016.09.022. Disponível em: https://www.sciencedirect.com/science/article/pii/S0263876216303070?via%3Dihub. Acesso em: 05 nov. 2021.
0263-8762
10.1016/j.cherd.2016.09.022
url https://repositorio.ufrn.br/handle/123456789/45192
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
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institution UFRN
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