Control and soft sensing strategies for a wastewater treatment plant using a neuro-genetic approach
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.1/14968 |
Resumo: | During the last years, machine learning-based control and optimization systems are playing an important role in the operation of wastewater treatment plants in terms of reduced operational costs and improved effluent quality. In this paper, a machine learning-based control strategy is proposed for optimizing both the consumption and the number of regulation violations of a biological wastewater treatment plant. The methodology proposed in this study uses neural networks as a soft-sensor for on-line prediction of the effluent quality and as an identification model of the plant dynamics, all under a neuro-genetic optimum model-based control approach. The complete scheme was tested on a simulation model of the activated sludge process of a large-scale municipal wastewater treatment plant running under the GPS-X simulation frame and validated with operational gathered data, showing optimal control performance by minimizing operational costs while satisfying the effluent requirements, thus reducing the investment in expensive sensor devices. |
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Control and soft sensing strategies for a wastewater treatment plant using a neuro-genetic approachNeural networksActivated sludge processGenetic algorithmsSoft-sensingOptimized controlDuring the last years, machine learning-based control and optimization systems are playing an important role in the operation of wastewater treatment plants in terms of reduced operational costs and improved effluent quality. In this paper, a machine learning-based control strategy is proposed for optimizing both the consumption and the number of regulation violations of a biological wastewater treatment plant. The methodology proposed in this study uses neural networks as a soft-sensor for on-line prediction of the effluent quality and as an identification model of the plant dynamics, all under a neuro-genetic optimum model-based control approach. The complete scheme was tested on a simulation model of the activated sludge process of a large-scale municipal wastewater treatment plant running under the GPS-X simulation frame and validated with operational gathered data, showing optimal control performance by minimizing operational costs while satisfying the effluent requirements, thus reducing the investment in expensive sensor devices.ElsevierSapientiaFernandez de Canete, J.del Saz-Orozco, P.Gómez-de-Gabriel, J.Baratti, R.Ruano, AntonioRivas-Blanco, I.2021-01-15T17:25:42Z20212021-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.1/14968eng0098-135410.1016/j.compchemeng.2020.107146info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-07-24T10:27:19Zoai:sapientia.ualg.pt:10400.1/14968Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T20:05:54.225859Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Control and soft sensing strategies for a wastewater treatment plant using a neuro-genetic approach |
title |
Control and soft sensing strategies for a wastewater treatment plant using a neuro-genetic approach |
spellingShingle |
Control and soft sensing strategies for a wastewater treatment plant using a neuro-genetic approach Fernandez de Canete, J. Neural networks Activated sludge process Genetic algorithms Soft-sensing Optimized control |
title_short |
Control and soft sensing strategies for a wastewater treatment plant using a neuro-genetic approach |
title_full |
Control and soft sensing strategies for a wastewater treatment plant using a neuro-genetic approach |
title_fullStr |
Control and soft sensing strategies for a wastewater treatment plant using a neuro-genetic approach |
title_full_unstemmed |
Control and soft sensing strategies for a wastewater treatment plant using a neuro-genetic approach |
title_sort |
Control and soft sensing strategies for a wastewater treatment plant using a neuro-genetic approach |
author |
Fernandez de Canete, J. |
author_facet |
Fernandez de Canete, J. del Saz-Orozco, P. Gómez-de-Gabriel, J. Baratti, R. Ruano, Antonio Rivas-Blanco, I. |
author_role |
author |
author2 |
del Saz-Orozco, P. Gómez-de-Gabriel, J. Baratti, R. Ruano, Antonio Rivas-Blanco, I. |
author2_role |
author author author author author |
dc.contributor.none.fl_str_mv |
Sapientia |
dc.contributor.author.fl_str_mv |
Fernandez de Canete, J. del Saz-Orozco, P. Gómez-de-Gabriel, J. Baratti, R. Ruano, Antonio Rivas-Blanco, I. |
dc.subject.por.fl_str_mv |
Neural networks Activated sludge process Genetic algorithms Soft-sensing Optimized control |
topic |
Neural networks Activated sludge process Genetic algorithms Soft-sensing Optimized control |
description |
During the last years, machine learning-based control and optimization systems are playing an important role in the operation of wastewater treatment plants in terms of reduced operational costs and improved effluent quality. In this paper, a machine learning-based control strategy is proposed for optimizing both the consumption and the number of regulation violations of a biological wastewater treatment plant. The methodology proposed in this study uses neural networks as a soft-sensor for on-line prediction of the effluent quality and as an identification model of the plant dynamics, all under a neuro-genetic optimum model-based control approach. The complete scheme was tested on a simulation model of the activated sludge process of a large-scale municipal wastewater treatment plant running under the GPS-X simulation frame and validated with operational gathered data, showing optimal control performance by minimizing operational costs while satisfying the effluent requirements, thus reducing the investment in expensive sensor devices. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-01-15T17:25:42Z 2021 2021-01-01T00:00:00Z |
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://hdl.handle.net/10400.1/14968 |
url |
http://hdl.handle.net/10400.1/14968 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
0098-1354 10.1016/j.compchemeng.2020.107146 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Elsevier |
publisher.none.fl_str_mv |
Elsevier |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799133299673661440 |