Model-free control based on reinforcement learning for a wastewater treatment problem

Autores
Syafiie, S.; Tadeo, F.; Martínez, Ernesto Carlos; Alvarez, T.
Año de publicación
2011
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
This article presents a proposal, based on the model-free learning control (MFLC) approach, for the control of the advanced oxidation process in wastewater plants. This is prompted by the fact that many organic pollutants in industrial wastewaters are resistant to conventional biological treatments, and the fact that advanced oxidation processes, controlled with learning controllers measuring the oxidation-reduction potential (ORP), give a cost-effective solution. The proposed automation strategy denoted MFLC-MSA is based on the integration of reinforcement learning with multiple step actions. This enables the most adequate control strategy to be learned directly from the process response to selected control inputs. Thus, the proposed methodology is satisfactory for oxidation processes of wastewater treatment plants, where the development of an adequate model for control design is usually too costly. The algorithm proposed has been tested in a lab pilot plant, where phenolic wastewater is oxidized to carboxylic acids and carbon dioxide. The obtained experimental results show that the proposed MFLC-MSA strategy can achieve good performance to guarantee on-specification discharge at maximum degradation rate using readily available measurements such as pH and ORP, inferential measurements of oxidation kinetics and peroxide consumption, respectively.
Fil: Syafiie, S.. Universiti Putra Malaysia; Malasia
Fil: Tadeo, F.. Universidad de Valladolid; España
Fil: Martínez, Ernesto Carlos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentina
Fil: Alvarez, T.. Universidad de Valladolid; España
Materia
Wastewater Treatment
Reinforcement Learning
Intelligent Control
Fenton Process
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:11336/70209
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Model-free control based on reinforcement learning for a wastewater treatment problemSyafiie, S.Tadeo, F.Martínez, Ernesto CarlosAlvarez, T.Wastewater TreatmentReinforcement LearningIntelligent ControlFenton Processhttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2This article presents a proposal, based on the model-free learning control (MFLC) approach, for the control of the advanced oxidation process in wastewater plants. This is prompted by the fact that many organic pollutants in industrial wastewaters are resistant to conventional biological treatments, and the fact that advanced oxidation processes, controlled with learning controllers measuring the oxidation-reduction potential (ORP), give a cost-effective solution. The proposed automation strategy denoted MFLC-MSA is based on the integration of reinforcement learning with multiple step actions. This enables the most adequate control strategy to be learned directly from the process response to selected control inputs. Thus, the proposed methodology is satisfactory for oxidation processes of wastewater treatment plants, where the development of an adequate model for control design is usually too costly. The algorithm proposed has been tested in a lab pilot plant, where phenolic wastewater is oxidized to carboxylic acids and carbon dioxide. The obtained experimental results show that the proposed MFLC-MSA strategy can achieve good performance to guarantee on-specification discharge at maximum degradation rate using readily available measurements such as pH and ORP, inferential measurements of oxidation kinetics and peroxide consumption, respectively.Fil: Syafiie, S.. Universiti Putra Malaysia; MalasiaFil: Tadeo, F.. Universidad de Valladolid; EspañaFil: Martínez, Ernesto Carlos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; ArgentinaFil: Alvarez, T.. Universidad de Valladolid; EspañaElsevier Science2011-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/70209Syafiie, S.; Tadeo, F.; Martínez, Ernesto Carlos; Alvarez, T.; Model-free control based on reinforcement learning for a wastewater treatment problem; Elsevier Science; Applied Soft Computing; 11; 1; 1-2011; 73-821568-4946CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.asoc.2009.10.018info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:26:21Zoai:ri.conicet.gov.ar:11336/70209instacron: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:34982025-09-29 10:26:21.985CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Model-free control based on reinforcement learning for a wastewater treatment problem
title Model-free control based on reinforcement learning for a wastewater treatment problem
spellingShingle Model-free control based on reinforcement learning for a wastewater treatment problem
Syafiie, S.
Wastewater Treatment
Reinforcement Learning
Intelligent Control
Fenton Process
title_short Model-free control based on reinforcement learning for a wastewater treatment problem
title_full Model-free control based on reinforcement learning for a wastewater treatment problem
title_fullStr Model-free control based on reinforcement learning for a wastewater treatment problem
title_full_unstemmed Model-free control based on reinforcement learning for a wastewater treatment problem
title_sort Model-free control based on reinforcement learning for a wastewater treatment problem
dc.creator.none.fl_str_mv Syafiie, S.
Tadeo, F.
Martínez, Ernesto Carlos
Alvarez, T.
author Syafiie, S.
author_facet Syafiie, S.
Tadeo, F.
Martínez, Ernesto Carlos
Alvarez, T.
author_role author
author2 Tadeo, F.
Martínez, Ernesto Carlos
Alvarez, T.
author2_role author
author
author
dc.subject.none.fl_str_mv Wastewater Treatment
Reinforcement Learning
Intelligent Control
Fenton Process
topic Wastewater Treatment
Reinforcement Learning
Intelligent Control
Fenton Process
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv This article presents a proposal, based on the model-free learning control (MFLC) approach, for the control of the advanced oxidation process in wastewater plants. This is prompted by the fact that many organic pollutants in industrial wastewaters are resistant to conventional biological treatments, and the fact that advanced oxidation processes, controlled with learning controllers measuring the oxidation-reduction potential (ORP), give a cost-effective solution. The proposed automation strategy denoted MFLC-MSA is based on the integration of reinforcement learning with multiple step actions. This enables the most adequate control strategy to be learned directly from the process response to selected control inputs. Thus, the proposed methodology is satisfactory for oxidation processes of wastewater treatment plants, where the development of an adequate model for control design is usually too costly. The algorithm proposed has been tested in a lab pilot plant, where phenolic wastewater is oxidized to carboxylic acids and carbon dioxide. The obtained experimental results show that the proposed MFLC-MSA strategy can achieve good performance to guarantee on-specification discharge at maximum degradation rate using readily available measurements such as pH and ORP, inferential measurements of oxidation kinetics and peroxide consumption, respectively.
Fil: Syafiie, S.. Universiti Putra Malaysia; Malasia
Fil: Tadeo, F.. Universidad de Valladolid; España
Fil: Martínez, Ernesto Carlos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentina
Fil: Alvarez, T.. Universidad de Valladolid; España
description This article presents a proposal, based on the model-free learning control (MFLC) approach, for the control of the advanced oxidation process in wastewater plants. This is prompted by the fact that many organic pollutants in industrial wastewaters are resistant to conventional biological treatments, and the fact that advanced oxidation processes, controlled with learning controllers measuring the oxidation-reduction potential (ORP), give a cost-effective solution. The proposed automation strategy denoted MFLC-MSA is based on the integration of reinforcement learning with multiple step actions. This enables the most adequate control strategy to be learned directly from the process response to selected control inputs. Thus, the proposed methodology is satisfactory for oxidation processes of wastewater treatment plants, where the development of an adequate model for control design is usually too costly. The algorithm proposed has been tested in a lab pilot plant, where phenolic wastewater is oxidized to carboxylic acids and carbon dioxide. The obtained experimental results show that the proposed MFLC-MSA strategy can achieve good performance to guarantee on-specification discharge at maximum degradation rate using readily available measurements such as pH and ORP, inferential measurements of oxidation kinetics and peroxide consumption, respectively.
publishDate 2011
dc.date.none.fl_str_mv 2011-01
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/11336/70209
Syafiie, S.; Tadeo, F.; Martínez, Ernesto Carlos; Alvarez, T.; Model-free control based on reinforcement learning for a wastewater treatment problem; Elsevier Science; Applied Soft Computing; 11; 1; 1-2011; 73-82
1568-4946
CONICET Digital
CONICET
url http://hdl.handle.net/11336/70209
identifier_str_mv Syafiie, S.; Tadeo, F.; Martínez, Ernesto Carlos; Alvarez, T.; Model-free control based on reinforcement learning for a wastewater treatment problem; Elsevier Science; Applied Soft Computing; 11; 1; 1-2011; 73-82
1568-4946
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/j.asoc.2009.10.018
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
dc.publisher.none.fl_str_mv Elsevier Science
publisher.none.fl_str_mv Elsevier Science
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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score 13.069144

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