Learning to Control pH Processes at Multiple Time Scales

Autores
Syafiie, S.; Tadeo, F.; Martínez, Ernesto Carlos
Año de publicación
2007
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
This article presents a solution to pH control based on model-free learning control (MFLC). The MFLC technique is proposed because the algorithm gives a general solution for acid-base systems, yet is simple enough for implementation in existing control hardware. MFLC is based on reinforcement learning (RL), which is learning by direct interaction with the environment. The MFLC algorithm is model free and satisfying incremental control, input and output constraints. A novel solution of MFLC using multi-step actions (MSA) is presented: actions on multiple time scales consist of several identical primitive actions. This solves the problem of determining a suitable fixed time scale to select control actions so as to trade off accuracy in control against learning complexity. An application of MFLC to a pH process at laboratory scale is presented, showing that the proposed MFLC learns to control adequately the neutralization process, and maintain the process in the goal band. Also, the MFLC controller smoothly manipulates the control signal.
Fil: Syafiie, S.. Universidad de Valladolid; España
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
Materia
Ph Control
Learning Control
Reinforcement Learning
Wastewater Treatment
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:11336/83824
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/83824
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Learning to Control pH Processes at Multiple Time ScalesSyafiie, S.Tadeo, F.Martínez, Ernesto CarlosPh ControlLearning ControlReinforcement LearningWastewater Treatmenthttps://purl.org/becyt/ford/2.4https://purl.org/becyt/ford/2This article presents a solution to pH control based on model-free learning control (MFLC). The MFLC technique is proposed because the algorithm gives a general solution for acid-base systems, yet is simple enough for implementation in existing control hardware. MFLC is based on reinforcement learning (RL), which is learning by direct interaction with the environment. The MFLC algorithm is model free and satisfying incremental control, input and output constraints. A novel solution of MFLC using multi-step actions (MSA) is presented: actions on multiple time scales consist of several identical primitive actions. This solves the problem of determining a suitable fixed time scale to select control actions so as to trade off accuracy in control against learning complexity. An application of MFLC to a pH process at laboratory scale is presented, showing that the proposed MFLC learns to control adequately the neutralization process, and maintain the process in the goal band. Also, the MFLC controller smoothly manipulates the control signal.Fil: Syafiie, S.. Universidad de Valladolid; EspañaFil: 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; ArgentinaThe Berkeley Electronic Press2007-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/83824Syafiie, S.; Tadeo, F.; Martínez, Ernesto Carlos; Learning to Control pH Processes at Multiple Time Scales; The Berkeley Electronic Press; Chemical Product and Process Modeling; 2; 1; 12-2007; 1-71934-2659CONICET DigitalCONICETenginfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:29:48Zoai:ri.conicet.gov.ar:11336/83824instacron: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 10:29:48.833CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Learning to Control pH Processes at Multiple Time Scales
title Learning to Control pH Processes at Multiple Time Scales
spellingShingle Learning to Control pH Processes at Multiple Time Scales
Syafiie, S.
Ph Control
Learning Control
Reinforcement Learning
Wastewater Treatment
title_short Learning to Control pH Processes at Multiple Time Scales
title_full Learning to Control pH Processes at Multiple Time Scales
title_fullStr Learning to Control pH Processes at Multiple Time Scales
title_full_unstemmed Learning to Control pH Processes at Multiple Time Scales
title_sort Learning to Control pH Processes at Multiple Time Scales
dc.creator.none.fl_str_mv Syafiie, S.
Tadeo, F.
Martínez, Ernesto Carlos
author Syafiie, S.
author_facet Syafiie, S.
Tadeo, F.
Martínez, Ernesto Carlos
author_role author
author2 Tadeo, F.
Martínez, Ernesto Carlos
author2_role author
author
dc.subject.none.fl_str_mv Ph Control
Learning Control
Reinforcement Learning
Wastewater Treatment
topic Ph Control
Learning Control
Reinforcement Learning
Wastewater Treatment
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.4
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv This article presents a solution to pH control based on model-free learning control (MFLC). The MFLC technique is proposed because the algorithm gives a general solution for acid-base systems, yet is simple enough for implementation in existing control hardware. MFLC is based on reinforcement learning (RL), which is learning by direct interaction with the environment. The MFLC algorithm is model free and satisfying incremental control, input and output constraints. A novel solution of MFLC using multi-step actions (MSA) is presented: actions on multiple time scales consist of several identical primitive actions. This solves the problem of determining a suitable fixed time scale to select control actions so as to trade off accuracy in control against learning complexity. An application of MFLC to a pH process at laboratory scale is presented, showing that the proposed MFLC learns to control adequately the neutralization process, and maintain the process in the goal band. Also, the MFLC controller smoothly manipulates the control signal.
Fil: Syafiie, S.. Universidad de Valladolid; España
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
description This article presents a solution to pH control based on model-free learning control (MFLC). The MFLC technique is proposed because the algorithm gives a general solution for acid-base systems, yet is simple enough for implementation in existing control hardware. MFLC is based on reinforcement learning (RL), which is learning by direct interaction with the environment. The MFLC algorithm is model free and satisfying incremental control, input and output constraints. A novel solution of MFLC using multi-step actions (MSA) is presented: actions on multiple time scales consist of several identical primitive actions. This solves the problem of determining a suitable fixed time scale to select control actions so as to trade off accuracy in control against learning complexity. An application of MFLC to a pH process at laboratory scale is presented, showing that the proposed MFLC learns to control adequately the neutralization process, and maintain the process in the goal band. Also, the MFLC controller smoothly manipulates the control signal.
publishDate 2007
dc.date.none.fl_str_mv 2007-12
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/11336/83824
Syafiie, S.; Tadeo, F.; Martínez, Ernesto Carlos; Learning to Control pH Processes at Multiple Time Scales; The Berkeley Electronic Press; Chemical Product and Process Modeling; 2; 1; 12-2007; 1-7
1934-2659
CONICET Digital
CONICET
url http://hdl.handle.net/11336/83824
identifier_str_mv Syafiie, S.; Tadeo, F.; Martínez, Ernesto Carlos; Learning to Control pH Processes at Multiple Time Scales; The Berkeley Electronic Press; Chemical Product and Process Modeling; 2; 1; 12-2007; 1-7
1934-2659
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
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 The Berkeley Electronic Press
publisher.none.fl_str_mv The Berkeley Electronic Press
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
_version_ 1844614305189724160
score 13.069144

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