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dc.contributor.author
Syafiie, S.
dc.contributor.author
Tadeo, F.
dc.contributor.author
Martínez, Ernesto Carlos
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dc.contributor.author
Alvarez, T.
dc.date.available
2019-02-14T18:59:49Z
dc.date.issued
2011-01
dc.identifier.citation
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
dc.identifier.issn
1568-4946
dc.identifier.uri
http://hdl.handle.net/11336/70209
dc.description.abstract
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.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Elsevier Science
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dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
Wastewater Treatment
dc.subject
Reinforcement Learning
dc.subject
Intelligent Control
dc.subject
Fenton Process
dc.subject.classification
Ingeniería de Sistemas y Comunicaciones
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dc.subject.classification
Ingeniería Eléctrica, Ingeniería Electrónica e Ingeniería de la Información
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dc.subject.classification
INGENIERÍAS Y TECNOLOGÍAS
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dc.title
Model-free control based on reinforcement learning for a wastewater treatment problem
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2019-02-12T17:23:26Z
dc.journal.volume
11
dc.journal.number
1
dc.journal.pagination
73-82
dc.journal.pais
Países Bajos
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dc.journal.ciudad
Amsterdam
dc.description.fil
Fil: Syafiie, S.. Universiti Putra Malaysia; Malasia
dc.description.fil
Fil: Tadeo, F.. Universidad de Valladolid; España
dc.description.fil
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
dc.description.fil
Fil: Alvarez, T.. Universidad de Valladolid; España
dc.journal.title
Applied Soft Computing
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dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1016/j.asoc.2009.10.018
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