Publicación:
Advanced Control by Reinforcement Learning for Wastewater Treatment Plants: A Comparison with Traditional Approaches

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dc.contributor.authorGorrotxategi Zipitria, Mikel
dc.contributor.authorHernández del Olmo, Félix
dc.contributor.authorGaudioso Vázquez, Elena
dc.contributor.authorDuro Carralero, Natividad
dc.contributor.authorDormido Canto, Raquel
dc.date.accessioned2024-05-20T11:43:06Z
dc.date.available2024-05-20T11:43:06Z
dc.date.issued2023
dc.description.abstractControl mechanisms for biological treatment of wastewater treatment plants are mostly based on PIDS. However, their performance is far from optimal due to the high non-linearity of the biological and changing processes involved. Therefore, more advanced control techniques are proposed in the literature (e.g., using artificial intelligence techniques). However, these new control techniques have not been compared to the traditional approaches that are actually being used in real plants. To this end, in this paper, we present a comparison of the PID control configurations currently applied to control the dissolved oxygen concentration (in the active sludge process) against a reinforcement learning agent. Our results show that it is possible to have a very competitive operating cost budget when these innovative techniques are applied.en
dc.description.versionversión publicada
dc.identifier.doihttps://doi.org/10.3390/app13084752
dc.identifier.issn2076-3417
dc.identifier.urihttps://hdl.handle.net/20.500.14468/12461
dc.journal.issue8
dc.journal.titleApplied Sciences
dc.journal.volume13
dc.language.isoen
dc.publisherMDPI
dc.relation.centerE.T.S. de Ingeniería Informática
dc.relation.departmentInteligencia Artificial
dc.rightsAtribución 4.0 Internacional
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by/4.0
dc.subject.keywordsadvanced control
dc.subject.keywordsreinforcement learning
dc.subject.keywordswastewater system
dc.titleAdvanced Control by Reinforcement Learning for Wastewater Treatment Plants: A Comparison with Traditional Approacheses
dc.typeartículoes
dc.typejournal articleen
dspace.entity.typePublication
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relation.isAuthorOfPublication.latestForDiscovery7c2613b6-1a06-4187-9cd1-4c51f5016c51
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