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dc.contributor.authorPascual Pañach, Josep
dc.contributor.authorCugeró Escofet, Miquel Àngel
dc.contributor.authorSànchez-Marrè, Miquel
dc.contributor.authorAguiló Martos, Pere
dc.contributor.otherUniversitat Politècnica de Catalunya. Doctorat en Intel·ligència Artificial
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Ciències de la Computació
dc.date.accessioned2020-05-26T06:56:23Z
dc.date.available2020-05-26T06:56:23Z
dc.date.issued2019
dc.identifier.citationPascual, J. [et al.]. Application of CBR for intelligent process control of a WWTP. "Frontiers in artificial intelligence and applications", 2019, vol. 319, p. 160-169.
dc.identifier.issn0922-6389
dc.identifier.urihttp://hdl.handle.net/2117/188957
dc.description.abstractThis paper proposes the use of a Case-Based Reasoning (CBR) system for the control and the supervision of a real wastewater treatment plant (WWTP). A WWTP is a critical system which aims to ensure the quality of the water discharged to the receiving bodies, stablished by applicable regulations. At the current stage the proposed methodology has been tested off-line on a real system for the control of the aeration process in the biological treatment of a WWTP within the ambit ofConsorci Besòs Tordera (CBT), a local water administration in the area of Barcelona. For this purpose, data mining methods are considered to extract the available knowledge from historical data to find a useful case base to be able to generate set-points for the local controllers in the WWTP. The results presented in this work are evaluated taking into account the performance of the CBR method e.g. case base size, CBR cycle time or number of cases resolved satisfactorily (forthcoming steps will include on-line tests). For this purpose, some Key Performance Indicators (KPI) are designed together with the plant manager and process experts, in order to monitor key parameters of the WWTP which are representative of the performance of the control and supervision system. Hence, these KPI are related with water quality regulations —e.g. ammonia concentration in the WWTP effluent— and the economic cost efficiency —e.g. electrical consumption of the installation. In order to evaluate the results, different flat-based memory organizations (i.e. cases are stored sequentially in a list) for the case base are considered. First, a unique case base is used. At the current stage and for the results shown in this work, this case base is divided in multiple libraries depending on a case classification. Finally, the combination of this approach with Rule-Based Reasoning (RBR) methods is proposed for the next stages of the work.
dc.description.sponsorshipThe authors acknowledge the partial support of this work by the Industrial Doctorate Programme (2017-DI-006) and the Research Consolidated Groups/Centres Grant (2017 SGR 574) from the Catalan Agency of University and Research Grants Management (AGAUR), from Catalan Government.
dc.format.extent10 p.
dc.language.isoeng
dc.publisherIOS Press
dc.subjectÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
dc.subjectÀrees temàtiques de la UPC::Desenvolupament humà i sostenible::Enginyeria ambiental
dc.subject.lcshData mining
dc.subject.lcshSewage disposal plants
dc.subject.otherCase-based reasoning
dc.subject.otherIntelligent process control
dc.subject.otherWastewater treatment plant
dc.titleApplication of CBR for intelligent process control of a WWTP
dc.typeArticle
dc.subject.lemacMineria de dades
dc.subject.lemacAigües residuals -- Depuració
dc.contributor.groupUniversitat Politècnica de Catalunya. SIC - Sistemes Intel·ligents de Control
dc.contributor.groupUniversitat Politècnica de Catalunya. KEMLG - Grup d'Enginyeria del Coneixement i Aprenentatge Automàtic
dc.identifier.doi10.3233/FAIA190119
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttp://ebooks.iospress.nl/publication/52832
dc.rights.accessOpen Access
local.identifier.drac28505262
dc.description.versionPostprint (author's final draft)
dc.relation.projectidinfo:eu-repo/grantAgreement/AGAUR/RIS3CAT/2017 SGR 574
dc.relation.projectidinfo:eu-repo/grantAgreement/AGAUR/V PRI/2017 DI 006
local.citation.authorPascual, J.; Cugeró-Escofet, M.A.; Sànchez-Marrè, M.; Aguiló, P.
local.citation.publicationNameFrontiers in artificial intelligence and applications
local.citation.volume319
local.citation.startingPage160
local.citation.endingPage169


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