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dc.contributor.authorPitarch Pérez, José Luis 
dc.contributor.authorSala, Antonio
dc.contributor.authorPrada Moraga, César de 
dc.date.accessioned2019-07-10T15:27:06Z
dc.date.available2019-07-10T15:27:06Z
dc.date.issued2019
dc.identifier.citation12th IFAC Symposium on Dynamics and Control of Process Systems, including Biosystems DYCOPS 2019: Florianópolis, Brazil, 23–26 April 2019 Edited by Benoit Chachuat, Olivier Bernard, Julio E. Normey-Ricoes
dc.identifier.urihttp://uvadoc.uva.es/handle/10324/36806
dc.descriptionProducción Científicaes
dc.description.abstractCombining empirical relationships with a backbone of first-principle laws allow the modeler to transfer the available process knowledge into a model. In order to get such so-called grey-box models, data reconciliation methods and constrained regression algorithms are key to obtain reliable process models that will be used later for optimization. However, the existent approaches require solving a semi-infinite constrained regression nonlinear problem, which is usually done numerically by an iterative procedure alternating between a relaxed problem and an a posteriori check for constraint violation. This paper proposes an alternative one-stage efficient approach for polynomial regression models based in sum-of-squares (convex) programming. Moreover, it is shown how several desirable features on the regression model can be naturally enforced in this optimization framework. The effectiveness of the proposed approach is illustrated through an academic example provided in the related literature.es
dc.format.extent6 p.es
dc.format.mimetypeapplication/pdfes
dc.language.isoenges
dc.publisherElsevieres
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/*
dc.subject.classificationConstrained regressiones
dc.subject.classificationProcess modelses
dc.subject.classificationGrey-box modelses
dc.subject.classificationSOS programminges
dc.titleA Sum-Of-Squares Constrained Regression Approach for Process Modelinges
dc.typeinfo:eu-repo/semantics/conferenceObjectes
dc.rights.holderElsevieres
dc.identifier.doi10.1016/j.ifacol.2019.06.152es
dc.relation.publisherversionhttps://doi.org/10.1016/j.ifacol.2019.06.152es
dc.title.event12th IFAC Symposium on Dynamics and Control of Process Systems, including Biosystems DYCOPS 2019es
dc.description.projectEuropean Union, Horizon 2020 research and innovation programme under grant agreement No 723575 (CoPro)es
dc.description.projectMINECO DPI2016-81002-R (AEI/FEDER)es
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/723575
dc.rightsAtribución-NoComercial-CompartirIgual 4.0 Internacional*
dc.type.hasVersioninfo:eu-repo/semantics/updatedVersiones


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