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dc.contributor.authorShokry, Ahmed
dc.contributor.authorAudino, Francesca
dc.contributor.authorVicente Núñez, Patricia
dc.contributor.authorEscudero Bakx, Gerard
dc.contributor.authorPérez Moya, Montserrat
dc.contributor.authorGraells Sobré, Moisès
dc.contributor.authorEspuña Camarasa, Antonio
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria Química
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Ciències de la Computació
dc.date.accessioned2016-03-02T12:17:39Z
dc.date.issued2015
dc.identifier.citationShokry , A., Audino, F., Vicente, P., Escudero, G., Pérez-Moya, M., Graells, M., Espuña, A. Modeling and simulation of complex nonlinear dynamic processes using data based models: application to photo-fenton process. A: European Symposium on Computer Aided Process Engineering. "12th Intenational Symposium on Process Systems Engineering and 25th European Symposium on Computer Aided Process Engineering. Part A. Computer Aided Chemical Engineering, 37". Copenhagen: Elsevier, 2015, p. 191-196.
dc.identifier.isbn9780444634290
dc.identifier.urihttp://hdl.handle.net/2117/83695
dc.description.abstractThis paper investigates data based modelling of complex nonlinear processes, for which a first principle model useful for process monitoring and control is not available. These empirical models may be used as soft sensors in order to monitor a reaction’s progress, so reducing expensive offline sampling and analysis. Three different data modelling techniques are used, namely Ordinary Kriging, Artificial Neural Networks and Support Vector Regression. A simple case is first used to illustrate the problem, assess and validate the modelling approach, and compare the modelling techniques. Next, the methodology is applied to a photo–Fenton pilot plant to model and predict the reaction progress. The results show promising accuracy even when few training points are available, which results in huge savings of time and cost of the experimental work.
dc.format.extent6 p.
dc.language.isoeng
dc.publisherElsevier
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Enginyeria química
dc.subjectÀrees temàtiques de la UPC::Informàtica
dc.subject.lcshOxidation
dc.subject.otherKriging
dc.subject.othersupport vector machines
dc.subject.otherneural networks
dc.subject.otherchemical kinetics.
dc.titleModeling and simulation of complex nonlinear dynamic processes using data based models: application to photo-fenton process
dc.typeConference report
dc.subject.lemacOxidació
dc.contributor.groupUniversitat Politècnica de Catalunya. CEPIMA - Center for Process and Environment Engineering
dc.contributor.groupUniversitat Politècnica de Catalunya. GPLN - Grup de Processament del Llenguatge Natural
dc.identifier.doi10.1016/B978-0-444-63578-5.50027-X
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttp://www.sciencedirect.com/science/article/pii/B978044463578550027X
dc.rights.accessRestricted access - publisher's policy
drac.iddocument17532838
dc.description.versionPostprint (author's final draft)
dc.date.lift10000-01-01
upcommons.citation.authorShokry , A., Audino, F., Vicente, P., Escudero, G., Pérez-Moya, M., Graells, M., Espuña, A.
upcommons.citation.contributorEuropean Symposium on Computer Aided Process Engineering
upcommons.citation.pubplaceCopenhagen
upcommons.citation.publishedtrue
upcommons.citation.publicationName12th Intenational Symposium on Process Systems Engineering and 25th European Symposium on Computer Aided Process Engineering. Part A. Computer Aided Chemical Engineering, 37
upcommons.citation.startingPage191
upcommons.citation.endingPage196


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