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dc.contributor.authorPitarch Pérez, José Luis 
dc.contributor.authorPrada Moraga, César de 
dc.date.accessioned2020-03-03T18:22:23Z
dc.date.available2020-03-03T18:22:23Z
dc.date.issued2019
dc.identifier.citationEmilio Jiménez, Juan Ignacio Latorre (eds.), 10th EUROSIM Congress, La Rioja, Logroño, Spain, July 1-5, 2019es
dc.identifier.isbn978-3-901608-92-6es
dc.identifier.urihttp://uvadoc.uva.es/handle/10324/40565
dc.descriptionProducción Científicaes
dc.description.abstractModern sensorization, communication and computational technolo-gies provide collecting and storing huge amounts of raw data from large cyber-physical systems. These data should serve as the basis to take better decisions at all levels (from the design to operation and management). Nevertheless, raw data needs to be transformed in useful information, usually in the form of prediction models. Machine learning plays thus a key role in this task. The process industry is not alien to this digital transformation, although large processing plants present particularities that differentiate them from other systems. These differences, if neglected, can make machine learning for general purpose fail in extracting the right information from data, leading thus to unreliable process models. As such models are the basis on which the ideas towards the cognitive plant rely, this issue is of key importance for a successful full digitalization of the process industry. Here we discuss these aspects, as well as the more suitable machine-learning ap-proaches, through our experience in an industrial case study.es
dc.format.extent12es
dc.format.mimetypeapplication/pdfes
dc.language.isoenges
dc.publisherARGESIMes
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/*
dc.subjectModellinges
dc.subjectSimulationes
dc.subjectMachine learninges
dc.subject.classificationmachine learninges
dc.subject.classificationdata conditioninges
dc.subject.classificationprocess modelinges
dc.subject.classificationdata reconciliationes
dc.subject.classificationconstrained regressiones
dc.subject.classificationgrey-box modelses
dc.titleMachine learning and the digital era from a Process Systems Engineering perspectivees
dc.typeinfo:eu-repo/semantics/conferenceObjectes
dc.identifier.doi10.11128/arep.58es
dc.title.event10th EUROSIM Congress on Modelling and Simulationes
dc.description.projectThis research received funding from the EU Horizon 2020 research and innovation programme under Grant No. 723575 (CoPro) and from the Spanish MICINN with FEDER funds (PGC2018-099312-B-C31)es
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/723575
dc.rightsCC0 1.0 Universal*
dc.type.hasVersioninfo:eu-repo/semantics/acceptedVersiones


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