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    Por favor, use este identificador para citar o enlazar este ítem:https://uvadoc.uva.es/handle/10324/56016

    Título
    A systematic grey-box modeling methodology via data reconciliation and SOS constrained regression
    Autor
    Pitarch Pérez, José LuisAutoridad UVA Orcid
    Sala, Antonio
    Prada Moraga, César deAutoridad UVA Orcid
    Año del Documento
    2019
    Editorial
    MDPI
    Descripción
    Producción Científica
    Documento Fuente
    Processes, 2019, vol. 7, n. 3, 170
    Resumo
    Developing the so-called grey box or hybrid models of limited complexity for process systems is the cornerstone in advanced control and real-time optimization routines. These models must be based on fundamental principles and customized with sub-models obtained from process experimental data. This allows the engineer to transfer the available process knowledge into a model. However, there is still a lack of a flexible but systematic methodology for grey-box modeling which ensures certain coherence of the experimental sub-models with the process physics. This paper proposes such a methodology based in data reconciliation (DR) and polynomial constrained regression. A nonlinear optimization of limited complexity is to be solved in the DR stage, whereas the proposed constrained regression is based in sum-of-squares (SOS) convex programming. It is shown how several desirable features on the polynomial regressors can be naturally enforced in this optimization framework. The goodnesses of the proposed methodology are illustrated through: (1) an academic example and (2) an industrial evaporation plant with real experimental data.
    Palabras Clave
    Machine learning
    Aprendizaje automático
    Process modeling
    Modelado de procesos
    ISSN
    2227-9717
    Revisión por pares
    SI
    DOI
    10.3390/pr7030170
    Patrocinador
    Ministerio de Economía, Industria y Competitividad (grant DPI2016-81002-R)
    European Union’s Horizon 2020 research and innovation program. grant agreement no 723575
    Patrocinador
    info:eu-repo/grantAgreement/EC/H2020/723575
    Version del Editor
    https://www.mdpi.com/2227-9717/7/3/170
    Propietario de los Derechos
    © 2019 The Authors
    Idioma
    eng
    URI
    https://uvadoc.uva.es/handle/10324/56016
    Tipo de versión
    info:eu-repo/semantics/publishedVersion
    Derechos
    openAccess
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    • DEP44 - Artículos de revista [78]
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    Universidad de Valladolid

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