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dc.contributor.author | Borràs-Ferrís, Joan | es_ES |
dc.contributor.author | Palací-López, Daniel | es_ES |
dc.contributor.author | Duchesne, Carl | es_ES |
dc.contributor.author | Ferrer, Alberto | es_ES |
dc.date.accessioned | 2023-03-20T19:01:08Z | |
dc.date.available | 2023-03-20T19:01:08Z | |
dc.date.issued | 2022-06-15 | es_ES |
dc.identifier.issn | 0169-7439 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/192524 | |
dc.description.abstract | [EN] A novel methodology is proposed for defining multivariate raw material specifications providing assurance of quality with a certain confidence level for the critical to quality attributes (CQA) of the manufactured product. The capability of the raw material batches of producing final product with CQAs within specifications is estimated before producing a single unit of the product, and, therefore, can be used as a decision making tool to accept or reject any new supplier raw material batch. The method is based on Partial Least Squares (PLS) model inversion taking into account the prediction uncertainty and can be used with historical/happenstance data, typical in Industry 4.0. The methodology is illustrated using data from three real industrial processes. | es_ES |
dc.description.sponsorship | This work was partially funded by the Valencian Regional Government: Direccion General de Ciencia e Investigacion (AICO/2021/111), the Spanish Ministry of Economy, Industry and Competitiveness (DPI2017-82896-C2-1-R), and the European Social Fund (ACIF/2018/165). | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | Elsevier | es_ES |
dc.relation.ispartof | Chemometrics and Intelligent Laboratory Systems | es_ES |
dc.rights | Reconocimiento (by) | es_ES |
dc.subject | Industry 4.0 | es_ES |
dc.subject | Design space | es_ES |
dc.subject | Model inversion | es_ES |
dc.subject | Partial least squares | es_ES |
dc.subject | Prediction uncertainty | es_ES |
dc.subject | Raw material multivariate specifications | es_ES |
dc.subject.classification | ESTADISTICA E INVESTIGACION OPERATIVA | es_ES |
dc.title | Defining multivariate raw material specifications in industry 4.0 | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.1016/j.chemolab.2022.104563 | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/DPI2017-82896-C2-1-R/ES/DISEÑO, CARACTERIZACION Y AJUSTE OPTIMO DE BIOCIRCUITOS SINTETICOS PARA BIOPRODUCCION CON CONTROL DE CARGA METABOLICA/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/GENERALITAT VALENCIANA//AICO%2F2021%2F111//OPTIMIZACIÓN DE PROCESOS EN LA INDUSTRIA 4.0 MEDIANTE TÉCNICAS ESTADÍSTICAS MULTIVARIANTES (INDOPT4.0)/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/ESF//ACIF%2F2018%2F165/ | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Escuela Técnica Superior de Ingenieros Industriales - Escola Tècnica Superior d'Enginyers Industrials | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Departamento de Estadística e Investigación Operativa Aplicadas y Calidad - Departament d'Estadística i Investigació Operativa Aplicades i Qualitat | es_ES |
dc.description.bibliographicCitation | Borràs-Ferrís, J.; Palací-López, D.; Duchesne, C.; Ferrer, A. (2022). Defining multivariate raw material specifications in industry 4.0. Chemometrics and Intelligent Laboratory Systems. 225:1-18. https://doi.org/10.1016/j.chemolab.2022.104563 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | https://doi.org/10.1016/j.chemolab.2022.104563 | es_ES |
dc.description.upvformatpinicio | 1 | es_ES |
dc.description.upvformatpfin | 18 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 225 | es_ES |
dc.relation.pasarela | S\479284 | es_ES |
dc.contributor.funder | European Social Fund | es_ES |
dc.contributor.funder | GENERALITAT VALENCIANA | es_ES |
dc.contributor.funder | Agencia Estatal de Investigación | es_ES |
dc.contributor.funder | Universitat Politècnica de València | es_ES |