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dc.contributor.author | Borràs-Ferrís, Joan![]() |
es_ES |
dc.contributor.author | Duchesne, Carl![]() |
es_ES |
dc.contributor.author | Ferrer, Alberto![]() |
es_ES |
dc.date.accessioned | 2024-06-12T18:19:45Z | |
dc.date.available | 2024-06-12T18:19:45Z | |
dc.date.issued | 2023-09-15 | es_ES |
dc.identifier.issn | 0169-7439 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/205109 | |
dc.description.abstract | [EN] The Sequential Multi-Block Partial Least Squares (SMB-PLS) model inversion is applied for defining analytically the multivariate raw material region providing assurance of quality with a certain confidence level for the critical to quality attributes (CQA). The SMB-PLS algorithm does identify the variation in process conditions uncorre-lated with raw material properties and known disturbances, which is crucial to implement an effective process control system attenuating most raw material variations. This allows expanding the specification region and, hence, one may potentially be able to accept lower cost raw materials that will yield products with perfectly satisfactory quality properties. The methodology can be used with historical/happenstance data, typical in In-dustry 4.0. This is illustrated using simulated data from an industrial case study. | es_ES |
dc.description.sponsorship | This work was partially supported by the Spanish Ministry of Science and Innovation (PID2020-119262RB-I00) , the Generalitat Valenciana (AICO/2021/111) 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 | Multivariate specifications | es_ES |
dc.subject | Raw materials | es_ES |
dc.subject | Design space | es_ES |
dc.subject | Industry 4,0 | es_ES |
dc.subject | SMB-PLS | es_ES |
dc.subject.classification | ESTADISTICA E INVESTIGACION OPERATIVA | es_ES |
dc.title | Defining multivariate raw material specifications via SMB-PLS | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.1016/j.chemolab.2023.104912 | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-119262RB-I00/ES/TECNICAS ESTADISTICAS MULTIVARIANTES BASADAS EN VARIABLES LATENTES PARA EL DESARROLLO DE BIOMARCADORES DE IMAGEN PARA LA DIAGNOSIS Y PROGNOSIS DE CANCER DE MAMA/ | 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.description.bibliographicCitation | Borràs-Ferrís, J.; Duchesne, C.; Ferrer, A. (2023). Defining multivariate raw material specifications via SMB-PLS. Chemometrics and Intelligent Laboratory Systems. 240. https://doi.org/10.1016/j.chemolab.2023.104912 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | https://doi.org/10.1016/j.chemolab.2023.104912 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 240 | es_ES |
dc.relation.pasarela | S\499430 | es_ES |
dc.contributor.funder | European Social Fund | es_ES |
dc.contributor.funder | GENERALITAT VALENCIANA | es_ES |
dc.contributor.funder | AGENCIA ESTATAL DE INVESTIGACION | es_ES |
dc.contributor.funder | Universitat Politècnica de València | es_ES |