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Prediction of five softwood paper properties from its density using support vector machine regression techniques

2016Journal article Open access
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dc.contributor.authorGarcía-Gonzalo, Esperanza
dc.contributor.authorSantos, António J.
dc.contributor.authorMartínez-Torres, Javier
dc.contributor.authorPereira, Helena
dc.contributor.authorSimões, Rogério
dc.contributor.authorGarcía-Nieto, Paulino
dc.contributor.authorAnjos, O.
dc.date.accessioned2016-02-18T16:00:40Z
dc.date.available2016-02-18T16:00:40Z
dc.date.issued2016
dc.description.abstractPrediction paper properties based on a limited number of measured variables can be an important tool for the industry. Mathematical models were developed to predict mechanical and optical properties from the corresponding paper density for some softwood papers using support vector machine regression with the Radial Basis Function Kemel. A dataset of different properties of paper handsheets produced from pulps of pine (Pinus pinaster and P. sylvestris) and cypress species (Cupressus lusitanica, C. sempervirens e C. arizonica) beaten at 1000, 4000, and 7000 revolutions was used. The results show that it is possible to obtain good models (with high coefficient of determination) with two variables: the numerical variable density and the categorical variable density.pt_PT
dc.identifier.citationGARCÍA-GONZALO, E. [et al.] (2016) - Prediction of five softwood paper properties from its density using support vector machine regression techniques. BioRes. 11:1, p. 1892-1904.pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.11/3183
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectVector machine regressionpt_PT
dc.subjectPaper propertiespt_PT
dc.subjectKraft pulppt_PT
dc.subjectPinus pinasterpt_PT
dc.subjectPinus sylvestrispt_PT
dc.subjectCupressus lusitanicapt_PT
dc.subjectCupressus sempervirenspt_PT
dc.subjectCupressus arizonicapt_PT
dc.titlePrediction of five softwood paper properties from its density using support vector machine regression techniquespt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage1904pt_PT
oaire.citation.startPage1892pt_PT
oaire.citation.titleBioRespt_PT
oaire.citation.volume11:1pt_PT
person.familyNameAnjos
person.givenNameOfélia
person.identifier.ciencia-idC21D-D8C7-3037
person.identifier.orcid0000-0003-0267-3252
person.identifier.ridG-2808-2012
person.identifier.scopus-author-id23395659700
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublicationdf9191ae-0bbb-4bb8-bbdc-0f79c7365876
relation.isAuthorOfPublication.latestForDiscoverydf9191ae-0bbb-4bb8-bbdc-0f79c7365876

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