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dc.contributor.CRUESPUNIVERSIDADE ESTADUAL DE CAMPINASpt_BR
dc.contributor.authorunicampFilgueiras, Paulo Roberto-
dc.contributor.authorunicampPoppi, Ronei Jesus-
dc.typeArtigopt_BR
dc.titleDetermination of API gravity, kinematic viscosity and water content in petroleum by ATR-FTIR spectroscopy and multivariate calibrationpt_BR
dc.contributor.authorFilgueiras, P. R.-
dc.contributor.authorSad, C. M. S.-
dc.contributor.authorDias, J. C. M.-
dc.contributor.authorCastro, E. V. R.-
dc.contributor.authorLoureiro, A. R.-
dc.contributor.authorPoppi, R. J.,-
dc.contributor.authorSantos, M. F. P-
dc.subjectÓleo crupt_BR
dc.subject.otherlanguageCrude oilpt_BR
dc.description.abstractIn this work, API gravity, kinematic viscosity and water content were determined in petroleum oil using Fourier transform infrared spectroscopy with attenuated total reflectance (FT-IR/ATR). Support vector regression (SVR) was used as the non-linear multivariate calibration procedure and partial least squares regression (PLS) as the linear procedure. In SVR models, the multiplication of the spectra matrix by support vectors resulted in information about the importance of the original variables. The most important variables in PLS models were attained by regression coefficients. For API gravity and kinematic viscosity these variables correspond to vibrations around 2900 cm(-1), 1450 cm(-1) and below to 720 cm(-1) and for water content, between 3200 and 3650 cm(-1), around 1650 cm(-1) and below to 900 cm(-1). The SVR model produced a root mean square error of prediction (RMSEP) of 0.25 for API gravity, 22 mm(2) s(-1) for kinematic viscosity and 0.26% v/v for water content. For PLS models, the RMSEP values for API gravity was 0.38 mm(2) s(-1), for kinematic viscosity was 27 mm(2) s(-1) and for water content was 0.34%. Using the F-test at 95% of confidence it was concluded that the SVR model produced better results than PLS for API gravity determination. For kinematic viscosity and water content the two methods were equivalent. However, a non-linear behavior in the PLS kinematic viscosity model was observedpt_BR
dc.relation.ispartofFuelpt_BR
dc.publisher.cityOxfordpt_BR
dc.publisher.countryReino Unidopt_BR
dc.publisherElsevierpt_BR
dc.date.issued2014-
dc.date.monthofcirculationJan.pt_BR
dc.language.isoengpt_BR
dc.description.volume116pt_BR
dc.description.firstpage123pt_BR
dc.description.lastpage130pt_BR
dc.rightsFechadopt_BR
dc.sourceWOSpt_BR
dc.identifier.issn0016-2361pt_BR
dc.identifier.eissn1873-7153pt_BR
dc.identifier.doi10.1016/j.fuel.2013.07.122pt_BR
dc.identifier.urlhttps://www.sciencedirect.com/science/article/pii/S0016236113007217pt_BR
dc.description.sponsorshipCONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQpt_BR
dc.date.available2020-10-13T15:26:22Z-
dc.date.accessioned2020-10-13T15:26:22Z-
dc.description.provenanceSubmitted by Cintia Oliveira de Moura (cintiaom@unicamp.br) on 2020-10-13T15:26:22Z No. of bitstreams: 0. Added 1 bitstream(s) on 2021-02-16T16:52:24Z : No. of bitstreams: 1 000326943400018.pdf: 794328 bytes, checksum: 303d6f8726ad72e3b471a38cd7c7758a (MD5)en
dc.description.provenanceMade available in DSpace on 2020-10-13T15:26:22Z (GMT). No. of bitstreams: 0 Previous issue date: 2014en
dc.identifier.urihttp://repositorio.unicamp.br/jspui/handle/REPOSIP/350909-
dc.contributor.departmentsem informaçãopt_BR
dc.contributor.departmentDepartamento de Química Analíticapt_BR
dc.contributor.unidadeInstituto de Químicapt_BR
dc.subject.keywordPartial least squares regressionpt_BR
dc.subject.keywordSupport vector regressionpt_BR
dc.identifier.source000326943400018pt_BR
dc.creator.orcid0000-0003-2617-1601pt_BR
dc.creator.orcid0000-0003-2994-0787pt_BR
dc.type.formArtigopt_BR
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