K-rank : an evolution of y-rank for multiple solutions problem
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFRGS |
Texto Completo: | http://hdl.handle.net/10183/214345 |
Resumo: | Y-rank can present faults when dealing with non-linear problems. A methodology is proposed to improve the selection of data in situations where y-rank is fragile. The proposed alternative, called k-rank, consists of splitting the data set into clusters using the k-means algorithm, and then apply y-rank to the generated clusters. Models were calibrated and tested with subsets split by y-rank and k-rank. For the Heating Tank case study, in 59% of the simulations, models calibrated with k-rank subsets achieved better results. For the Propylene / Propane Separation Unit case, when dealing with a small number of sample points, the y-rank models had errors almost three times higher than the k-rank models for the test subset, meaning that the fitted model could not deal properly with new unseen data. The proposed methodology was successful in splitting the data, especially in cases with a limited amount of samples. |
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Santos, Pedro Victor José de LimaRanzan, LucasFarenzena, MarceloTrierweiler, Jorge Otávio2020-10-23T04:09:27Z20190104-6632http://hdl.handle.net/10183/214345001118098Y-rank can present faults when dealing with non-linear problems. A methodology is proposed to improve the selection of data in situations where y-rank is fragile. The proposed alternative, called k-rank, consists of splitting the data set into clusters using the k-means algorithm, and then apply y-rank to the generated clusters. Models were calibrated and tested with subsets split by y-rank and k-rank. For the Heating Tank case study, in 59% of the simulations, models calibrated with k-rank subsets achieved better results. For the Propylene / Propane Separation Unit case, when dealing with a small number of sample points, the y-rank models had errors almost three times higher than the k-rank models for the test subset, meaning that the fitted model could not deal properly with new unseen data. The proposed methodology was successful in splitting the data, especially in cases with a limited amount of samples.application/pdfengBrazilian journal of chemical engineering [recurso eletrônico]. São Paulo. Vol. 36, no. 1 (Jan./Mar. 2019), p. 409-419Análise de dadosAlgoritmosAmostragemSplitting dataK-meansSystematic samplingMultiple solutionsK-rank : an evolution of y-rank for multiple solutions probleminfo:eu-repo/semantics/articleinfo:eu-repo/semantics/otherinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFRGSinstname:Universidade Federal do Rio Grande do Sul (UFRGS)instacron:UFRGSTEXT001118098.pdf.txt001118098.pdf.txtExtracted Texttext/plain37465http://www.lume.ufrgs.br/bitstream/10183/214345/2/001118098.pdf.txte879022ff211c8253f204d42c13da8e5MD52ORIGINAL001118098.pdfTexto completo (inglês)application/pdf951749http://www.lume.ufrgs.br/bitstream/10183/214345/1/001118098.pdf12fbef42baff33e1ac0bb4060fef7c79MD5110183/2143452021-03-09 04:33:10.901612oai:www.lume.ufrgs.br:10183/214345Repositório de PublicaçõesPUBhttps://lume.ufrgs.br/oai/requestopendoar:2021-03-09T07:33:10Repositório Institucional da UFRGS - Universidade Federal do Rio Grande do Sul (UFRGS)false |
dc.title.pt_BR.fl_str_mv |
K-rank : an evolution of y-rank for multiple solutions problem |
title |
K-rank : an evolution of y-rank for multiple solutions problem |
spellingShingle |
K-rank : an evolution of y-rank for multiple solutions problem Santos, Pedro Victor José de Lima Análise de dados Algoritmos Amostragem Splitting data K-means Systematic sampling Multiple solutions |
title_short |
K-rank : an evolution of y-rank for multiple solutions problem |
title_full |
K-rank : an evolution of y-rank for multiple solutions problem |
title_fullStr |
K-rank : an evolution of y-rank for multiple solutions problem |
title_full_unstemmed |
K-rank : an evolution of y-rank for multiple solutions problem |
title_sort |
K-rank : an evolution of y-rank for multiple solutions problem |
author |
Santos, Pedro Victor José de Lima |
author_facet |
Santos, Pedro Victor José de Lima Ranzan, Lucas Farenzena, Marcelo Trierweiler, Jorge Otávio |
author_role |
author |
author2 |
Ranzan, Lucas Farenzena, Marcelo Trierweiler, Jorge Otávio |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Santos, Pedro Victor José de Lima Ranzan, Lucas Farenzena, Marcelo Trierweiler, Jorge Otávio |
dc.subject.por.fl_str_mv |
Análise de dados Algoritmos Amostragem |
topic |
Análise de dados Algoritmos Amostragem Splitting data K-means Systematic sampling Multiple solutions |
dc.subject.eng.fl_str_mv |
Splitting data K-means Systematic sampling Multiple solutions |
description |
Y-rank can present faults when dealing with non-linear problems. A methodology is proposed to improve the selection of data in situations where y-rank is fragile. The proposed alternative, called k-rank, consists of splitting the data set into clusters using the k-means algorithm, and then apply y-rank to the generated clusters. Models were calibrated and tested with subsets split by y-rank and k-rank. For the Heating Tank case study, in 59% of the simulations, models calibrated with k-rank subsets achieved better results. For the Propylene / Propane Separation Unit case, when dealing with a small number of sample points, the y-rank models had errors almost three times higher than the k-rank models for the test subset, meaning that the fitted model could not deal properly with new unseen data. The proposed methodology was successful in splitting the data, especially in cases with a limited amount of samples. |
publishDate |
2019 |
dc.date.issued.fl_str_mv |
2019 |
dc.date.accessioned.fl_str_mv |
2020-10-23T04:09:27Z |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/other |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10183/214345 |
dc.identifier.issn.pt_BR.fl_str_mv |
0104-6632 |
dc.identifier.nrb.pt_BR.fl_str_mv |
001118098 |
identifier_str_mv |
0104-6632 001118098 |
url |
http://hdl.handle.net/10183/214345 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.ispartof.pt_BR.fl_str_mv |
Brazilian journal of chemical engineering [recurso eletrônico]. São Paulo. Vol. 36, no. 1 (Jan./Mar. 2019), p. 409-419 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.source.none.fl_str_mv |
reponame:Repositório Institucional da UFRGS instname:Universidade Federal do Rio Grande do Sul (UFRGS) instacron:UFRGS |
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Universidade Federal do Rio Grande do Sul (UFRGS) |
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UFRGS |
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UFRGS |
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Repositório Institucional da UFRGS |
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Repositório Institucional da UFRGS |
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