StatSSCandlePlot : a new way of monitoring operational performance indicators
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/10183/214313 |
Resumo: | Operational KPIs play an extremely important role in the process industry, aiding in decision making. However, they need to be reliably calculated to be representative. The present work presents a schematic methodology for the calculation of these KPIs, including techniques of steady-state detection, denoising, error propagation and sensitivity analysis, presented, as far as it is known, in the form of a new graphical tool proposed by the authors named StatSSCandlePlot. The methodology was applied in a real case study of a gas fired boiler in which the indicator studied was its efficiency evaluated by the Stack Loss Method. From the StatSSCandlePlot it was possible to identify the trends of the indicator, the portion of each window in the steady-state, the values to be considered from the indicator and, in a complementary way, to identify the variable that most influences the variation of the indicator, through the sensitivity analysis. |
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Giaquinto, Cláudia Daniela MeloTrierweiler, Jorge OtávioTeixeira, Herbert Campos GonçalvesTrierweiler, Luciane FerreiraFarenzena, Marcelo2020-10-22T04:06:39Z20190104-6632http://hdl.handle.net/10183/214313001118095Operational KPIs play an extremely important role in the process industry, aiding in decision making. However, they need to be reliably calculated to be representative. The present work presents a schematic methodology for the calculation of these KPIs, including techniques of steady-state detection, denoising, error propagation and sensitivity analysis, presented, as far as it is known, in the form of a new graphical tool proposed by the authors named StatSSCandlePlot. The methodology was applied in a real case study of a gas fired boiler in which the indicator studied was its efficiency evaluated by the Stack Loss Method. From the StatSSCandlePlot it was possible to identify the trends of the indicator, the portion of each window in the steady-state, the values to be considered from the indicator and, in a complementary way, to identify the variable that most influences the variation of the indicator, through the sensitivity analysis.application/pdfengBrazilian journal of chemical engineering [recurso eletrônico]. São Paulo. Vol. 36, no. 1 (Jan./Mar. 2019), p. 393-408Indicadores de desempenhoProcessos industriaisGráficosKey performance indicatorsCandlestick chartProcess assessmentStatSSCandlePlot : a new way of monitoring operational performance indicatorsinfo: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:UFRGSTEXT001118095.pdf.txt001118095.pdf.txtExtracted Texttext/plain60707http://www.lume.ufrgs.br/bitstream/10183/214313/2/001118095.pdf.txt2ef5d10ada917071e5c39d5a676f50b9MD52ORIGINAL001118095.pdfTexto completo (inglês)application/pdf827885http://www.lume.ufrgs.br/bitstream/10183/214313/1/001118095.pdff50ed86b5ad5a96dbc3ccc11ddc43db7MD5110183/2143132021-03-09 04:30:32.598603oai:www.lume.ufrgs.br:10183/214313Repositório de PublicaçõesPUBhttps://lume.ufrgs.br/oai/requestopendoar:2021-03-09T07:30:32Repositório Institucional da UFRGS - Universidade Federal do Rio Grande do Sul (UFRGS)false |
dc.title.pt_BR.fl_str_mv |
StatSSCandlePlot : a new way of monitoring operational performance indicators |
title |
StatSSCandlePlot : a new way of monitoring operational performance indicators |
spellingShingle |
StatSSCandlePlot : a new way of monitoring operational performance indicators Giaquinto, Cláudia Daniela Melo Indicadores de desempenho Processos industriais Gráficos Key performance indicators Candlestick chart Process assessment |
title_short |
StatSSCandlePlot : a new way of monitoring operational performance indicators |
title_full |
StatSSCandlePlot : a new way of monitoring operational performance indicators |
title_fullStr |
StatSSCandlePlot : a new way of monitoring operational performance indicators |
title_full_unstemmed |
StatSSCandlePlot : a new way of monitoring operational performance indicators |
title_sort |
StatSSCandlePlot : a new way of monitoring operational performance indicators |
author |
Giaquinto, Cláudia Daniela Melo |
author_facet |
Giaquinto, Cláudia Daniela Melo Trierweiler, Jorge Otávio Teixeira, Herbert Campos Gonçalves Trierweiler, Luciane Ferreira Farenzena, Marcelo |
author_role |
author |
author2 |
Trierweiler, Jorge Otávio Teixeira, Herbert Campos Gonçalves Trierweiler, Luciane Ferreira Farenzena, Marcelo |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Giaquinto, Cláudia Daniela Melo Trierweiler, Jorge Otávio Teixeira, Herbert Campos Gonçalves Trierweiler, Luciane Ferreira Farenzena, Marcelo |
dc.subject.por.fl_str_mv |
Indicadores de desempenho Processos industriais Gráficos |
topic |
Indicadores de desempenho Processos industriais Gráficos Key performance indicators Candlestick chart Process assessment |
dc.subject.eng.fl_str_mv |
Key performance indicators Candlestick chart Process assessment |
description |
Operational KPIs play an extremely important role in the process industry, aiding in decision making. However, they need to be reliably calculated to be representative. The present work presents a schematic methodology for the calculation of these KPIs, including techniques of steady-state detection, denoising, error propagation and sensitivity analysis, presented, as far as it is known, in the form of a new graphical tool proposed by the authors named StatSSCandlePlot. The methodology was applied in a real case study of a gas fired boiler in which the indicator studied was its efficiency evaluated by the Stack Loss Method. From the StatSSCandlePlot it was possible to identify the trends of the indicator, the portion of each window in the steady-state, the values to be considered from the indicator and, in a complementary way, to identify the variable that most influences the variation of the indicator, through the sensitivity analysis. |
publishDate |
2019 |
dc.date.issued.fl_str_mv |
2019 |
dc.date.accessioned.fl_str_mv |
2020-10-22T04:06:39Z |
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/10183/214313 |
dc.identifier.issn.pt_BR.fl_str_mv |
0104-6632 |
dc.identifier.nrb.pt_BR.fl_str_mv |
001118095 |
identifier_str_mv |
0104-6632 001118095 |
url |
http://hdl.handle.net/10183/214313 |
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. 393-408 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
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application/pdf |
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reponame:Repositório Institucional da UFRGS instname:Universidade Federal do Rio Grande do Sul (UFRGS) instacron:UFRGS |
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Repositório Institucional da UFRGS |
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