Log-normal model linearization for particle size distribution
Autor(a) principal: | |
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Data de Publicação: | 2008 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | por |
Título da fonte: | Acta scientiarum. Technology (Online) |
Texto Completo: | http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/3128 |
Resumo: | Granulometric analyses of solids are satisfactorily represented by the following two parameters models: Gates-Gaudin-Schumann (GGS), Rosin-Rammler-Bennet (RRB) and Log-Normal (LN). GGS and RRB models may be linearized to get a correlation coefficient to qualify them. Nevertheless, for LN model the linear fit is done by a particle diameter graph in logarithm scale versus the cumulative mass fraction in a probability scale. It’s not possible to compare the three models on the same basis. Equations developed by Lawless (1978) were developed to obtain a linear correlation coefficient for LN model. Thus, GGS, RRB and LN models may be available by simple comparison of the linear regression coefficients. Adjustment turns up to the faster and more precise. |
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Log-normal model linearization for particle size distributionLinearização do modelo log-normal para distribuição de tamanho de partículastamanho de partículasmodelos de distribuiçãoanálise granulométrica3.06.00.00-6 Engenharia QuímicaGranulometric analyses of solids are satisfactorily represented by the following two parameters models: Gates-Gaudin-Schumann (GGS), Rosin-Rammler-Bennet (RRB) and Log-Normal (LN). GGS and RRB models may be linearized to get a correlation coefficient to qualify them. Nevertheless, for LN model the linear fit is done by a particle diameter graph in logarithm scale versus the cumulative mass fraction in a probability scale. It’s not possible to compare the three models on the same basis. Equations developed by Lawless (1978) were developed to obtain a linear correlation coefficient for LN model. Thus, GGS, RRB and LN models may be available by simple comparison of the linear regression coefficients. Adjustment turns up to the faster and more precise.As análises granulométricas de sólidos podem ser satisfatoriamente representadas pelos seguintes modelos de distribuição a 2 parâmetros: Gates-Gaudin-Schumann (GGS), Rosin-Rammler-Bennet (RRB) e Log-Normal (LN). Os modelos GGS e RRB podem ser linearizados, obtendo-se um coeficiente de correlação que permite avaliar a qualidade do ajuste. Entretanto, para o modelo LN, o ajuste linear é feito através de um gráfico do diâmetro de partícula em escala logarítmica versus fração cumulativa em escala de probabilidades, não sendo possível comparar seu ajuste com os dois anteriores, sob uma mesma base. Sendo assim, partiu-se neste trabalho de um conjunto de equações desenvolvidas por LAWLESS (1978), que possibilita a obtenção de um coeficiente de correlação linear para o modelo LN. Desta forma, os modelos GGS, RRB e LN podem agora ser avaliados por meio da comparação dos coeficientes envolvidos na regressão linear, tornando o trabalho de ajuste mais ágil e preciso.Universidade Estadual De Maringá2008-05-13info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/312810.4025/actascitechnol.v22i0.3128Acta Scientiarum. Technology; Vol 22 (2000); 1235-1239Acta Scientiarum. Technology; v. 22 (2000); 1235-12391806-25631807-8664reponame:Acta scientiarum. Technology (Online)instname:Universidade Estadual de Maringá (UEM)instacron:UEMporhttp://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/3128/2245Frare, Laércio MontovaniGimenes, Marcelino LuizPereira, Nehemias CurveloMendes, Elisabete Scolininfo:eu-repo/semantics/openAccess2024-05-17T13:02:58Zoai:periodicos.uem.br/ojs:article/3128Revistahttps://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/indexPUBhttps://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/oai||actatech@uem.br1807-86641806-2563opendoar:2024-05-17T13:02:58Acta scientiarum. Technology (Online) - Universidade Estadual de Maringá (UEM)false |
dc.title.none.fl_str_mv |
Log-normal model linearization for particle size distribution Linearização do modelo log-normal para distribuição de tamanho de partículas |
title |
Log-normal model linearization for particle size distribution |
spellingShingle |
Log-normal model linearization for particle size distribution Frare, Laércio Montovani tamanho de partículas modelos de distribuição análise granulométrica 3.06.00.00-6 Engenharia Química |
title_short |
Log-normal model linearization for particle size distribution |
title_full |
Log-normal model linearization for particle size distribution |
title_fullStr |
Log-normal model linearization for particle size distribution |
title_full_unstemmed |
Log-normal model linearization for particle size distribution |
title_sort |
Log-normal model linearization for particle size distribution |
author |
Frare, Laércio Montovani |
author_facet |
Frare, Laércio Montovani Gimenes, Marcelino Luiz Pereira, Nehemias Curvelo Mendes, Elisabete Scolin |
author_role |
author |
author2 |
Gimenes, Marcelino Luiz Pereira, Nehemias Curvelo Mendes, Elisabete Scolin |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Frare, Laércio Montovani Gimenes, Marcelino Luiz Pereira, Nehemias Curvelo Mendes, Elisabete Scolin |
dc.subject.por.fl_str_mv |
tamanho de partículas modelos de distribuição análise granulométrica 3.06.00.00-6 Engenharia Química |
topic |
tamanho de partículas modelos de distribuição análise granulométrica 3.06.00.00-6 Engenharia Química |
description |
Granulometric analyses of solids are satisfactorily represented by the following two parameters models: Gates-Gaudin-Schumann (GGS), Rosin-Rammler-Bennet (RRB) and Log-Normal (LN). GGS and RRB models may be linearized to get a correlation coefficient to qualify them. Nevertheless, for LN model the linear fit is done by a particle diameter graph in logarithm scale versus the cumulative mass fraction in a probability scale. It’s not possible to compare the three models on the same basis. Equations developed by Lawless (1978) were developed to obtain a linear correlation coefficient for LN model. Thus, GGS, RRB and LN models may be available by simple comparison of the linear regression coefficients. Adjustment turns up to the faster and more precise. |
publishDate |
2008 |
dc.date.none.fl_str_mv |
2008-05-13 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/3128 10.4025/actascitechnol.v22i0.3128 |
url |
http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/3128 |
identifier_str_mv |
10.4025/actascitechnol.v22i0.3128 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/3128/2245 |
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.publisher.none.fl_str_mv |
Universidade Estadual De Maringá |
publisher.none.fl_str_mv |
Universidade Estadual De Maringá |
dc.source.none.fl_str_mv |
Acta Scientiarum. Technology; Vol 22 (2000); 1235-1239 Acta Scientiarum. Technology; v. 22 (2000); 1235-1239 1806-2563 1807-8664 reponame:Acta scientiarum. Technology (Online) instname:Universidade Estadual de Maringá (UEM) instacron:UEM |
instname_str |
Universidade Estadual de Maringá (UEM) |
instacron_str |
UEM |
institution |
UEM |
reponame_str |
Acta scientiarum. Technology (Online) |
collection |
Acta scientiarum. Technology (Online) |
repository.name.fl_str_mv |
Acta scientiarum. Technology (Online) - Universidade Estadual de Maringá (UEM) |
repository.mail.fl_str_mv |
||actatech@uem.br |
_version_ |
1799315333096407040 |