Log-normal model linearization for particle size distribution

Detalhes bibliográficos
Autor(a) principal: Frare, Laércio Montovani
Data de Publicação: 2008
Outros Autores: Gimenes, Marcelino Luiz, Pereira, Nehemias Curvelo, Mendes, Elisabete Scolin
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.
id UEM-6_5250440de53e94b8610401703f39c00e
oai_identifier_str oai:periodicos.uem.br/ojs:article/3128
network_acronym_str UEM-6
network_name_str Acta scientiarum. Technology (Online)
repository_id_str
spelling 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

Registros relacionados