Comparison of neural networks. An estimation model in yield of monoglycerides from biodiesel by-product

Autores
Alvarez, Dolores María Eugenia; Balsamo, Nancy Florentina; Modesti, Mario Roberto; Crivello, Mónica Elsie
Año de publicación
2019
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Biodiesel is generally manufactured by transesterification, obtaining glycerol as a by-product. The transesterification of methyl stearate selectively produced monoglycerides, for glycerol valuation. Mixed oxides containing lithium catalysed the reaction. The purpose of this work was to develop and compare mathematical models obtained through artificial neural networks (ANN), capable for characterising the relationship between the mole percent conversion of methyl stearate and the yield of the products mono-, di- and triglycerides. The lowest mean squared error (MSE), the highest correlation coefficient (R), similarity in the evolution of validation and simulation errors and absence of data overlearning were considered to select the best model. Three ANNs with backpropagation structures were compared. They evidenced high correspondence between the estimated product yield values and the interpolated experimental ones. The ANN containing 35 neurons with sigmoid transfer function in the hidden layer and a linear neuron in the output one was the simplest.Consequently, the 5, 15 and 60 neurons were also explored in the hidden layer. The ANN structured with an intermediate number of neurons (35) achieved the most adequate MSE, considering mono- and diglyceride products (0.011193, 0.000489). The development of these models contributes to the dynamic estimation of the process.
Fil: Alvarez, Dolores María Eugenia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Centro de Investigación y Tecnología Química. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Centro de Investigación y Tecnología Química; Argentina
Fil: Balsamo, Nancy Florentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Centro de Investigación y Tecnología Química. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Centro de Investigación y Tecnología Química; Argentina
Fil: Modesti, Mario Roberto. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Centro de Investigación en Informática para la Ingeniería; Argentina
Fil: Crivello, Mónica Elsie. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Centro de Investigación y Tecnología Química. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Centro de Investigación y Tecnología Química; Argentina
Materia
ARTIFICIAL NEURAL NETWORK
MONOGLYCERIDES
YIELD
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/105883
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/105883
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repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Comparison of neural networks. An estimation model in yield of monoglycerides from biodiesel by-productAlvarez, Dolores María EugeniaBalsamo, Nancy FlorentinaModesti, Mario RobertoCrivello, Mónica ElsieARTIFICIAL NEURAL NETWORKMONOGLYCERIDESYIELDhttps://purl.org/becyt/ford/2.4https://purl.org/becyt/ford/2Biodiesel is generally manufactured by transesterification, obtaining glycerol as a by-product. The transesterification of methyl stearate selectively produced monoglycerides, for glycerol valuation. Mixed oxides containing lithium catalysed the reaction. The purpose of this work was to develop and compare mathematical models obtained through artificial neural networks (ANN), capable for characterising the relationship between the mole percent conversion of methyl stearate and the yield of the products mono-, di- and triglycerides. The lowest mean squared error (MSE), the highest correlation coefficient (R), similarity in the evolution of validation and simulation errors and absence of data overlearning were considered to select the best model. Three ANNs with backpropagation structures were compared. They evidenced high correspondence between the estimated product yield values and the interpolated experimental ones. The ANN containing 35 neurons with sigmoid transfer function in the hidden layer and a linear neuron in the output one was the simplest.Consequently, the 5, 15 and 60 neurons were also explored in the hidden layer. The ANN structured with an intermediate number of neurons (35) achieved the most adequate MSE, considering mono- and diglyceride products (0.011193, 0.000489). The development of these models contributes to the dynamic estimation of the process.Fil: Alvarez, Dolores María Eugenia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Centro de Investigación y Tecnología Química. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Centro de Investigación y Tecnología Química; ArgentinaFil: Balsamo, Nancy Florentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Centro de Investigación y Tecnología Química. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Centro de Investigación y Tecnología Química; ArgentinaFil: Modesti, Mario Roberto. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Centro de Investigación en Informática para la Ingeniería; ArgentinaFil: Crivello, Mónica Elsie. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Centro de Investigación y Tecnología Química. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Centro de Investigación y Tecnología Química; ArgentinaEMaTTech Journals2019-07info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/105883Alvarez, Dolores María Eugenia; Balsamo, Nancy Florentina; Modesti, Mario Roberto; Crivello, Mónica Elsie; Comparison of neural networks. An estimation model in yield of monoglycerides from biodiesel by-product; EMaTTech Journals; Journal of Engineering Science and Technology Review; 12; 4; 7-2019; 103-1071791-2377CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.25103/jestr.124.12info:eu-repo/semantics/altIdentifier/url/http://www.jestr.org/downloads/Volume12Issue4/fulltext121242019.pdfinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:27:10Zoai:ri.conicet.gov.ar:11336/105883instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 10:27:10.48CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Comparison of neural networks. An estimation model in yield of monoglycerides from biodiesel by-product
title Comparison of neural networks. An estimation model in yield of monoglycerides from biodiesel by-product
spellingShingle Comparison of neural networks. An estimation model in yield of monoglycerides from biodiesel by-product
Alvarez, Dolores María Eugenia
ARTIFICIAL NEURAL NETWORK
MONOGLYCERIDES
YIELD
title_short Comparison of neural networks. An estimation model in yield of monoglycerides from biodiesel by-product
title_full Comparison of neural networks. An estimation model in yield of monoglycerides from biodiesel by-product
title_fullStr Comparison of neural networks. An estimation model in yield of monoglycerides from biodiesel by-product
title_full_unstemmed Comparison of neural networks. An estimation model in yield of monoglycerides from biodiesel by-product
title_sort Comparison of neural networks. An estimation model in yield of monoglycerides from biodiesel by-product
dc.creator.none.fl_str_mv Alvarez, Dolores María Eugenia
Balsamo, Nancy Florentina
Modesti, Mario Roberto
Crivello, Mónica Elsie
author Alvarez, Dolores María Eugenia
author_facet Alvarez, Dolores María Eugenia
Balsamo, Nancy Florentina
Modesti, Mario Roberto
Crivello, Mónica Elsie
author_role author
author2 Balsamo, Nancy Florentina
Modesti, Mario Roberto
Crivello, Mónica Elsie
author2_role author
author
author
dc.subject.none.fl_str_mv ARTIFICIAL NEURAL NETWORK
MONOGLYCERIDES
YIELD
topic ARTIFICIAL NEURAL NETWORK
MONOGLYCERIDES
YIELD
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.4
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv Biodiesel is generally manufactured by transesterification, obtaining glycerol as a by-product. The transesterification of methyl stearate selectively produced monoglycerides, for glycerol valuation. Mixed oxides containing lithium catalysed the reaction. The purpose of this work was to develop and compare mathematical models obtained through artificial neural networks (ANN), capable for characterising the relationship between the mole percent conversion of methyl stearate and the yield of the products mono-, di- and triglycerides. The lowest mean squared error (MSE), the highest correlation coefficient (R), similarity in the evolution of validation and simulation errors and absence of data overlearning were considered to select the best model. Three ANNs with backpropagation structures were compared. They evidenced high correspondence between the estimated product yield values and the interpolated experimental ones. The ANN containing 35 neurons with sigmoid transfer function in the hidden layer and a linear neuron in the output one was the simplest.Consequently, the 5, 15 and 60 neurons were also explored in the hidden layer. The ANN structured with an intermediate number of neurons (35) achieved the most adequate MSE, considering mono- and diglyceride products (0.011193, 0.000489). The development of these models contributes to the dynamic estimation of the process.
Fil: Alvarez, Dolores María Eugenia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Centro de Investigación y Tecnología Química. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Centro de Investigación y Tecnología Química; Argentina
Fil: Balsamo, Nancy Florentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Centro de Investigación y Tecnología Química. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Centro de Investigación y Tecnología Química; Argentina
Fil: Modesti, Mario Roberto. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Centro de Investigación en Informática para la Ingeniería; Argentina
Fil: Crivello, Mónica Elsie. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Centro de Investigación y Tecnología Química. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Centro de Investigación y Tecnología Química; Argentina
description Biodiesel is generally manufactured by transesterification, obtaining glycerol as a by-product. The transesterification of methyl stearate selectively produced monoglycerides, for glycerol valuation. Mixed oxides containing lithium catalysed the reaction. The purpose of this work was to develop and compare mathematical models obtained through artificial neural networks (ANN), capable for characterising the relationship between the mole percent conversion of methyl stearate and the yield of the products mono-, di- and triglycerides. The lowest mean squared error (MSE), the highest correlation coefficient (R), similarity in the evolution of validation and simulation errors and absence of data overlearning were considered to select the best model. Three ANNs with backpropagation structures were compared. They evidenced high correspondence between the estimated product yield values and the interpolated experimental ones. The ANN containing 35 neurons with sigmoid transfer function in the hidden layer and a linear neuron in the output one was the simplest.Consequently, the 5, 15 and 60 neurons were also explored in the hidden layer. The ANN structured with an intermediate number of neurons (35) achieved the most adequate MSE, considering mono- and diglyceride products (0.011193, 0.000489). The development of these models contributes to the dynamic estimation of the process.
publishDate 2019
dc.date.none.fl_str_mv 2019-07
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/105883
Alvarez, Dolores María Eugenia; Balsamo, Nancy Florentina; Modesti, Mario Roberto; Crivello, Mónica Elsie; Comparison of neural networks. An estimation model in yield of monoglycerides from biodiesel by-product; EMaTTech Journals; Journal of Engineering Science and Technology Review; 12; 4; 7-2019; 103-107
1791-2377
CONICET Digital
CONICET
url http://hdl.handle.net/11336/105883
identifier_str_mv Alvarez, Dolores María Eugenia; Balsamo, Nancy Florentina; Modesti, Mario Roberto; Crivello, Mónica Elsie; Comparison of neural networks. An estimation model in yield of monoglycerides from biodiesel by-product; EMaTTech Journals; Journal of Engineering Science and Technology Review; 12; 4; 7-2019; 103-107
1791-2377
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.25103/jestr.124.12
info:eu-repo/semantics/altIdentifier/url/http://www.jestr.org/downloads/Volume12Issue4/fulltext121242019.pdf
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv EMaTTech Journals
publisher.none.fl_str_mv EMaTTech Journals
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1844614273833107456
score 13.070432

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