Density and electrical conductivity for aqueous mixtures of monoethylene glycol and sodium chloride: experimental data and data-driven modeling for composition determination
Autor(a) principal: | |
---|---|
Data de Publicação: | 2021 |
Outros Autores: | , , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFRN |
Texto Completo: | https://repositorio.ufrn.br/handle/123456789/44882 |
Resumo: | Monoethylene glycol (MEG) is a gas hydrate inhibitor widely applied for natural gas flow assurance. A series of density and electrical conductivity measurements of water + MEG + NaCl mixtures are reported, allowing the supervision of the MEG regeneration unit. Density (509 data points) and electrical conductivity (212 data points) measurements were performed in wide ranges of temperature, T = 278.15−363.15 K, and concentration of solvents and NaCl up to almost saturation. The theory of solutions was applied for density description using excess volume, which was correlated with the Redlich−Kister equation. The resulting absolute and relative mean deviations are 0.00127 g·cm−3 and 0.12%, indicating accurate representation. A semi- empirical correlation with 15 adjustable parameters was considered for electrical conductivity of water + MEG + NaCl mixtures. The obtained absolute and relative mean deviations are 1.49 mS·cm−1 and 5.70%. The properties functions presented an approximately orthogonal behavior to each other, allowing the determination of mixture composition from experimental density and electrical conductivity data. The Matlab environment was found to be robust in solving the nonlinear system of two equations with constraints. The proposed methodology was extensively tested, and deviations less than 0.0060 and 0.0011 in solvents and NaCl mass fractions were obtained, respectively, demonstrating the required accuracy for industrial application |
id |
UFRN_6a7a7353e353ed9f97c009ec8ca83a0a |
---|---|
oai_identifier_str |
oai:https://repositorio.ufrn.br:123456789/44882 |
network_acronym_str |
UFRN |
network_name_str |
Repositório Institucional da UFRN |
repository_id_str |
|
spelling |
Chiavone Filho, OsvaldoMoura Neto, Mário Hermes deMonteiro, Mateus FernandesFerreira, Fedra A. V.Silva, Dannielle JanainneFigueiredo, Camila S.Ciambelli, João Rafael PerroniPereira, Leonardo S.Nascimento, Jailton Ferreira do2021-11-10T21:33:54Z2021-11-10T21:33:54Z2021-04-09MOURA-NETO, MARIO H.; MONTEIRO, Mateus Fernandes; FERREIRA, F. A. S. V. M. ; SILVA, D. J. ; FIGUEIREDO, C. S. ; CIAMBELLI, J. R. P. ; PEREIRA, L. S. ; DO NASCIMENTO, JAILTON FERREIRA ; CHIAVONE-FILHO, O. . Density and Electrical Conductivity for Aqueous Mixtures of Monoethylene Glycol and Sodium Chloride: Experimental Data and Data?driven Modeling for Composition Determination. JOURNAL OF CHEMICAL AND ENGINEERING DATA, v. 66, p. 1-15, 2021. Disponível em: https://pubs.acs.org/doi/10.1021/acs.jced.0c00962. Acesso em: 16 jun. 2021.https://doi.org/10.1021/acs.jced.0c00962.0021-95681520-5134https://repositorio.ufrn.br/handle/123456789/4488210.1021/acs.jced.0c00962ACS PublicationsAttribution-NonCommercial-NoDerivs 3.0 Brazilhttp://creativecommons.org/licenses/by-nc-nd/3.0/br/info:eu-repo/semantics/openAccessDensity and Electrical ConductivityMonoethylene GlycolData-DrivenComposition DeterminationDensity and electrical conductivity for aqueous mixtures of monoethylene glycol and sodium chloride: experimental data and data-driven modeling for composition determinationinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleMonoethylene glycol (MEG) is a gas hydrate inhibitor widely applied for natural gas flow assurance. A series of density and electrical conductivity measurements of water + MEG + NaCl mixtures are reported, allowing the supervision of the MEG regeneration unit. Density (509 data points) and electrical conductivity (212 data points) measurements were performed in wide ranges of temperature, T = 278.15−363.15 K, and concentration of solvents and NaCl up to almost saturation. The theory of solutions was applied for density description using excess volume, which was correlated with the Redlich−Kister equation. The resulting absolute and relative mean deviations are 0.00127 g·cm−3 and 0.12%, indicating accurate representation. A semi- empirical correlation with 15 adjustable parameters was considered for electrical conductivity of water + MEG + NaCl mixtures. The obtained absolute and relative mean deviations are 1.49 mS·cm−1 and 5.70%. The properties functions presented an approximately orthogonal behavior to each other, allowing the determination of mixture composition from experimental density and electrical conductivity data. The Matlab environment was found to be robust in solving the nonlinear system of two equations with constraints. The proposed methodology was extensively tested, and deviations less than 0.0060 and 0.0011 in solvents and NaCl mass fractions were obtained, respectively, demonstrating the required accuracy for industrial applicationengreponame:Repositório Institucional da UFRNinstname:Universidade Federal do Rio Grande do Norte (UFRN)instacron:UFRNCC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8811https://repositorio.ufrn.br/bitstream/123456789/44882/2/license_rdfe39d27027a6cc9cb039ad269a5db8e34MD52ORIGINALDensityElectricalConductivity_ChiavoneFilho_2021.pdfDensityElectricalConductivity_ChiavoneFilho_2021.pdfapplication/pdf5083796https://repositorio.ufrn.br/bitstream/123456789/44882/1/DensityElectricalConductivity_ChiavoneFilho_2021.pdf52b295beda69d488438626263c515b7cMD51LICENSElicense.txtlicense.txttext/plain; charset=utf-81484https://repositorio.ufrn.br/bitstream/123456789/44882/3/license.txte9597aa2854d128fd968be5edc8a28d9MD53123456789/448822021-11-12 11:29:18.127oai:https://repositorio.ufrn.br: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Repositório de PublicaçõesPUBhttp://repositorio.ufrn.br/oai/opendoar:2021-11-12T14:29:18Repositório Institucional da UFRN - Universidade Federal do Rio Grande do Norte (UFRN)false |
dc.title.pt_BR.fl_str_mv |
Density and electrical conductivity for aqueous mixtures of monoethylene glycol and sodium chloride: experimental data and data-driven modeling for composition determination |
title |
Density and electrical conductivity for aqueous mixtures of monoethylene glycol and sodium chloride: experimental data and data-driven modeling for composition determination |
spellingShingle |
Density and electrical conductivity for aqueous mixtures of monoethylene glycol and sodium chloride: experimental data and data-driven modeling for composition determination Chiavone Filho, Osvaldo Density and Electrical Conductivity Monoethylene Glycol Data-Driven Composition Determination |
title_short |
Density and electrical conductivity for aqueous mixtures of monoethylene glycol and sodium chloride: experimental data and data-driven modeling for composition determination |
title_full |
Density and electrical conductivity for aqueous mixtures of monoethylene glycol and sodium chloride: experimental data and data-driven modeling for composition determination |
title_fullStr |
Density and electrical conductivity for aqueous mixtures of monoethylene glycol and sodium chloride: experimental data and data-driven modeling for composition determination |
title_full_unstemmed |
Density and electrical conductivity for aqueous mixtures of monoethylene glycol and sodium chloride: experimental data and data-driven modeling for composition determination |
title_sort |
Density and electrical conductivity for aqueous mixtures of monoethylene glycol and sodium chloride: experimental data and data-driven modeling for composition determination |
author |
Chiavone Filho, Osvaldo |
author_facet |
Chiavone Filho, Osvaldo Moura Neto, Mário Hermes de Monteiro, Mateus Fernandes Ferreira, Fedra A. V. Silva, Dannielle Janainne Figueiredo, Camila S. Ciambelli, João Rafael Perroni Pereira, Leonardo S. Nascimento, Jailton Ferreira do |
author_role |
author |
author2 |
Moura Neto, Mário Hermes de Monteiro, Mateus Fernandes Ferreira, Fedra A. V. Silva, Dannielle Janainne Figueiredo, Camila S. Ciambelli, João Rafael Perroni Pereira, Leonardo S. Nascimento, Jailton Ferreira do |
author2_role |
author author author author author author author author |
dc.contributor.author.fl_str_mv |
Chiavone Filho, Osvaldo Moura Neto, Mário Hermes de Monteiro, Mateus Fernandes Ferreira, Fedra A. V. Silva, Dannielle Janainne Figueiredo, Camila S. Ciambelli, João Rafael Perroni Pereira, Leonardo S. Nascimento, Jailton Ferreira do |
dc.subject.por.fl_str_mv |
Density and Electrical Conductivity Monoethylene Glycol Data-Driven Composition Determination |
topic |
Density and Electrical Conductivity Monoethylene Glycol Data-Driven Composition Determination |
description |
Monoethylene glycol (MEG) is a gas hydrate inhibitor widely applied for natural gas flow assurance. A series of density and electrical conductivity measurements of water + MEG + NaCl mixtures are reported, allowing the supervision of the MEG regeneration unit. Density (509 data points) and electrical conductivity (212 data points) measurements were performed in wide ranges of temperature, T = 278.15−363.15 K, and concentration of solvents and NaCl up to almost saturation. The theory of solutions was applied for density description using excess volume, which was correlated with the Redlich−Kister equation. The resulting absolute and relative mean deviations are 0.00127 g·cm−3 and 0.12%, indicating accurate representation. A semi- empirical correlation with 15 adjustable parameters was considered for electrical conductivity of water + MEG + NaCl mixtures. The obtained absolute and relative mean deviations are 1.49 mS·cm−1 and 5.70%. The properties functions presented an approximately orthogonal behavior to each other, allowing the determination of mixture composition from experimental density and electrical conductivity data. The Matlab environment was found to be robust in solving the nonlinear system of two equations with constraints. The proposed methodology was extensively tested, and deviations less than 0.0060 and 0.0011 in solvents and NaCl mass fractions were obtained, respectively, demonstrating the required accuracy for industrial application |
publishDate |
2021 |
dc.date.accessioned.fl_str_mv |
2021-11-10T21:33:54Z |
dc.date.available.fl_str_mv |
2021-11-10T21:33:54Z |
dc.date.issued.fl_str_mv |
2021-04-09 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.citation.fl_str_mv |
MOURA-NETO, MARIO H.; MONTEIRO, Mateus Fernandes; FERREIRA, F. A. S. V. M. ; SILVA, D. J. ; FIGUEIREDO, C. S. ; CIAMBELLI, J. R. P. ; PEREIRA, L. S. ; DO NASCIMENTO, JAILTON FERREIRA ; CHIAVONE-FILHO, O. . Density and Electrical Conductivity for Aqueous Mixtures of Monoethylene Glycol and Sodium Chloride: Experimental Data and Data?driven Modeling for Composition Determination. JOURNAL OF CHEMICAL AND ENGINEERING DATA, v. 66, p. 1-15, 2021. Disponível em: https://pubs.acs.org/doi/10.1021/acs.jced.0c00962. Acesso em: 16 jun. 2021.https://doi.org/10.1021/acs.jced.0c00962. |
dc.identifier.uri.fl_str_mv |
https://repositorio.ufrn.br/handle/123456789/44882 |
dc.identifier.issn.none.fl_str_mv |
0021-9568 1520-5134 |
dc.identifier.doi.none.fl_str_mv |
10.1021/acs.jced.0c00962 |
identifier_str_mv |
MOURA-NETO, MARIO H.; MONTEIRO, Mateus Fernandes; FERREIRA, F. A. S. V. M. ; SILVA, D. J. ; FIGUEIREDO, C. S. ; CIAMBELLI, J. R. P. ; PEREIRA, L. S. ; DO NASCIMENTO, JAILTON FERREIRA ; CHIAVONE-FILHO, O. . Density and Electrical Conductivity for Aqueous Mixtures of Monoethylene Glycol and Sodium Chloride: Experimental Data and Data?driven Modeling for Composition Determination. JOURNAL OF CHEMICAL AND ENGINEERING DATA, v. 66, p. 1-15, 2021. Disponível em: https://pubs.acs.org/doi/10.1021/acs.jced.0c00962. Acesso em: 16 jun. 2021.https://doi.org/10.1021/acs.jced.0c00962. 0021-9568 1520-5134 10.1021/acs.jced.0c00962 |
url |
https://repositorio.ufrn.br/handle/123456789/44882 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
Attribution-NonCommercial-NoDerivs 3.0 Brazil http://creativecommons.org/licenses/by-nc-nd/3.0/br/ info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Attribution-NonCommercial-NoDerivs 3.0 Brazil http://creativecommons.org/licenses/by-nc-nd/3.0/br/ |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
ACS Publications |
publisher.none.fl_str_mv |
ACS Publications |
dc.source.none.fl_str_mv |
reponame:Repositório Institucional da UFRN instname:Universidade Federal do Rio Grande do Norte (UFRN) instacron:UFRN |
instname_str |
Universidade Federal do Rio Grande do Norte (UFRN) |
instacron_str |
UFRN |
institution |
UFRN |
reponame_str |
Repositório Institucional da UFRN |
collection |
Repositório Institucional da UFRN |
bitstream.url.fl_str_mv |
https://repositorio.ufrn.br/bitstream/123456789/44882/2/license_rdf https://repositorio.ufrn.br/bitstream/123456789/44882/1/DensityElectricalConductivity_ChiavoneFilho_2021.pdf https://repositorio.ufrn.br/bitstream/123456789/44882/3/license.txt |
bitstream.checksum.fl_str_mv |
e39d27027a6cc9cb039ad269a5db8e34 52b295beda69d488438626263c515b7c e9597aa2854d128fd968be5edc8a28d9 |
bitstream.checksumAlgorithm.fl_str_mv |
MD5 MD5 MD5 |
repository.name.fl_str_mv |
Repositório Institucional da UFRN - Universidade Federal do Rio Grande do Norte (UFRN) |
repository.mail.fl_str_mv |
|
_version_ |
1814832792265031680 |