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dc.contributor.author
Maffi, Juan Martín
dc.contributor.author
Estenoz, Diana Alejandra
dc.date.available
2022-09-28T19:10:03Z
dc.date.issued
2020-09
dc.identifier.citation
Maffi, Juan Martín; Estenoz, Diana Alejandra; Predicting phase inversion in agitated dispersions with machine learning algorithms; Taylor & Francis; Chemical Engineering Communications; 208; 12; 9-2020; 1757-1774
dc.identifier.issn
0098-6445
dc.identifier.uri
http://hdl.handle.net/11336/170817
dc.description.abstract
In agitated systems, the phase inversion (PI) phenomenon–the mechanism by which a dispersed phase becomes the continuous one–has been studied extensively in an empirical manner, and few models have been put forward through the years. The underlying physics are still to be fully understood. In this work, the experimental evidence published in literature is used to train machine learning models that may infer the inherent rules that lead to a given dispersion type (O/W or W/O), as well as predict the value of the dispersed phase volume fraction at the edge of the inversion point. Decision trees, bagged decision trees, support-vector machines, and multiple perceptrons are implemented and compared. Results show that it is possible to infer an ensemble of physical rules that explain why a given dispersion is O/W or W/O, where a strong “turbulence constraint” is identified. The intuitive rule that PI occurs at 50% dispersed phase almost never holds. Moreover, neural networks have shown a better performance at predicting the PI point than the other algorithms tested. Finally, a theoretical study is performed in an effort to produce a phase inversion map with the relevant operating variables. This study showed a strong nonlinear effect of the impeller-to-vessel size ratio and an asymmetrical behavior of the interfacial tension on the phase inversion points.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Taylor & Francis
dc.rights
info:eu-repo/semantics/restrictedAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
LIQUID–LIQUID DISPERSIONS
dc.subject
MACHINE LEARNING
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NEURAL NETWORK
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PHASE BEHAVIOR
dc.subject.classification
Otras Ingeniería Química
dc.subject.classification
Ingeniería Química
dc.subject.classification
INGENIERÍAS Y TECNOLOGÍAS
dc.title
Predicting phase inversion in agitated dispersions with machine learning algorithms
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2022-09-26T17:51:09Z
dc.identifier.eissn
1563-5201
dc.journal.volume
208
dc.journal.number
12
dc.journal.pagination
1757-1774
dc.journal.pais
Estados Unidos
dc.description.fil
Fil: Maffi, Juan Martín. Instituto Tecnológico de Buenos Aires. Departamento de Ingeniería Química; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
dc.description.fil
Fil: Estenoz, Diana Alejandra. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentina
dc.journal.title
Chemical Engineering Communications
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://www.tandfonline.com/doi/full/10.1080/00986445.2020.1815715
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1080/00986445.2020.1815715
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