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dc.contributor.author
Perez Correa, Ignacio
dc.contributor.author
Giunta, Pablo Daniel
dc.contributor.author
Francesconi, Javier Andres
dc.contributor.author
Mariño, Fernando Javier
dc.date.available
2023-09-26T15:28:32Z
dc.date.issued
2022-11
dc.identifier.citation
Perez Correa, Ignacio; Giunta, Pablo Daniel; Francesconi, Javier Andres; Mariño, Fernando Javier; Artificial neural network for the prediction of physical properties of organic compounds based on the group contribution method; John Wiley & Sons; The Canadian Journal Of Chemical Engineering; 101; 8; 11-2022; 4771-4783
dc.identifier.issn
0008-4034
dc.identifier.uri
http://hdl.handle.net/11336/213074
dc.description.abstract
In the development and optimization of chemical processes involving the selection of organic fluids, knowledge of the physical properties of compounds is vital. In many cases, it is complex to find experimental measurements for all substances, so it becomes necessary to have a tool to predict properties based on the characteristics of the molecule. One of the most extensively used methods in the literature is the estimation by contribution of functional groups, where properties are calculated using the constituent elements of the molecule. There are several models published in the literature, but they fail to represent a wide variety of compounds with high accuracy and simultaneously maintain a low computational complexity. The aim of this work is to develop a prediction model for eight thermodynamic properties (melting temperature, boiling temperature, critical pressure, critical temperature, critical volume, enthalpy of vaporization, enthalpy of fusion, and enthalpy of gas formation) based on the group contribution methodology by implementing a multilayer perceptron. Here, 2736 substances were used to train the neural network, whose prediction capacity was compared with other reference models available in the literature. The proposed model presents errors ranging from 1% to 5% for the different properties (except for the melting point), which improves the reference models with errors in the range of 3%–30%. Nevertheless, a difficulty in the prediction of the melting point is detected, which could represent an inherent hindrance to this methodology.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
John Wiley & Sons
dc.rights
info:eu-repo/semantics/restrictedAccess
dc.rights
Atribución-NoComercial-CompartirIgual 2.5 Argentina (CC BY-NC-SA 2.5 AR)
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
ARTIFICIAL NEURAL NETWORK
dc.subject
GROUP CONTRIBUTION MODEL
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ORGANIC FLUIDS
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PROPERTY PREDICTION
dc.subject.classification
Ingeniería de Procesos Químicos
dc.subject.classification
Ingeniería Química
dc.subject.classification
INGENIERÍAS Y TECNOLOGÍAS
dc.title
Artificial neural network for the prediction of physical properties of organic compounds based on the group contribution method
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2023-07-07T22:39:41Z
dc.journal.volume
101
dc.journal.number
8
dc.journal.pagination
4771-4783
dc.journal.pais
Estados Unidos
dc.journal.ciudad
Nueva Jersey
dc.description.fil
Fil: Perez Correa, Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Tecnologías del Hidrogeno y Energias Sostenibles. Universidad de Buenos Aires. Facultad de Ingeniería. Instituto de Tecnologías del Hidrogeno y Energias Sostenibles; Argentina
dc.description.fil
Fil: Giunta, Pablo Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Tecnologías del Hidrogeno y Energias Sostenibles. Universidad de Buenos Aires. Facultad de Ingeniería. Instituto de Tecnologías del Hidrogeno y Energias Sostenibles; Argentina
dc.description.fil
Fil: Francesconi, Javier Andres. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Tecnologías del Hidrogeno y Energias Sostenibles. Universidad de Buenos Aires. Facultad de Ingeniería. Instituto de Tecnologías del Hidrogeno y Energias Sostenibles; Argentina
dc.description.fil
Fil: Mariño, Fernando Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Tecnologías del Hidrogeno y Energias Sostenibles. Universidad de Buenos Aires. Facultad de Ingeniería. Instituto de Tecnologías del Hidrogeno y Energias Sostenibles; Argentina
dc.journal.title
The Canadian Journal Of Chemical Engineering
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://onlinelibrary.wiley.com/doi/10.1002/cjce.24788
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1002/cjce.24788
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