Modeling viscosity of CO2 at high temperature and pressure conditions
Created by W.Langdon from
gp-bibliography.bib Revision:1.7975
- @Article{Amar:2020:jNGSE,
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author = "Menad Nait Amar and Mohammed Abdelfetah Ghriga and
Hocine Ouaer and Mohamed El Amine Ben Seghier and
Binh Thai Pham and Pal Ostebo Andersen",
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title = "Modeling viscosity of {CO2} at high temperature and
pressure conditions",
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journal = "Journal of Natural Gas Science and Engineering",
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year = "2020",
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volume = "77",
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pages = "103271",
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month = may,
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keywords = "genetic algorithms, genetic programming, gene
expression programming, ANN, carbon dioxide,
correlations, data-driven, GEP, MLP, viscosity,
chemical sciences/polymers, material chemistry,
physical chemistry",
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ISSN = "1875-5100",
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publisher = "HAL CCSD; Elsevier",
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URL = "https://hal.archives-ouvertes.fr/hal-02534736",
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DOI = "doi:10.1016/j.jngse.2020.103271",
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abstract = "The present work aims at applying Machine Learning
approaches to predict CO2 viscosity at different
thermodynamical conditions. Various data-driven
techniques including multilayer perceptron (MLP), gene
expression programming (GEP) and group method of data
handling (GMDH) were implemented using 1124
experimental points covering temperature from 220 to
673 K and pressure from 0.1 to 7960 MPa. Viscosity was
modelled as function of temperature and density
measured at the stated conditions. Four
backpropagation-based techniques were considered in the
MLP training phase; Levenberg-Marquardt (LM), bayesian
regularization (BR), scaled conjugate gradient (SCG)
and resilient backpropagation (RB). MLP-LM was the most
fit of the proposed models with an overall root mean
square error (RMSE) of 0.0012 mPa s and coefficient of
determination (R2) of 0.9999. A comparison showed that
our MLP-LM model outperformed the best preexisting
Machine Learning CO2 viscosity models, and that our GEP
correlation was superior to preexisting explicit
correlations.",
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annote = "Institut des sciences analytiques et de physico-chimie
pour l'environnement et les materiaux (IPREM) ;
Universite de Pau et des Pays de l'Adour (UPPA)-Centre
National de la Recherche Scientifique (CNRS); Institute
of Research and Development; Duy-Tan University;
University of Stavanger ; University of Stavanger",
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bibsource = "OAI-PMH server at api.archives-ouvertes.fr",
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description = "International audience",
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identifier = "hal-02534736; DOI: 10.1016/j.jngse.2020.103271",
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language = "en",
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oai = "oai:HAL:hal-02534736v1",
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relation = "info:eu-repo/semantics/altIdentifier/doi/10.1016/j.jngse.2020.103271",
- }
Genetic Programming entries for
Menad Nait Amar
Mohammed Abdelfetah Ghriga
Hocine Ouaer
Mohamed El Amine Ben Seghier
Binh Thai Pham
Pal Ostebo Andersen
Citations