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Please use this identifier to cite or link to this item: http://dlibrary.univ-boumerdes.dz:8080/handle/123456789/6105

Titre: Modeling viscosity of CO 2 at high temperature and pressure conditions
Auteur(s): Nait Amar, Menad
Ghriga, Mohammed Abdelfetah
Ouaer, Hocine
Ben Seghier, Mohamed El Amine
Thai Pham, Binh
Mots-clés: CO2
Viscosity
Data-driven
Correlations
MLP
GEP
Issue Date: 2020
Editeur: Elsevier
Collection/Numéro: Journal of Natural Gas Science and Engineering Volume 77, May 2020, 103271;
Résumé: 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.
URI: https://www.sciencedirect.com/science/article/pii/S1875510020301256#!
http://dlibrary.univ-boumerdes.dz:8080/handle/123456789/6105
ISSN: 1875-5100
Appears in Collections:Publications Internationales

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