Modeling viscosity of CO2 at high temperature and pressure conditions - Archive ouverte HAL Access content directly
Journal Articles Journal of Natural Gas Science and Engineering Year : 2020

Modeling viscosity of CO2 at high temperature and pressure conditions

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.
Fichier principal
Vignette du fichier
1-s2.0-S1875510020301256-main.pdf (1.98 Mo) Télécharger le fichier
Origin : Publisher files allowed on an open archive
Loading...

Dates and versions

hal-02534736 , version 1 (28-09-2020)

Identifiers

Cite

Menad Nait Amar, Mohammed Abdelfetah Ghriga, Hocine Ouaer, Mohamed El Amine Ben Seghier, Binh Thai Pham, et al.. Modeling viscosity of CO2 at high temperature and pressure conditions. Journal of Natural Gas Science and Engineering, 2020, 77, pp.103271. ⟨10.1016/j.jngse.2020.103271⟩. ⟨hal-02534736⟩
28 View
90 Download

Altmetric

Share

Gmail Facebook X LinkedIn More