Modeling viscosity of CO2 at high temperature and pressure conditions

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@Article{Amar:2020:jNGSE,

• 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",
• title = "Modeling viscosity of {CO2} at high temperature and pressure conditions",
• journal = "Journal of Natural Gas Science and Engineering",
• year = "2020",
• volume = "77",
• pages = "103271",
• month = may,
• keywords = "genetic algorithms, genetic programming, gene expression programming, ANN, carbon dioxide, correlations, data-driven, GEP, MLP, viscosity, chemical sciences/polymers, material chemistry, physical chemistry",
• ISSN = "1875-5100",
• publisher = "HAL CCSD; Elsevier",
• URL = "https://hal.archives-ouvertes.fr/hal-02534736",
• DOI = "doi:10.1016/j.jngse.2020.103271",
• 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.",
• 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",
• bibsource = "OAI-PMH server at api.archives-ouvertes.fr",
• description = "International audience",
• identifier = "hal-02534736; DOI: 10.1016/j.jngse.2020.103271",
• language = "en",
• oai = "oai:HAL:hal-02534736v1",
• 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