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    Assessment of Cubic Equations of State: Machine Learning for Rich Carbon-Dioxide Systems

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    Publication date
    2021-02-26
    Author
    Truc, George
    Rahmanian, Nejat
    Pishnamazi, M.
    Keyword
    Equation of State
    EoS
    Carbon Capture Systems
    CCS
    Machine learning
    Fluid package selection
    Rights
    (c) 2021 The Authors. This is an Open Access article distributed under the Creative Commons CC-BY license (https://creativecommons.org/licenses/by/4.0/)
    Peer-Reviewed
    Yes
    Open Access status
    Gold
    
    Metadata
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    Abstract
    Carbon capture and storage (CCS) has attracted renewed interest in the re-evaluation of the equations of state (EoS) for the prediction of thermodynamic properties. This study also evaluates EoS for Peng–Robinson (PR) and Soave–Redlich–Kwong (SRK) and their capability to predict the thermodynamic properties of CO2-rich mixtures. The investigation was carried out using machine learning such as an artificial neural network (ANN) and a classified learner. A lower average absolute relative deviation (AARD) of 7.46% was obtained for the PR in comparison with SRK (AARD = 15.0%) for three components system of CO2 with N2 and CH4. Moreover, it was found to be 13.5% for PR and 19.50% for SRK in the five components’ (CO2 with N2, CH4, Ar, and O2) case. In addition, applying machine learning provided promise and valuable insight to deal with engineering problems. The implementation of machine learning in conjunction with EoS led to getting lower predictive AARD in contrast to EoS. An of AARD 2.81% was achieved for the three components and 12.2% for the respective five components mixture.
    URI
    http://hdl.handle.net/10454/18397
    Version
    Published version
    Citation
    Truc C, Rahmanian N and Pishnamazi (2021) Assessment of Cubic Equations of State: Machine Learning for Rich Carbon-Dioxide Systems. Sustainability. 13(5): 2527.
    Link to publisher’s version
    https://doi.org/10.3390/su13052527
    Type
    Article
    Collections
    Engineering and Informatics Publications

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