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

Author

Listed:
  • George Truc

    (Chemical Engineering Department, Faculty of Engineering & Informatics, University of Bradford, Bradford BD7 1DP, UK)

  • Nejat Rahmanian

    (Chemical Engineering Department, Faculty of Engineering & Informatics, University of Bradford, Bradford BD7 1DP, UK)

  • Mahboubeh Pishnamazi

    (Department of Chemical Sciences, Bernal Institute, University of Limerick, Limerick V94 T9PX, Ireland)

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 CO 2 -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 CO 2 with N 2 and CH 4 . Moreover, it was found to be 13.5% for PR and 19.50% for SRK in the five components’ (CO 2 with N 2 , CH 4 , Ar, and O 2 ) 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.

Suggested Citation

  • George Truc & Nejat Rahmanian & Mahboubeh Pishnamazi, 2021. "Assessment of Cubic Equations of State: Machine Learning for Rich Carbon-Dioxide Systems," Sustainability, MDPI, vol. 13(5), pages 1-18, February.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:5:p:2527-:d:506355
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    References listed on IDEAS

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    1. Vassilis Gaganis & Dirar Homouz & Maher Maalouf & Naji Khoury & Kyriaki Polychronopoulou, 2019. "An Efficient Method to Predict Compressibility Factor of Natural Gas Streams," Energies, MDPI, vol. 12(13), pages 1-20, July.
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