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Mr. George Truc
IChemE

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Journal article
Published: 26 February 2021 in Sustainability
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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.

ACS Style

George Truc; Nejat Rahmanian; Mahboubeh Pishnamazi. Assessment of Cubic Equations of State: Machine Learning for Rich Carbon-Dioxide Systems. Sustainability 2021, 13, 2527 .

AMA Style

George Truc, Nejat Rahmanian, Mahboubeh Pishnamazi. Assessment of Cubic Equations of State: Machine Learning for Rich Carbon-Dioxide Systems. Sustainability. 2021; 13 (5):2527.

Chicago/Turabian Style

George Truc; Nejat Rahmanian; Mahboubeh Pishnamazi. 2021. "Assessment of Cubic Equations of State: Machine Learning for Rich Carbon-Dioxide Systems." Sustainability 13, no. 5: 2527.