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.
Highlights
The increased global awareness of the effect of CO2 on the climate has renewed the interest in carbon capture and storage (CCS) technologies for the reduction of CO2 emissions relative to historic emissions; these innovative technologies aim to achieve a lower relative rise in global mean temperature (GMT) long-term [1]
average absolute relative deviation (AARD) (%) of 22.27% and 5.95% were obtained for temperatures between 6.71 K and
Out of the evaluated equations of state (EoS) that is used in Aspen HSYS, Peng–Robinson more accurately represents CO2 mixtures
Summary
The increased global awareness of the effect of CO2 on the climate has renewed the interest in carbon capture and storage (CCS) technologies for the reduction of CO2 emissions relative to historic emissions; these innovative technologies aim to achieve a lower relative rise in global mean temperature (GMT) long-term [1]. There are a vast number of equations of state (EoS) used for the prediction of thermodynamic properties of gas mixtures. These EoS are used in a fluid package within software such as Aspen HYSYS to predict fundamental properties such as density, heat capacity, viscosity, etc. It is highly important to combine the existing EoS with other methods to improve the reliability and accuracy of predicting thermodynamic properties. It is because using EoS in the design calculations in software packages such as AspenONE, without access to autonomous property prediction programes such as NIST Thermo Data
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