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Assessment of cubic equations of state: machine learning for rich carbon-dioxide systems

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dc.contributor.author Truc, George
dc.contributor.author Rahmanian, Nejat
dc.contributor.author Pishnamazi, Mahboubeh
dc.date.accessioned 2021-03-04T11:39:09Z
dc.date.available 2021-03-04T11:39:09Z
dc.date.issued 2021
dc.identifier.uri http://hdl.handle.net/10344/9846
dc.description peer-reviewed en_US
dc.description.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. en_US
dc.language.iso eng en_US
dc.publisher MDPI en_US
dc.relation.ispartofseries Sustainability;13, 2527
dc.subject equation of state en_US
dc.subject carbon capture systems en_US
dc.title Assessment of cubic equations of state: machine learning for rich carbon-dioxide systems en_US
dc.type info:eu-repo/semantics/article en_US
dc.type.supercollection all_ul_research en_US
dc.type.supercollection ul_published_reviewed en_US
dc.identifier.doi 10.3390/su13052527
dc.rights.accessrights info:eu-repo/semantics/openAccess en_US


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