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dc.contributor.authorTruc, George
dc.contributor.authorRahmanian, Nejat
dc.contributor.authorPishnamazi, M.
dc.date.accessioned2021-03-12T16:59:08Z
dc.date.accessioned2021-03-18T08:04:26Z
dc.date.available2021-03-12T16:59:08Z
dc.date.available2021-03-18T08:04:26Z
dc.date.issued2021-02-26
dc.identifier.citationTruc C, Rahmanian N and Pishnamazi (2021) Assessment of Cubic Equations of State: Machine Learning for Rich Carbon-Dioxide Systems. Sustainability. 13(5): 2527.en_US
dc.identifier.urihttp://hdl.handle.net/10454/18397
dc.descriptionYesen_US
dc.description.abstractCarbon 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.isoenen_US
dc.relation.isreferencedbyhttps://doi.org/10.3390/su13052527en_US
dc.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/)en_US
dc.subjectEquation of Stateen_US
dc.subjectEoSen_US
dc.subjectCarbon Capture Systemsen_US
dc.subjectCCSen_US
dc.subjectMachine learningen_US
dc.subjectFluid package selectionen_US
dc.titleAssessment of Cubic Equations of State: Machine Learning for Rich Carbon-Dioxide Systemsen_US
dc.status.refereedYesen_US
dc.date.Accepted2021-02-20
dc.typeArticleen_US
dc.type.versionPublished versionen_US
dc.date.updated2021-03-12T16:59:14Z
refterms.dateFOA2021-03-18T08:38:37Z
dc.openaccess.statusGolden_US


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