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dc.contributor.author이문용ko
dc.contributor.authorAlireza Bahadori[Alireza Bahadori]ko
dc.contributor.authorMohammad Mesbah[Mohammad Mesbah]ko
dc.contributor.authorEbrahim Soroushb[Ebrahim Soroushb]ko
dc.contributor.authorVahid Azari[Vahid Azari]ko
dc.contributor.authorSamaneh Habibnia[Samaneh Habibnia]ko
dc.date.accessioned2015-12-17T05:04:27Z-
dc.date.available2015-12-17T05:04:27Z-
dc.date.created2015-11-13-
dc.date.issued201502-
dc.identifier.citationJOURNAL OF SUPERCRITICAL FLUIDS, v.97, pp.256 - 267-
dc.identifier.issn0896-8446-
dc.identifier.urihttp://hdl.handle.net/YU.REPOSITORY/33485-
dc.identifier.urihttp://dx.doi.org/10.1016/j.supflu.2014.12.011-
dc.description.abstractMany supercritical processes, like monomer separation depends crucially on VLE data. The need of simple, robust and general method, which can overcome deficiencies of EOSs, especially in critical regions, is obvious. In this study, a mathematical algorithm based on Least-Squares Support Vector Machine (LSSVM) has been developed for simulating 425 VLE data of seven CO2/hydrocarbon binary mixtures in supercritical or near critical conditions. The target value, bubble point/dew point pressure, is considered as a function of reduced temperature, hydrocarbon mole fraction and the hydrocarbons acentric factor and critical pressure. The proposed LSSVM model with its magnificent R-2 of 0.9932 and AARD% of 3.61 is proving able to predict VLE data of CO2/hydrocarbon binary mixture in a very precise manner. In addition, comparison of LSSVM with EOSs indicates its supremacy over conventional methods. A sensitivity analysis, with three different methods, was performed on the independent variables in an effort to determine the relative importance of each one. At the end with the aid of Leverage statistical algorithm, the statistical validity of the model was guaranteed and proved that the majority of the data points are in the applicability domain of the proposed LSSVM. (C) 2014 Elsevier B.V. All rights reserved.-
dc.language영어-
dc.publisherELSEVIER SCIENCE BV-
dc.subjectARTIFICIAL NEURAL-NETWORKS-
dc.subjectPHASE-EQUILIBRIUM-
dc.subjectBINARY-SYSTEMS-
dc.subjectHIGH-PRESSURE-
dc.subjectMIXTURES-
dc.subjectBEHAVIOR-
dc.subjectTERNARY-
dc.titleVapor liquid equilibrium prediction of carbon dioxide and hydrocarbon systems using LSSVM algorithm-
dc.typeArticle-
dc.identifier.wosid000348952800030-
dc.identifier.scopusid2-s2.0-84921033887-
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