Vapor liquid equilibrium prediction of carbon dioxide and hydrocarbon systems using LSSVM algorithm

Title
Vapor liquid equilibrium prediction of carbon dioxide and hydrocarbon systems using LSSVM algorithm
Author(s)
이문용Alireza Bahadori[Alireza Bahadori]Mohammad Mesbah[Mohammad Mesbah]Ebrahim Soroushb[Ebrahim Soroushb]Vahid Azari[Vahid Azari]Samaneh Habibnia[Samaneh Habibnia]
Keywords
ARTIFICIAL NEURAL-NETWORKS; PHASE-EQUILIBRIUM; BINARY-SYSTEMS; HIGH-PRESSURE; MIXTURES; BEHAVIOR; TERNARY
Issue Date
201502
Publisher
ELSEVIER SCIENCE BV
Citation
JOURNAL OF SUPERCRITICAL FLUIDS, v.97, pp.256 - 267
Abstract
Many 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.
URI
http://hdl.handle.net/YU.REPOSITORY/33485http://dx.doi.org/10.1016/j.supflu.2014.12.011
ISSN
0896-8446
Appears in Collections:
공과대학 > 화학공학부 > Articles
Files in This Item:
There are no files associated with this item.
Export
RIS (EndNote)
XLS (Excel)
XML


qrcode

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

BROWSE