CSA-LSSVM model for the estimation of solubility of n-alkane in supercritical CO2

Abstract

ABSTRACTPoor solubility of substantial hydrocarbons in CO2 has constrained the use of CO2-EOR (enhanced oil recovery) in the modern oil recovery industry to some extent. Subsequently, it is significant to research the solubility regularity of various hydrocarbons in supercritical carbon dioxide (scCO2) in the first place. CO2 injection as one of the popular methodologies in light of financially and environmentally friendly has wide applications in EOR. In this paper, our objective is to estimate the solubility of n-alkanes in scCO2. This study highlights the application of a model based on least square support vector machine for estimation of solubility of n-alkanes in scCO2. The tuning parameters of the developed model were determined by an optimization algorithm, namely coupled simulated annealing. A set of 184 data points of solubility was used to execute the new model. To assess the accuracy and effectiveness of the developed model for prediction of experimental data, statistical and graphical techniques were used. Moreover, the outcomes were compared with the results of literature correlations to predict the solubility of alkanes. Results demonstrate that the model is precise and viable for prediction of solubility data. The resulted values of R2, root-mean-square error, SD, and % average absolute relative deviation for total data points are 0.99204, 0.12862, 0.6437, and 0.7753 for overall data, respectively.