Towards improved genetic programming based-correlations for predicting the interfacial tension of the systems pure/impure CO2-brine

• Published in: Journal of the Taiwan Institute of Chemical Engineers 2021-10, Vol.127, p.186-196
• Main Author: Nait Amar, Menad
• Format: Article
• Language: English
• Subjects: brine; carbon capture and sequestration; CO2; genetic programming (GP); interfacial tension (IFT); saline aquifers
• ISSN: 1876-1070; 1876-1089
• DOI: 10.1016/j.jtice.2021.08.010

•The interfacial tension of the systems pure/impure CO2-brine was modeled using GP.•2346 authenticated points were utilized for establishing the correlations.•The implemented correlations very satisfactory prediction performance. Accurate knowledge of the interfacial tension (IFT) of the systems pure/impure CO2-brine has significant importance for satisfying the dichotomy of maximizing CO2 injections and minimizing leakage risks during injection of CO2 in deep saline aquifers for carbon capture and sequestration (CCS). In this study, new explicit correlations were implemented using the genetic programming (GP) approach for estimating the IFT of the systems pure/impure CO2-brine under widespread operational conditions. In this regard, 2346 authenticated IFT measurements for these systems were utilized by considering pressure, temperature, monovalent cation molality, bivalent cation molality, and the mole fractions of CH4 and N2 as the correlations’ inputs. The obtained results showed that the outcomes of GP-based correlations were in high agreement with the real IFT measurements. Moreover, GP-based correlations achieved overall coefficient of determination (R2) and root mean square error (RMSE) values of 0.9519 and 3.30, respectively. Besides, the GP-based correlations outperformed the best existing explicit correlations for estimating the IFT of the systems pure/impure CO2-brine. Lastly, the performed trend analyses revealed that these newly implemented correlations preserved correctly the physics sense with respect to changes in the independent variables. [Display omitted]