Toward accurate density and interfacial tension modeling for carbon dioxide/water mixtures

Abstract

Phase behavior of carbon dioxide/water binary mixtures plays an important role in various CO2-based industry processes. This work aims to screen a thermodynamic model out of a number of promising candidate models to capture the vapor–liquid equilibria, liquid–liquid equilibria, and phase densities of CO2/H2O mixtures. A comprehensive analysis reveals that Peng–Robinson equation of state (PR EOS) (Peng and Robinson 1976), Twu α function (Twu et al. 1991), Huron–Vidal mixing rule (Huron and Vidal 1979), and Abudour et al. (2013) volume translation model (Abudour et al. 2013) is the best model among the ones examined; it yields average absolute percentage errors of 5.49% and 2.90% in reproducing the experimental phase composition data and density data collected in the literature. After achieving the reliable modeling of phase compositions and densities, a new IFT correlation based on the aforementioned PR EOS model is proposed through a nonlinear regression of the measured IFT data collected from the literature over 278.15–477.59 K and 1.00–1200.96 bar. Although the newly proposed IFT correlation only slightly improves the prediction accuracy yielded by the refitted Chen and Yang (2019)’s correlation (Chen and Yang 2019), the proposed correlation avoids the inconsistent predictions present in Chen and Yang (2019)’s correlation and yields smooth IFT predictions.

Machine learning approaches for estimating interfacial tension between oil/gas and oil/water systems: a performance analysis

“…They also proposed a new IFT correlation based on the PR EOS model, which improved consistency. The study showed the effectiveness of their models for CO 2 /H 2 O mixtures 30 . In their 2021 study, Roy Setiawan and co-researchers assessed various machine learning algorithms to predict the surface tension of binary mixtures containing ionic liquids.…”