CO₂–N₂ Mixture Viscosity Modelling Using Machine Learning: Towards Sustainable Carbon Capture and Energy Efficiency

Accurate determination of the viscosity of carbon dioxide (CO₂) mixed with nitrogen (N₂) is vital for enhanced oil recovery (EOR) and carbon capture, utilisation and storage (CCUS/CCS). The determination of this important thermophysical property is usually through costly and time-consuming experiments, which is not ideal for field recovery planning and rapid decision-making. On the other hand, the conventional modelling relies largely on equations of state (EoS) and empirical correlations, which can be inaccurate for CO₂–N₂ viscosity, particularly near supercritical conditions due to simplifying assumptions and limited transferability. Consequently, machine-learning (ML) methods have gained popularity for fast and accurate prediction. Hence, in this study, ~3036 literature data points spanning pressures of 0.00127–160.99 MPa and temperature of 66.55–575.15 K were collected, cleaned and pre-processed. Then, using pre-processed data, several ML models, including gradient boosting (GB), extreme gradient boosting (XGBoost), LightGBM, CatBoost, random forest, three multilayer perceptron artificial neural networks (MLP-ANNs), a stacking ensemble and a group method of data handling (GMDH) were developed. The developed models were benchmarked to predict CO₂–N₂ viscosity as a function of temperature, pressure and the mole fractions of CO₂–N₂ in the mixture. The analysis of the results indicate that the GB achieved the best performance with a correlation coefficient (R²) of 0.9933 ± 0.0011, root mean square error (RMSE) of 4.83 ± 0.39 μPa·s and mean absolute error (MAE) of 2.34 ± 0.10 μPa·s (mean ± 95% CI) for the test dataset, outperforming all other ML models and the utilised literature correlations. In addition, based on GMDH, two practical explicit equations within temperature ranges of T < 300 K and T > 300 K that predict the experimental viscosity with high accuracy were proposed. The sensitivity analysis also shows that the pressure has the highest positive impact, while temperature exhibited a comparably strong negative effect on viscosity.