Comparative Analysis of Ensemble Learning, Evolutionary Algorithm, and Molecular Dynamics Simulation for Enhanced Aqueous H₂/Cushion Gases Interfacial Tension Prediction: Implications on Underground H₂ Storage

There is a strong push for decarbonization in the Global North and South to combat climate change and avert its dire consequences including rising sea levels, severe weather events, biodiversity loss, and threats to food and water security. Hydrogen (H₂) emerges as a sustainable energy carrier in this regard. Underground hydrogen storage (UHS) presents significant potential but requires a thorough understanding of H₂ behavior in porous media. One of the crucial parameters is interfacial tension (IFT) which influences capillary entry pressure, H₂ column height, and storage capacity. Accurate IFT measurement is therefore imperative but current laboratory methods are resource-intensive. To address the limitation, the current study employed computational techniques to estimate the IFT of aqueous H₂/cushion gases systems using minimum resources. The novelty of this investigation lies in the comparative analysis of diverse techniques including ensemble learning, evolutionary algorithm, and molecular dynamics simulation. Specifically, this study employed Random Forest and Adaptive Boosting (AdaBoost) to predict the IFT of aqueous H₂/cushion gases systems. We utilized a comprehensive database of 2400 experimental data points encompassing pressure (0.50-45.20 MPa), temperature (298-449 K), brine salinity (0-3.42 mol/kg), and gas mole fractions (0-100 mol%). Numerical statistical error measures and multiple data visualizations were used to evaluate the performance of the developed models. The best smart model was also compared to an evolutionary algorithm and molecular dynamics simulation to ascertain robustness and generalizability. Subsequently, sensitivity analysis and feature importance were performed to comprehend the impact of the individual input parameters on the IFT. Finally, the implication of model-predicted IFT on UHS was extensively investigated. The developed paradigms demonstrated remarkable predictive prowess, yielding a coefficient of determination>0.96, average absolute deviation (AAD)<1.50 mN/m, and root mean square error (RMSE)<2.1 mN/m, with AdaBoost emerging as the upper-echelon paradigm. Additionally, AdaBoost exhibited predictive dominance over genetic programming and molecular-dynamics-based correlations developed by other scholars. Sensitivity analysis identified carbon dioxide mole fraction as the main factor influencing IFT. Analysis also indicate that higher concentrations of the cushion gas, particularly CO₂ significantly reduces IFT and this could compromise structural hydrogen trapping and lead to H₂ loss. AdaBoost also provided precise results for capillary entry pressure (11.9-12.1 MPa), H₂ storage height (1177-1217 m), and storage capacity (690-2226 kg/m²) of a Saudi basaltic formation comparable to experimental findings (2-3% deviation), affirming its potential for practical applications. AdaBoost can also be utilized in conjunction with Monte Carlo simulation for a swift and accurate assessment of the uncertainty associated with the influencing factors of UHS. The paradigms offer a promising approach to estimating IFT using minimal resources. Additionally, the findings facilitate UHS optimization, reduce H₂ loss, and improve storage capacity.