White-box machine-learning models for accurate interfacial tension prediction in hydrogen-brine mixtures

The severity of climate change and global warming necessitates the need for a transition from traditional hydrocarbon-based energy sources to renewable energy sources. One intrinsic challenge with renewable energy sources is their intermittent nature, which can be addressed by transforming excess energy into hydrogen and storing it safely for future use. To securely store hydrogen underground, a comprehensive knowledge of the interactions between hydrogen and residing fluids is required. Interfacial tension is an important variable influenced by cushion gases such as CO2 and CH4. This research developed explicit correlations for approximating the interfacial tension of a hydrogen-brine mixture using two advanced machine-learning techniques: gene expression programming and the group method of data handling. The interfacial tension of a hydrogen-brine mixture was considered to be heavily influenced by temperature, pressure, water salinity, and the average critical temperature of the gas mixture. The results indicated a higher performance of the group method of data handling-based correlation, showing an average absolute relative error of 4.53%. Subsequently, Pearson, Spearman, and Kendall methods were used to assess the influence of individual input variables on the outputs of the correlations. Analysis showed that the temperature and the average critical temperature of the gas mixture had considerable inverse impacts on the estimated interfacial tension values. Finally, the reliability of the gathered databank and the scope of application for the proposed correlations were verified using the leverage approach by illustrating 97.6% of the gathered data within the valid range of the Williams plot. Explicit correlations for approximating the interfacial tension of a hydrogen-brine mixture are developed using two advanced machine-learning techniques: gene expression programming and the group method of data handling. Graphical Abstract