Machine-learning based prediction of hydrogen/methane mixture solubility in brine

Abstract

Abstract With regard to underground hydrogen storage projects, presuming that the hydrogen storage site has served as a repository for methane, the coexistence of a blend of methane and hydrogen is anticipated during the incipient stage of hydrogen storage. Therefore, the solubility of hydrogen/methane mixtures in brine becomes imperative. On the contrary, laboratory tasks of such measurements are hard because of its extreme corrosion ability and flammability, hence modeling methodologies are highly preferred. Therefore, in this study, we seek to create accurate data-driven intelligent models based upon laboratory data using hybrid models of adaptive neuro-fuzzy inference system (ANFIS) and least squares support vector machine (LSSVM) optimized with either particle swarm optimization (PSO), genetic algorithm (GA) and coupled simulated annealing (CSA) to predict hydrogen/methane mixture solubility in brine as a function of pressure, temperature, hydrogen mole fraction in hydrogen/methane mixture and brine salt concentration. The results indicate that almost all the gathered experimental data are technically suitable for the model development. The sensitivity study shows that pressure and hydrogen mole fraction in the mixture are strongly related with the solubility data with direct and indirect effects, respectively. The analyses of evaluation indexes and graphical methods indicates that the developed LSSVM-GA and LSSVM-CSA models are the most accurate as they exhibit the lowest AARE% and MSE values and the highest R-squared values. These findings show that machine learning methods could be a useful tool for predicting hydrogen solubility in brine encountered in underground hydrogen storage projects, aiding in the advancement of intelligent, affordable, and secure hydrogen storage technologies.

Keywords

• Machine learning
• Data-driven models
• Relevancy factor
• Outlier detection