Modeling interfacial tension of the hydrogen-brine system using robust machine learning techniques: Implication for underground hydrogen storage

Abstract

During the last years, there has been a surge of interest in cleaner ways for producing energy in order to successfully handle the climate issues caused by the consumption of fossil fuels. The production of hydrogen (H2) is among the techniques which have grown up as attractive strategies towards energy transition. In this context, underground hydrogen storage (UHS) in saline aquifers has turned into one of the greatest challenges in the context of conserving energy for later use. The interfacial tension (IFT) of the H2-brine system is a paramount parameter which affects greatly the successful design and implementation of UHS. In this study, robust machine learning (ML) techniques, viz., genetic programming (GP), gradient boosting regressor (GBR), and multilayer perceptron (MLP) optimized with Levenberg-Marquardt (LMA) and Adaptive Moment Estimation (Adam) algorithms were implemented for establishing accurate paradigms to predict the IFT of the H2-brine system. The obtained results exhibited that the proposed models and correlation provide excellent estimations of the IFT. In addition, it was deduced that MLP-LMA outperforms the other models and the existing correlation in the literature. MLP-LMA yielded R2 and AAPRE values of 0.9997 and 0.1907%, respectively. Lastly, the trend analysis demonstrated the physical coherence and tendency of the predictions of MLP-LMA.