Prediction of hydrogen solubility in aqueous solution using modified mixed effects random forest based on particle swarm optimization for underground hydrogen storage

• Published in: International journal of hydrogen energy Vol. 87; pp. 373 - 388
• Main Authors: Mwakipunda, Grant Charles, Komba, Norga Alloyce, Kouassi, Allou Koffi Franck, Ayimadu, Edwin Twum, Mgimba, Melckzedeck Michael, Ngata, Mbega Ramadhani, Yu, Long
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 18.10.2024
• Subjects: Geological formations; Hydrogen solubility; Machine learning; Underground hydrogen storage; Geological formations; Underground hydrogen storage; Hydrogen solubility; Machine learning

This paper aims to enhance the prediction accuracy of hydrogen solubility in aqueous solution, which is crucial for safe and efficient underground hydrogen storage (UHS). The study developed a new hybrid machine learning (ML) algorithm, particle swarm optimization-mixed effects random forest (PSO-ME...