Machine learning methods help accurate estimation of the hydrogen solubility in biomaterials

• Published in: International journal of hydrogen energy Vol. 47; no. 6; pp. 3611 - 3624
• Main Authors: Cao, Yan, Karimi, Mehdi, Kamrani, Elham, Nourani, Pejman, Mohammadi Manesh, Afshin, Momenieskandari, Homa, E. Anqi, Ali
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 19.01.2022
• Subjects: Biomaterials; Data-driven approach; Equations of state; Gene expression programming; Hydrogen solubility; Equations of state; Hydrogen solubility; Data-driven approach; Biomaterials; Gene expression programming

This study investigates the hydrogen solubility in five industrially relevant biochemicals, i.e., eugenol, furfural, furan, allyl alcohol, and furfuryl alcohol. Gene expression programming, three different artificial neural networks, and least-squares support vector regression (LS-SVR) are checked i...