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

摘要

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 in this regard. The ranking analysis employing seven well-known statistical criteria confirmed that the LS-SVR is the most accurate model for the given purpose. The developed LS-SVR is superior to the Peng-Robinson, Soave-Redlich-Kwong, and perturbed-chain statistical associating fluid theory thermodynamic-based correlations proposed in the literature. The LS-SVR model predicts hydrogen solubility in the considered biochemicals with the relative absolute deviation of 1.91, mean squared error of 6.4 x 10(-7), and regression coefficient of 0.99924. Both experimental and modeling observations approved that furan has the maximum tendency to absorb hydrogen molecules. A pure simulation analysis indicates that the maximum hydrogen solubility of 0.11 (mole fraction) can be absorbed by furan at pressure = 14.93 MPa and temperature = 402 K. (C) 2021 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.