Hydrogen solubility in furfural and furfuryl bio-alcohol: Comparison between the reliability of intelligent and thermodynamic models

Abstract

This study compares the reliability of intelligent and thermodynamic modeling of hydrogen (H2) solubility in two bio-derived compounds (furfuryl alcohol and furfural). The intelligent modeling phase conducts using seven different scenarios. The most accurate approach selects employing the ranking analysis over various statistical indices. The general regression neural network appears as the best intelligent model for the given purpose. This model presents the relative absolute deviation (RAD) of 0.6%, mean square error of 3.87 × 10−7, and regression coefficient of 0.99912 for predicting experimental measurements in the literature. The general regression neural network accuracy is far better than the perturbed-chain statistically associating fluid theory (PC-SAFT), Peng-Robinson, and Soave-Redlich-Kwong correlations. The most accurate thermodynamic approach, i.e., PC-SAFT, predicts H2 solubility in furfuryl alcohol and furfural with the RAD = 4.58% and 4.62%, while the general regression model has RAD = 0.79% and 0.5%. Indeed, the proposed model improves the prediction accuracy by more than three hundred percent.