Hydrogen solubility in aromatic/cyclic compounds: Prediction by different machine learning techniques

A systematic procedure based on adaptive neuro-fuzzy inference systems (ANFIS), artificial neural networks, and least-squares support vector machines develop to estimate hydrogen solubility in aromatic and cyclic compounds. The key features of these models are determined through a massive trial-and-error process. The proposed intelligent models estimate hydrogen solubility as a function of critical properties and acentric factor of aromatic/cyclic compounds, temperature, and pressure. The ranking analysis based on seven statistical criteria indicates the priority of the ANFIS method over other paradigms. The proposed ANFIS model estimates 278 experimental hydrogen solubility in eleven aromatic/cyclic compounds by the absolute average relative deviation of 7.88%, the mean absolute error of 0.0023, the relative absolute error of 5.05%, mean squared error of 2.74 x 10(-5), root mean squared error of 0.0052, and regression coefficient of 0.99664. Moreover, the relevancy analysis justifies that the pressure is the strongest influential variable for the hydrogen solubility in aromatic/cyclic compounds. (C) 2021 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.