Predicting the hydrogen uptake ability of a wide range of zeolites utilizing supervised machine learning methods

Abstract

This study predicts the hydrogen uptake ability of 28 zeolites using artificial neural networks. The topology-related features of four artificial neural networks and their hyper-parameters determine utilizing 349 experimental data reported for zeolites with different surface areas at wide ranges of pressure and −196 °C. Ranking analysis over various statistical criteria confirms that the most accurate model is the cascade feed-forward neural networks with twelve hidden nodes and logarithm and tangent sigmoid activation functions. This model predicts the experimental data with the absolute average relative deviation of 7.24%, mean absolute error of 0.041, root mean squared error of 0.058, and regression factor of 0.99429. The leverage method approves that 94% of the databank is reliable. Furthermore, the relevancy analysis shows a strong direct relationship between the hydrogen uptake ability of zeolite (HUAZ) and pressure and surface area. This study is the first to develop a model for predicting the HUAZ.