Predicting the Hydrogen Storage Potential of Ionic Liquids Using the Data Analytics Techniques

Abstract

AbstractIn recent years, hydrogen has been an attractive substitute as an energy carrier to fossil fuels, though it is difficult to store by conventional means. Ionic Liquids (ILs) are low-melting salts with varying properties of interest. Experimental investigations into the utilization of ILs as hydrogen storage mediums are still ongoing. This study aimed to predict the solubility of hydrogen in ILs using the data analytics method, whereby the correlations between the ILs’ requisite hydrogen properties and hydrogen solubility were developed and validated. The methodology involves comparing the experimental data from the literature and the simulated data from COSMO-RS software, where predictive correlations were developed using analytical software such as Python. The predictive model can be used to predict the hydrogen solubility of ILs based on the input inherent thermophysical properties of the IL before a particular IL is synthesized and tested in an actual laboratory setting.