Application of machine learning in hydrogen production via the process of sorbent enhanced steam methane reforming.

This thesis is focused on the exploration of the use of machine learning and computational methods for modelling process conditions and for materials screening within the process of sorbent enhanced steam methane reforming (SE- SMR) for carbon-abated hydrogen production. Hydrogen is a clean, abundant and versatile energy carrier that can be used for a wide range of applications. However, the production of hydrogen is still largely dependent on fossil fuels, which presents a significant challenge for achieving a truly sustainable energy system. The purpose of this study is to address this challenge by exploring novel approaches to hydrogen production, namely using machine learning, thermodynamic simulations, theoretical modelling, and the proposal of new methodologies and materials for low-carbon hydrogen production. Three main areas of work were conducted within this thesis, which include 1) two surrogate models have been developed and used to predict and estimate variables that would otherwise be difficult direct measured.; 2) applying machine learning, namely quantitative structure–property relationship analysis (QSPR) has been employed in the exploration of combined sorbent catalyst material (CSCM) for SE-SMR; and 3) applying machine learning to screen suitable metal organic frameworks (MOFs) for the storage of the produced blue hydrogen. Firstly, a surrogate model, was developed which was done by firstly simulating the model in Aspen Plus, applying a sensitivity analysis to gather a large dataset, then applying two multiple linear regression model, to observe the accuracy of predicting the gas concentration outputs. Two models were successfully developed with both models were accurate with high R² values, all above 98%. Secondly, the novel approach of QSPR with inductive transfer learning and datamining, was applied to develop two large databases of sorbent and catalyst properties, respectively. Then the developed machine learning models from these databases were applied, to predict the optimal conditions and precursor materials for the highest performing CSCM, in terms of last cycle capacity and methane conversion. Lastly, a similar approach was applied for the screening of MOFs for the storage of hydrogen by using multiple linear regression, simple geometric descriptors, and patterns in data to identify a better performing MOF than the currently reported experimental MOFs in literature.