The rise of the machines: A state-of-the-art technical review on process modelling and machine learning within hydrogen production with carbon capture

Abstract

Data availability: No data was used for the research described in the article.Copyright © 2023 The Authors. This study aims to present a compendious yet technical scrutiny of the current trends in process modelling as well as the implementation of machine learning within combined hydrogen production and carbon capture (i.e. blue hydrogen). The paper is intended to accurately portray the role that machine learning is anticipated to play within research and development in blue hydrogen production in the forthcoming years. This covers the implementation of machine learning at both material and process development levels. The paper provides a concise overview of the current trends in blue hydrogen production, as well as an intro to machine learning and process modelling within the same context. We have reinforced our paper by first summarising a brief description of the key “tools” used in machine learning and process modelling, before painstakingly examining the implementation of these techniques in blue hydrogen production and the less-discovered merits and de-merits. Ultimately, the paper depicts a clear picture of the advancements in machine learning and the major role it is expected to play in accelerating research and development in blue hydrogen production on both material and process development fronts. The paper strives to shed some light on the key advantages that machine learning has to offer in blue hydrogen for future research work.UK Engineering and Physical Sciences Research Council (EPSRC) via the grant “Multiphysics and multiscale modelling for safe and feasible CO2 capture and storage - EP/T033940/1”; EPSRC Doctoral Training Partnerships (DTP) award, EP/T518116/1 (project reference: 2688399)