Sorption-enhanced steam methane reforming for combined CO2 capture and hydrogen production: a state-of-the-art review

The European Commission have just stated that hydrogen would play a major role in the economic recovery of post-COVID-19 EU countries. Hydrogen is recognised as one of the key players in a fossil fuel-free world in decades to come. However, commercially practiced pathways to hydrogen production todays, are associated with a considerable amount of carbon emissions. The Paris Climate Change Agreement has set out plans for an international commitment to reduce carbon emissions within the forthcoming decades. A sustainable hydrogen future would only be achievable if hydrogen production is “designed” to capture such emissions. Today, nearly 98% of global hydrogen production relies on the utilisation of fossil fuels. Among these, steam methane reforming (SMR) boasts the biggest share of nearly 50% of the global generation. SMR processes correspond to a significant amount of carbon emissions at various points throughout the process. Despite the dark side of the SMR processes, they are projected to play a major role in hydrogen production by the first half of this century. This that a sustainable, yet clean short/medium-term hydrogen production is only possible by devising a plan to efficiently capture this co-produced carbon as stated in the latest International Energy Agency (IEA) reports. Here, we have carried out an in-depth technical review of the processes employed in sorption-enhanced steam methane reforming (SE-SMR), an emerging technology in low-carbon SMR, for combined carbon capture and hydrogen production. This paper aims to provide an in-depth review on two key challenging elements of SE-SMR i.e. the advancements in catalysts/adsorbents preparation, and current approaches in process synthesis and optimisation including the employment of artificial intelligence in SE-SMR processes. To the best of the authors’ knowledge, there is a clear gap in the literature where the above areas have been scrutinised in a systematic and coherent fashion. The gap is even more pronounced in the application of AI in SE-SMR technologies. As a result, this work aims to fill this gap within the scientific literature.