Oxy-fuel and chemical-looping combustion for a low-carbon future.

This thesis is focused on investigating the potential of oxy-fuel and chemical- looping combustion (CLC) for carbon capture, and their integration with sorbent enhanced steam methane reforming (SE-SMR) for low-carbon hydrogen production. Oxy-fuel combustion converts a fuel within a mixture of O₂/CO₂ instead of air, while CLC converts a fuel by reduction of a metal oxide. In both cases, the resulting flue gas is free of N₂, and consist of only CO₂ and steam, and the steam can easily be condensed out. With the use of biomass as the fuel feedstock for the oxy-fuel combustion and CLC, negative CO₂ emissions can be achieved for power and heat generation. Oxy-fuel combustion is also a likely route to decarbonise the calcination of limestone, as used in the calcium looping and SE-SMR processes. SE-SMR combines the conventional steam methane reforming with calcium looping (CaL), which utilises CO₂ sorbents (e.g. CaO) to capture the CO₂ produced during the SMR process and shifts the equilibrium of the reforming and water-gas shift reactions in favour of more H₂ production according to Le Chatelier’s principle. Three main areas of work were conducted within this thesis, which includes 1) a detailed investigation into the effects of various parameters on the reaction kinetics of air and oxy-fuel combustion of woody biomass in a lab-scale fluidised- bed reactor; 2) applying machine learning in estimating the performance of oxygen carriers in chemical-looping processes; and 3) thermodynamic and techno-economic assessment of the integration of SE-SMR with oxy-fuel and chemical-looping combustion for low-carbon hydrogen production. Firstly, combustion rates of the biomass and its char were measured by a lab- scale fluidised-bed reactor. The shirking core model was used to simulate the char conversion during the experiments and under combustion mechanisms. Then, a novel approach that uses machine learning to efficiently screen the suitable oxygen carrier materials for CLC has been proposed. Lastly, the integration of oxy-fuel and CLC within the calciner of SE-SMR has been simulated in the Aspen Plus to understand their thermodynamic limitations and optimal operating conditions. Moreover, a detailed techno-economic analysis of the proposed configurations has been conducted to investigate their feasibility for a large-scare low-carbon hydrogen production. The obtained combustion kinetics and characteristics of air- and oxy-fuel combustion of biomass can provide useful information for retrofit and design of boilers. The framework of applying machine learning in oxygen carriers is expected to accelerate the finding and designing cost-effective oxygen carriers for large-scale CLC. The results of techno-economic analysis of the integration of oxy-fuel combustion and CLC with SE-SMR indicate that it is competitive with conventional steam methane reforming (SMR) with carbon capture and storage (CCS).