Abstract

Achieving net-zero greenhouse gas emissions by mid-century, and sustaining climate neutrality beyond 2050, requires a dual strategy involving both aggressive point-source mitigation and active atmospheric CO₂ removal. While Integrated Carbon Capture and Utilisation (ICCU) using Dual-Function Materials (DFMs) offers a promising route for process intensification, its industrial translation has been hindered by a lack of multiscale engineering frameworks that bridge material discovery with process-level performance. This thesis delivers a unified and systematic computational framework that links system-level techno-economic assessment, reactor-scale kinetic modelling, and data-efficient operational optimisation to quantify the commercial potential of DFM-based systems.

At the system level, a superstructure optimisation framework is developed to benchmark DFM-based direct air capture (DAC) against decoupled configurations. The results demonstrate that integrated DFM systems are economically comparable to state-of-the-art temperature–vacuum swing adsorption processes employing advanced amine-functionalised and metal–organic framework (MOF) sorbents, followed by a separate Sabatier reactor, yielding a capture–conversion cost of approximately $740 per tonne of CO₂. Crucially, the analysis identifies that (in the DAC mode of operation) economic viability is primarily governed by the energy penalties of air handling and pressure drop rather than purely catalytic costs, reorienting research priorities toward sorbent engineering and structured reactor geometries

At the reactor scale, a predictive kinetic framework is established for calcium-based DFMs using Bayesian optimisation for parameter estimation. This study resolves the temperature-dependent trade-offs between adsorption capacity and reaction rates, identifying carbonate decomposition as the primary kinetic bottleneck during hydrogenation. Mechanistically, the work demonstrates that the DFM pathway benefits from a spillover effect, which lowers the activation energy for hydrogenation and the simultaneous regeneration compared to traditional methanation systems.

Finally, the framework implements an open-source multi-objective optimisation workflow to characterise the reactor's performance envelope under fully converged Cyclic Steady-State conditions. This study utilises an evolutionary-based optimisation approach to formalise the Pareto-optimal trade-offs between methane purity, CO₂ recovery, and productivity. The analysis provides a quantitative basis for selecting operating conditions by identifying gas flow rate and hydrogenation duration as the dominant operational levers while assessing the sensitivity of these metrics to cost-sensitive variables such as hydrogen intensity and DFM loading.

The integrated synthesis of findings across analytical scales reveals that DFM commercial viability depends on the precise synchronisation of thermodynamic capacity, kinetic mechanisms, and operational scheduling. A critical capacity-kinetics conflict exists where the strong basicity required for high storage inherently increases the energy barrier for carbonate decomposition. Moreover, the primary process bottleneck shifts depending on the application context because direct air capture is limited by the duration of the adsorption stage while point-source operation is constrained by the kinetics of the hydrogenation phase. Economic viability rests on a techno-economic optimum in hydrogen intensity that balances productivity gains against procurement costs. Finally, scaling contradictions between material loading and parasitic pressure drop favour modular reactor configurations to maintain efficiency.

Ultimately, this thesis delivers a systematic computational framework that demonstrates the viability of the DFM process as a promising pathway for ICCU, providing a predictive model of performance while identifying critical kinetic and economic bottlenecks to guide future scale-up and the selection of optimal design features.