UKCCSRC Knowledge Exchange Conference 2024 – Parallel 1a: CO2 Capture – Next Generation (ECR Meeting Fund)

Dr Usman Ali (Net Zero Industry Innovation Centre, Teesside University) and Zi Huang (University of Oxford) share their takeaways from “Parallel 1a: CO2 Capture – Next Generation” at the UKCCSRC Knowledge Exchange Conference 2024.

In the parallel session about next generation CO₂ capture, talks were delivered regarding molten carbonate membranes for CO₂ separation, porous carbon materials for the CO₂ separation and conversion, and adoption of machine learning to carry out sensitivity analysis of the blue hydrogen production. The parallel session was chaired by Professor Dawid Hanak from Teesside University.

Steam-Driven CO₂ Separation Using Supported Molten Salt Membranes

Jacqueline Penn, from Newcastle University, presented a novel approach to high-temperature CO₂ separation using supported molten carbonate and hydroxide salt membranes. Traditional membranes rely on a CO₂ pressure gradient to achieve separation; however, Penn’s work introduces a mechanism which uses steam in the sweep gas to drive CO₂ flux, even in the absence of a CO₂ pressure differential. The most mature membranes for CO₂ separation are polymeric membranes, which often do not meet these requirements and are poorly suited to the high temperature and humidity of the flue gas. This study considers the measurement of the CO₂ permeation in molten-carbonate and hydroxide salt membranes by performing the sensitivity analysis of the temperature, humidity and CO₂ concentration. Her findings reveal that adding steam significantly increases CO₂ permeation across the membrane, a breakthrough which could optimize carbon capture from industrial flue gases. These membranes also outperform traditional polymeric membranes, showing orders of magnitude improvements in permeability and selectivity.

Next-Generation Impact: By enabling CO₂ separation without relying on a pressure gradient, this steam-driven process offers a more versatile and efficient solution for high-temperature carbon capture, a potential game-changer in the industrial applications.

Nitrogen-Doped Carbon Materials for CO₂ Adsorption and Conversion

Dr. Stephen Lyth, from the University of Strathclyde, explored nitrogen-doped porous carbon materials for selective CO₂ adsorption and electrochemical reduction. By controlling the nitrogen content in the carbon foam, his team developed materials which enhance CO₂ binding and show high selectivity in converting CO₂ to synthesis gas (CO and H₂). Sustainable electrocatalysts were tested in an electrochemical flow cell, displaying record selectivity and activity for production of CO over the competing hydrogen evolution reaction. This study has explored the effect of tailoring the nitrogen content and pore size distribution on the CO₂ uptake capacity and selectivity, with reference to the isosteric heat of adsorption. This technology stands out by integrating capture and utilization, allowing CO₂ to be both captured and transformed into valuable chemicals. Traditional adsorbents lack this versatility and often suffer from stability issues; however, Lyth’s materials demonstrate durability over multiple cycles, suggesting strong potential for the industrial use.

Next-Generation Impact: This integration of capture and utilization in a single material exemplifies a new approach in carbon capture and utilization, where CO₂ can be converted directly into high-value products. This paves the way for sustainable carbon capture applications with added economic benefits.

Data-Driven Optimization for Blue Hydrogen Production

Billy Davies, from Brunel University of London, introduced a machine learning-based global sensitivity analysis approach to optimize blue hydrogen production. Using different computational methods, including SHAP, MORRIS and SOBOL, his team analyzed the key input variables, such as the CaO/CH₄ ratio, which influence hydrogen purity and efficiency. This data-driven method enhances model accuracy and interpretability, offering a sophisticated tool for optimizing complex systems in carbon capture and utilization processes. The research indicated star-based rating system which compares the GSA approaches in terms of robustness, computational efficiency and interpretability. Unlike traditional sensitivity analysis methods, which are often computationally intensive and limited in scope, Davies’ approach improves both speed and scalability.

Next-Generation Impact: By merging global sensitivity analysis with machine learning, this research represents a shift towards intelligent, adaptive process optimization in carbon capture and utilization. Such data-driven methods could improve performance and lower costs in complex carbon capture and hydrogen production processes.

These talks have been very informative in terms of the new developments across membranes, porous carbon materials for the CO₂ separation and data-driven approaches for analyzing the blue hydrogen production whilst maintaining high computational efficiency. The parallel session ended with a round of applause for the presenters for their insightful work.