The CO2MAN project aims to develop an AI tool to screen solvents for CO2 capture, targeting 25% efficiency gains and 15% cost reductions.
Solvent-based CO2 capture has proven to be one of the only capture technologies that is scalable before 2030. However, current solvent technology still faces challenges such as high energy penalty, solvent degradation, and corrosiveness. If these challenges are not solved by 2030, there is a risk that the cost of CO2 capture plants will be too high.
The aim of the CO2MAN project is to enhance an existing prediction tool with unique capabilities in predicting solvent properties, to develop an artificial intelligence (AI) tool that will be used to screen more than 1 billion solvent formulations, meeting the following criteria:
- High CO2 absorption capacity and absorption rate to minimise equipment size
- High selectivity to capture CO2
- Low regeneration energy
- High stability towards solvent degradation
- Low environmental impact
- Cost-effective to minimise the cost of capture
- Non-corrosive
The developed tool will be the first to allow screening based on the overall efficiency and cost of full-scale CO2 capture plants.
Based on the predictions, the 3 most promising solvent candidates will be validated at lab scale and demonstrated at pilot scale at the Skærbækværket biomass combustion facility.
We expect the best-performing solvent to result in >25% improved capture plant efficiency, >15% lower costs, and have improved corrosion and environmental profile compared to current state-of-the-art solvents.
Main supervisor
Philip Fosbøl
Co- supervisor
Randi Neerup