Modifying absorption process configurations to improve their performance for Post-Combustion CO2 capture - What have we learned and what is still Missing?

Mitigation of climate change by reducing the anthropogenic CO2 emissions has recently gained huge momentum. A prime pathway to achieve it in the short-medium term lies within capturing CO2 from industries' flue gases. Post-combustion CO2 capture (PCC) is the most mature methodology for large-scale CO2 capture and closest to market. However, the conventional chemical absorption-based PCC process implies high regeneration energy requirements, constituting the majority of the energy load when adding PCC to the plant from which the CO2 is going to be captured. As such, reducing the overall parasitic load of the conventional process is of interest. Changing the solvent or improving the process flowsheet are two main methodologies to achieve higher ener-getic efficiency and lower exergy losses. As there are several reviews dealing with novel solvents, this review critically evaluates the progress in improving the conventional PCC process through modifications of process configurations and shadowed by solvent replacements as drop-in solutions. The process configurations were grouped into absorption and stripping enhancement categories. A comparative analysis was conducted between the different available schemes in the literature and interactions between solvents and process configurations were showcased, highlighting the advantages and shortcomings of some of them. The review reveals the need for standardized framework and optimization algorithms using exergo-economic performance indicators for inte-grated solvent and process synthesis.

Automated summary

The mitigation of climate change through reducing anthropogenic CO2 emissions has gained significant traction recently. Improving the conventional PCC process by changing solvents or enhancing process flowsheets is crucial for achieving higher energetic efficiency and lower exergy losses. Standardized frameworks and optimization algorithms are needed for integrated solvent and process synthesis.