(525d) Decision Matrix Tool for Selection of Novel Solvents and Absorption Process Modifications to Improve the Performance of Post-Combustion CO2 Capture

A prime pathway to reduce the anthropogenic CO₂ emissions in the short-medium term lies within capturing CO₂ from industries’ flue gases, being post-combustion CO₂ capture (PCC) the most mature methodology nowadays. However, the conventional chemical absorption-based PCC process by using aqueous alkanolamines (e.g., monoethanolamine (MEA)) implies high regeneration energy requirements, as well as losses due to degradation and evaporation [1]. Changing the solvent or improving the process flowsheet are the two main methodologies to achieve higher energy efficiency and lower exergy losses. Subsequently, the development of new chemical/physical solvents and process designs are in progress to enhance the performance or mitigate issues associated with the established process with aqueous MEA [2]. Nevertheless, evaluating these emerging solvents/processes remains limited to overall characteristics such as the net power for regeneration, and constrained to conditions far from typical process operating conditions with non-standardized measurements. Considering the vast amount of research in this area, a rapid and reliable procedure to screen emerging solvents/processes for CO₂ capture and rank them against processes currently in use is needed.

Therefore, in this contribution, we showcase the capabilities and results of a developed robust decision-matrix tool [3] devoted to the evaluation and identification of top-performer prime candidates for the next generation of PCC plants and compare them to the traditional MEA aqueous solution at relevant gas separation process conditions (i.e., T, P, CO₂ concentration). The equilibrium-based process model includes 50+ different amine co-solvents (including water-free and water-lean systems) and different process configurations allowing extracting conclusions in terms of technical and economic performance of the solvents and processes, as well as performing sensitivity analysis in terms of economic parameters. Furthermore, the soft-SAFT equation of state [4,5] was used when needed to generate missing thermophysical data from the literature.

The evaluation of candidate solvents and process modifications was based on key performance indicators such as the net power of regeneration, capture cost per tonne of CO₂, CAPEX, and OPEX. The model confirms that the column sizing and reboiler duty represent the two most important process parameters to be used for fast comparative performance, while also showing that the two solvent properties that have more influence on the capital cost, altogether with the absorption capacity, are the absorption enthalpy (heat of absorption) and the liquid phase viscosity. Moreover, the CO₂ gas concentration is a stronger determinant of the cost of capture than the degree of capture. This implies that for the same investment, it is economically preferable to capture CO₂ from higher concentration sources. The cost of capture is nearly constant for capture rates ~85%, but the marginal cost increases exponentially if rates above >95% are desired.

In conclusion, by applying the standardized metrics, the top promising amines obtained from the analysis were found to be 2-amino-1,3-propandiol, 2-(isopropylamino)etanol, N-(3-aminopropyl)1,3-propanediamine, aminoethylethanolamine, 3-(methylamino)propylamine, triethanolamine, 2 (ethylamino)ethanol, 2-methyl piperazine, piperazine + 2-amino-2-methyl-1-propanol, and 2-(methylamino)ethanol.

This work has been financially supported by Khalifa University of Science and Technology through project RC2-2019-007.

References

[1]. N. MacDowell et al., Energy Environm. Sci., 3, 1645–1669 (2010).

[2]. I.I.I. Alkhatib, L.M.C. Pereira, A. Alhajaj, and L.F. Vega, J. CO₂ Util., 35, 126–144 (2020).

[3]. L.F. Vega et al., IEAGHG Technical Report 2022-03 “Prime Solvent candidates for next generation of PCC plants” (February 2022).

[4]. F.J. Blas, and L.F. Vega, Mol. Phys. 92, 135–150 (1997).

[5]. I.I.I Alkhatib, L.M.C. Pereira, and L.F. Vega, Ind. Eng. Chem. Res., 58, 6870-6886 (2019).