Aerosol emissions from water-lean solvents for post-combustion CO2 capture

• Published in: International journal of greenhouse gas control Vol. 106; no. C
• Main Authors: Gupta, Vijay, Mobley, Paul, Tanthana, Jak, Cody, Lucas, Barbee, David, Lee, Jacob, Pope, Roger, Chartier, Ryan, Thornburg, Jonathan, Lail, Marty
• Format: Journal Article
• Language: English
• Published: United States Elsevier 27.02.2021
• Subjects: Aerosol emissions; Energy & fuels; ENGINEERING; ENVIRONMENTAL SCIENCES; Post-combustion CO2 capture; Projection to latent squares (PLS); Statistical modeling; Water-lean solvent
• ISSN: 1750-5836; 1878-0148

Advanced water-lean solvents (WLS) for post-combustion CO2 capture have been gaining interest due to their ability to reduce the parasitic penalty from energy needed for solvent regeneration. Commercial implementation of these novel CO2 capture technologies hinges on successful control of amine emissions. RTI conducted a parametric study of fundamental and operational variables influence on overall amine aerosol and vapor emissions from our water-lean solvent eCO2Sol™ using our 6-kW equivalent bench-scale gas absorption system. The parametric testing used a simulated flue gas with 15 % CO2, 2.3–4.2 % H2O, and 0–6 ppm sulfite (SO3) to examine the impact of the presence of aerosols to the capture performance and amine emissions from the system. The SO3 reacts with water in the flue gas to create H2SO4, which forms liquid aerosol droplets and provide nucleation sites for growth of aerosols. Scanning Mobility Particle Sizer and Aerodynamic Particle Sizer instruments monitored the aerosol particle size distribution. Parametric testing results suggested that the presence of the aerosols in the flue gas could increase the overall amine emissions by 10X compared to the baseline emissions from WLS’s vapor pressure. Principal component analysis (PCA) and projection to latent squares (PLS) developed models to predict the aerosol-based amine emissions from process data. The predictive PLS model had a correlation coefficient (Q2) of 0.92 and could predict the aerosol-based emissions from the NAS process with ±15 % accuracy (average absolute deviation, AAD). The PLS regression model also identified key variables affecting aerosol-based emissions from WLS.