TY - JOUR
T1 - Analysis of a Batch Adsorber Analogue for Rapid Screening of Adsorbents for Postcombustion CO2 Capture
AU - Balashankar, Vishal Subramanian
AU - Rajagopalan, Ashwin Kumar
AU - Pauw, Ruben de
AU - Avila, Adolfo M.
AU - Rajendran, Arvind
PY - 2019/2/27
Y1 - 2019/2/27
N2 - A simplified proxy model based on a well-mixed batch adsorber for vacuum swing adsorption (VSA) based CO2 capture from dry postcombustion flue gas is presented. A graphical representation of the model output allows for the rationalization of broad trends of process performance. The results of the simplified model are compared with a detailed VSA model that takes into account mass and heat transfer, column pressure drop, and column switching, in order to understand its potential and limitations. A new classification metric to identify whether an adsorbent can produce CO2 purity and recovery values that meet current U.S. Department of Energy (US-DOE) targets for postcombustion CO2 capture and to calculate the corresponding parasitic energy is developed. The model, which can be evaluated within a few seconds, showed a classification Matthew correlation coefficient of 0.76 compared to 0.39, the best offered by any traditional metric. The model was also able to predict the energy consumption within 15% accuracy of the detailed model for 83% of the adsorbents studied. The developed metric and the correlation are then used to screen the NIST/ARPA-E database to identify promising adsorbents for CO2 capture applications.
AB - A simplified proxy model based on a well-mixed batch adsorber for vacuum swing adsorption (VSA) based CO2 capture from dry postcombustion flue gas is presented. A graphical representation of the model output allows for the rationalization of broad trends of process performance. The results of the simplified model are compared with a detailed VSA model that takes into account mass and heat transfer, column pressure drop, and column switching, in order to understand its potential and limitations. A new classification metric to identify whether an adsorbent can produce CO2 purity and recovery values that meet current U.S. Department of Energy (US-DOE) targets for postcombustion CO2 capture and to calculate the corresponding parasitic energy is developed. The model, which can be evaluated within a few seconds, showed a classification Matthew correlation coefficient of 0.76 compared to 0.39, the best offered by any traditional metric. The model was also able to predict the energy consumption within 15% accuracy of the detailed model for 83% of the adsorbents studied. The developed metric and the correlation are then used to screen the NIST/ARPA-E database to identify promising adsorbents for CO2 capture applications.
UR - https://doi.org/10.1021/acs.iecr.8b05420
U2 - 10.1021/acs.iecr.8b05420
DO - 10.1021/acs.iecr.8b05420
M3 - Article
SN - 0888-5885
VL - 58
SP - 3314
EP - 3328
JO - Industrial & Engineering Chemistry Research
JF - Industrial & Engineering Chemistry Research
IS - 8
ER -
