TY - JOUR
T1 - A Machine Learning-Aided Equilibrium Model of VTSA Processes for Sorbents Screening Applied to CO2Capture from Diluted Sources
AU - Grimm, Alexa
AU - Gazzani, Matteo
N1 - Funding Information:
This work was sponsored by Shell Global Solutions International BV. The authors would like to thank: Leonie Horst for her contribution in building the screening framework, especially the automatic data acquisition from the NIST database; Prof. Arvind Rajendran (University of Alberta) for the fruitful discussions on the draft version of the paper; Prof. Kramer (Utrecht University) and Prof. Sint Annaland (TU Eindhoven) for the fruitful discussions during the execution of the research. 1
PY - 2022/9/21
Y1 - 2022/9/21
N2 - The large design space of the sorbents' structure and the associated capability of tailoring properties to match process requirements make adsorption-based technologies suitable candidates for improved CO2capture processes. This is particularly of interest in novel, diluted, and ultradiluted separations as direct CO2removal from the atmosphere. Here, we present an equilibrium model of vacuum temperature swing adsorption cycles that is suitable for large throughput sorbent screening, e.g., for direct air capture applications. The accuracy and prediction capabilities of the equilibrium model are improved by incorporating feed-forward neural networks, which are trained with data from rate-based models. This allows one, for example, to include the process productivity, a key performance indicator typically obtained in rate-based models. We show that the equilibrium model reproduces well the results of a sophisticated rate-based model in terms of both temperature and composition profiles for a fixed cycle as well as in terms of process optimization and sorbent comparison. Moreover, we apply the proposed equilibrium model to screen and identify promising sorbents from the large NIST/ARPA-E database; we do this for three different (ultra)diluted separation processes: direct air capture, yCO2= 0.1%, and yCO2= 1.0%. In all cases, the tool allows for a quick identification of the most promising sorbents and the computation of the associated performance indicators. Also, in this case, outcomes are very well in line with the 1D model results. The equilibrium model is available in the GitHub repository https://github.com/UU-ER/SorbentsScreening0D.
AB - The large design space of the sorbents' structure and the associated capability of tailoring properties to match process requirements make adsorption-based technologies suitable candidates for improved CO2capture processes. This is particularly of interest in novel, diluted, and ultradiluted separations as direct CO2removal from the atmosphere. Here, we present an equilibrium model of vacuum temperature swing adsorption cycles that is suitable for large throughput sorbent screening, e.g., for direct air capture applications. The accuracy and prediction capabilities of the equilibrium model are improved by incorporating feed-forward neural networks, which are trained with data from rate-based models. This allows one, for example, to include the process productivity, a key performance indicator typically obtained in rate-based models. We show that the equilibrium model reproduces well the results of a sophisticated rate-based model in terms of both temperature and composition profiles for a fixed cycle as well as in terms of process optimization and sorbent comparison. Moreover, we apply the proposed equilibrium model to screen and identify promising sorbents from the large NIST/ARPA-E database; we do this for three different (ultra)diluted separation processes: direct air capture, yCO2= 0.1%, and yCO2= 1.0%. In all cases, the tool allows for a quick identification of the most promising sorbents and the computation of the associated performance indicators. Also, in this case, outcomes are very well in line with the 1D model results. The equilibrium model is available in the GitHub repository https://github.com/UU-ER/SorbentsScreening0D.
UR - http://www.scopus.com/inward/record.url?scp=85137904242&partnerID=8YFLogxK
U2 - 10.1021/acs.iecr.2c01695
DO - 10.1021/acs.iecr.2c01695
M3 - Article
C2 - 36164596
AN - SCOPUS:85137904242
SN - 0888-5885
VL - 61
SP - 14004
EP - 14019
JO - Industrial and Engineering Chemistry Research
JF - Industrial and Engineering Chemistry Research
IS - 37
ER -