A predictive model for the techno-economic assessment of CO₂ chemisorption processes applicable to a large number of amine solvents
File(s)
Published version
Author(s)
Lee, Ye Seol
Jackson, George
Galindo, Amparo
Adjiman, Claire S
Type
Journal Article
Abstract
With the growing need to reduce carbon dioxide (CO2) emissions, there have been substantial efforts to identify
new solvents that can improve the overall performance of chemical-absorption CO2 capture processes. Given
the large number of potential solvents, computer-aided molecular and process design (CAMPD) approaches
can play a critical role in accelerating the search for optimal solvents by enabling the systematic exploration
of solvent candidates and process conditions. One of the challenges in developing such a framework is the
requirement for a process model that can be used to capture the interactions between process performance
and solvent structure without significantly increasing its numerical complexity. In the current work, a model for the absorption–desorption of CO2 is developed using the predictive SAFT-𝛾-Mie group-contribution approach, allowing the performance of numerous solvents to be assessed without the need for extensive experimental data. In order to avoid convergence difficulties when solving this highly nonlinear model, a tailored initialization strategy is established, using an adapted inside-out algorithm to prime a nonlinear equation solver for each column, providing a good initial guess for the whole flowsheet. Tests on three solvents confirm the robustness of the approach. Building on this enhanced numerical stability, the model is validated by comparison against pilot-plant data, showing good accuracy. A detailed parametric study of the effect of the key process variables is undertaken; the important role of the CO2
capture rate, of the lean solvent temperature and
loading, and of the desorber pressure is highlighted. The results of the parametric study are used to formulate
an optimization problem which is successfully solved for four solvents. A large reduction in total annualized
cost and energy requirements is achieved by tuning the operating conditions to each solvent considered. The
predictive capability, robustness, and reliability of the proposed model and associated initialization strategy
open the way for the evaluation of a large number of novel solvents.
new solvents that can improve the overall performance of chemical-absorption CO2 capture processes. Given
the large number of potential solvents, computer-aided molecular and process design (CAMPD) approaches
can play a critical role in accelerating the search for optimal solvents by enabling the systematic exploration
of solvent candidates and process conditions. One of the challenges in developing such a framework is the
requirement for a process model that can be used to capture the interactions between process performance
and solvent structure without significantly increasing its numerical complexity. In the current work, a model for the absorption–desorption of CO2 is developed using the predictive SAFT-𝛾-Mie group-contribution approach, allowing the performance of numerous solvents to be assessed without the need for extensive experimental data. In order to avoid convergence difficulties when solving this highly nonlinear model, a tailored initialization strategy is established, using an adapted inside-out algorithm to prime a nonlinear equation solver for each column, providing a good initial guess for the whole flowsheet. Tests on three solvents confirm the robustness of the approach. Building on this enhanced numerical stability, the model is validated by comparison against pilot-plant data, showing good accuracy. A detailed parametric study of the effect of the key process variables is undertaken; the important role of the CO2
capture rate, of the lean solvent temperature and
loading, and of the desorber pressure is highlighted. The results of the parametric study are used to formulate
an optimization problem which is successfully solved for four solvents. A large reduction in total annualized
cost and energy requirements is achieved by tuning the operating conditions to each solvent considered. The
predictive capability, robustness, and reliability of the proposed model and associated initialization strategy
open the way for the evaluation of a large number of novel solvents.
Date Issued
2023-12-01
Date Acceptance
2023-11-03
Citation
Chemical Engineering Research and Design, 2023, 200, pp.615-636
ISSN
0263-8762
Publisher
Elsevier
Start Page
615
End Page
636
Journal / Book Title
Chemical Engineering Research and Design
Volume
200
Copyright Statement
© 2023 The Author(s). Published by Elsevier Ltd on behalf of Institution of Chemical Engineers. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
License URL
Subjects
AQUEOUS-SOLUTIONS
CAPTURE PROCESS
CARBON-DIOXIDE
CHEMICAL ABSORPTION
CO2 chemical absorption process
Engineering
Engineering, Chemical
EQUATION-OF-STATE
FIRED POWER-PLANTS
Inside-out algorithm
INTEGRATED SOLVENT
MOLECULAR DESIGN
MONOETHANOLAMINE
Process modelling
SAFT
SAFT-gamma Mie equation of state
Science & Technology
Techno-economic analysis
Technology
Publication Status
Published
Date Publish Online
2023-11-10