Data-Driven Simultaneous Process Optimization and Adsorbent Selection for Vacuum Pressure Swing Adsorption

Sun Hye Kim; Héctor Octavio Rubiera Landa; Suryateja Ravutla; Matthew J. Realff; Fani Boukouvala

doi:10.1016/j.cherd.2022.10.002

Data-Driven Simultaneous Process Optimization and Adsorbent Selection for Vacuum Pressure Swing Adsorption

Sun Hye Kim, Héctor Octavio Rubiera Landa, Suryateja Ravutla, Matthew J. Realff, Fani Boukouvala

Chemische Ingenieurstechnieken en Industriele Scheikunde

Onderzoeksoutput: Article › peer review

15 Citaten (Scopus)

Samenvatting

Technologies for post-combustion carbon capture are essential for the reduction of greenhouse gas emissions to the atmosphere. However, they are still associated with high costs and energy consumption. Intensified processes for carbon capture have the potential to overcome these challenges due to their higher efficiency, lower capital cost, and increased operational flexibility. This work investigates simultaneous optimization of process conditions and adsorbent selection for a modular Vacuum Pressure-Swing Adsorption system designed for CO₂ capture. Both surrogate-based Nonlinear Programming and Mixed-Integer Nonlinear Programming approaches are applied and compared in terms of computational efficiency and solution accuracy. Moreover, process performance results are examined by applying several data analytics techniques to gain insights into the material-process correlations. Data-driven classifiers and neural networks can accurately predict whether a material is likely to satisfy purity, recovery, and energy constraints when operated at optimal process conditions.

Originele taal-2	English
Pagina's (van-tot)	1013-1028
Aantal pagina's	16
Tijdschrift	Chemical Engineering Research and Design
Volume	188
DOI's	https://doi.org/10.1016/j.cherd.2022.10.002
Status	Published - dec. 2022

Bibliografische nota

Funding Information:
H. O. Rubiera Landa and M. J. Realff were partially supported by the US DOE through grant: DE-FE0026433 for project: ‘Enabling 10 mol/kg swing capacity via heat-integrated sub-ambient pressure swing adsorption’. S.H. Kim and F. Boukouvala would like to acknowledge funding from DOE/AIChE through the RAPID Manufacturing Institute (Synopsis Project; 102623 ) and NSF-CBET ( 1805724 , 1944678 ). Any opinions, findings, conclusions or recommendations expressed herein are those of the authors and do not necessarily reflect the view of the DOE.

Publisher Copyright:
© 2022 Institution of Chemical Engineers

Copyright:
Copyright 2022 Elsevier B.V., All rights reserved.

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title = "Data-Driven Simultaneous Process Optimization and Adsorbent Selection for Vacuum Pressure Swing Adsorption",

abstract = "Technologies for post-combustion carbon capture are essential for the reduction of greenhouse gas emissions to the atmosphere. However, they are still associated with high costs and energy consumption. Intensified processes for carbon capture have the potential to overcome these challenges due to their higher efficiency, lower capital cost, and increased operational flexibility. This work investigates simultaneous optimization of process conditions and adsorbent selection for a modular Vacuum Pressure-Swing Adsorption system designed for CO2 capture. Both surrogate-based Nonlinear Programming and Mixed-Integer Nonlinear Programming approaches are applied and compared in terms of computational efficiency and solution accuracy. Moreover, process performance results are examined by applying several data analytics techniques to gain insights into the material-process correlations. Data-driven classifiers and neural networks can accurately predict whether a material is likely to satisfy purity, recovery, and energy constraints when operated at optimal process conditions.",

author = "Kim, {Sun Hye} and {Rubiera Landa}, {H{\'e}ctor Octavio} and Suryateja Ravutla and Realff, {Matthew J.} and Fani Boukouvala",

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year = "2022",

month = dec,

doi = "10.1016/j.cherd.2022.10.002",

language = "English",

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AU - Realff, Matthew J.

AU - Boukouvala, Fani

N1 - Funding Information: H. O. Rubiera Landa and M. J. Realff were partially supported by the US DOE through grant: DE-FE0026433 for project: ‘Enabling 10 mol/kg swing capacity via heat-integrated sub-ambient pressure swing adsorption’. S.H. Kim and F. Boukouvala would like to acknowledge funding from DOE/AIChE through the RAPID Manufacturing Institute (Synopsis Project; 102623 ) and NSF-CBET ( 1805724 , 1944678 ). Any opinions, findings, conclusions or recommendations expressed herein are those of the authors and do not necessarily reflect the view of the DOE. Publisher Copyright: © 2022 Institution of Chemical Engineers Copyright: Copyright 2022 Elsevier B.V., All rights reserved.

PY - 2022/12

Y1 - 2022/12

N2 - Technologies for post-combustion carbon capture are essential for the reduction of greenhouse gas emissions to the atmosphere. However, they are still associated with high costs and energy consumption. Intensified processes for carbon capture have the potential to overcome these challenges due to their higher efficiency, lower capital cost, and increased operational flexibility. This work investigates simultaneous optimization of process conditions and adsorbent selection for a modular Vacuum Pressure-Swing Adsorption system designed for CO2 capture. Both surrogate-based Nonlinear Programming and Mixed-Integer Nonlinear Programming approaches are applied and compared in terms of computational efficiency and solution accuracy. Moreover, process performance results are examined by applying several data analytics techniques to gain insights into the material-process correlations. Data-driven classifiers and neural networks can accurately predict whether a material is likely to satisfy purity, recovery, and energy constraints when operated at optimal process conditions.

AB - Technologies for post-combustion carbon capture are essential for the reduction of greenhouse gas emissions to the atmosphere. However, they are still associated with high costs and energy consumption. Intensified processes for carbon capture have the potential to overcome these challenges due to their higher efficiency, lower capital cost, and increased operational flexibility. This work investigates simultaneous optimization of process conditions and adsorbent selection for a modular Vacuum Pressure-Swing Adsorption system designed for CO2 capture. Both surrogate-based Nonlinear Programming and Mixed-Integer Nonlinear Programming approaches are applied and compared in terms of computational efficiency and solution accuracy. Moreover, process performance results are examined by applying several data analytics techniques to gain insights into the material-process correlations. Data-driven classifiers and neural networks can accurately predict whether a material is likely to satisfy purity, recovery, and energy constraints when operated at optimal process conditions.

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Data-Driven Simultaneous Process Optimization and Adsorbent Selection for Vacuum Pressure Swing Adsorption

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