A thermodynamic-based mixed-integer linear model of post-combustion carbon capture for reliable use in energy system optimisation

Lukas Weimann, Guus Dubbink, Louis van der Ham, Matteo Gazzani

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Assessing the role of carbon capture in energy systems dominated by non-dispatchable renewable energy sources requires a reliable and accurate model. However, carbon capture models used in complex systems optimisation are often very simplified. Therefore, we developed a mixed-integer linear model of post-combustion carbon capture starting from rigorous thermodynamic modelling in Aspen Plus. The final model decides the size and the operation of the capture process and returns the cost and energy requirements as a function of the CO2 concentration and the flow rate of the treated flue gas. Validation against actual plant data (Petra Nova) showed excellent accuracy with a deviation in total CO2 captured of just 2%. By applying the model to an exemplary case study, we show that it allows for co-optimising renewables deployment and carbon capture design and operation for a gas turbine, thus opening opportunities to explore new system designs of practical added value.
Original languageEnglish
Article number120738
Number of pages16
JournalApplied Energy
Volume336
DOIs
Publication statusPublished - 15 Apr 2023

Bibliographical note

Publisher Copyright:
© 2023 The Author(s)

Funding

ACT ELEGANCY, Project No 271498, has received funding from DETEC (CH) , FZJ/PtJ (DE) , RVO (NL) , Gassnova (NO) , BEIS (UK) , Gassco AS and Statoil Petroleum AS , and is cofunded by the European Commission under the Horizon 2020 programme, ACT Grant Agreement No 691712 .

FundersFunder number
DETEC
Gassco AS
Statoil Petroleum AS
Horizon 2020 Framework Programme
Department for Business, Energy and Industrial Strategy, UK Government
Rijksdienst voor Ondernemend Nederland
ACT Government691712
European Commission

    Keywords

    • Carbon capture
    • Energy system
    • MEA
    • MILP
    • Optimisation

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