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Selecting the approach for dynamic modelling of CO2 capture in biomass/waste fired CHP plants
Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
Tianjin Key Laboratory of Refrigeration Technology, Tianjin University of Commerce, Tianjin, 300134, China.
Mälardalen University, School of Business, Society and Engineering, Future Energy Center.ORCID iD: 0000-0002-5341-3656
Mälardalen University, School of Business, Society and Engineering, Future Energy Center.ORCID iD: 0000-0002-3485-5440
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2023 (English)In: International Journal of Greenhouse Gas Control, ISSN 1750-5836, E-ISSN 1878-0148, Vol. 130, article id 104008Article in journal (Refereed) Published
Abstract [en]

Integrating CO2 capture with biomass/waste fired combined heat and power (CHP) plants is a promising method to achieve negative emissions. However, the use of versatile biomass/waste and the dynamic operation of CHP plants result in bigger fluctuations in the properties of flue gas (FG), e.g. CO2 concentration (CO2vol%) and flowrates, and the heat that can be used for CO2 capture, when comparing with coal fired power plants. To address such a challenge, dynamic modelling is essential to accurately estimate the amount of captured CO2 and optimize the operation of CO2 capture. This paper compares three dynamic approaches commonly used in literature, namely using the ideal static model (IST) and using dynamic models without control (Dw/oC) and with control (DwC), for MEA based chemical absorption CO2 capture. The performance of approaches is assessed under the variations of key factors, including the flowrate and CO2vol% of FG, and the available heat for CO2 capture. Simulation results show clear differences. For example, when the CO2vol% drops from 15.7 % to 9.7 % (about 38 %) within 4 hours, DwC gives the highest amount of captured CO2, which is 7.3 % and 22.3 % higher than IST and Dw/oC, respectively. It is also found that the time step size has a clear impact on the CO2 capture amount, especially for DwC. Based on the results, suggestions are also provided regarding the selection of dynamic modelling approaches for different purposes of simulations.

Place, publisher, year, edition, pages
Elsevier Ltd , 2023. Vol. 130, article id 104008
Keywords [en]
Bioenergy with carbon capture and storage (BECCS), Biomass/waste fired combined heat and power plants, Dynamic modelling approach, Dynamic performance, MEA based chemical absorption, Biomass, Carbon capture, Coal fired power plant, Cogeneration plants, Ethanolamines, Fossil fuel power plants, Gas plants, More electric aircraft, Bioenergies with carbon capture and storages, Bioenergy with carbon capture and storage, Biomass wastes, Biomass/waste fired combined heat and power plant, Chemical absorption, Dynamic modeling approach, Dynamics models, Static modelling, Carbon dioxide
National Category
Energy Systems
Identifiers
URN: urn:nbn:se:mdh:diva-64753DOI: 10.1016/j.ijggc.2023.104008ISI: 001112149200001Scopus ID: 2-s2.0-85175621556OAI: oai:DiVA.org:mdh-64753DiVA, id: diva2:1812307
Available from: 2023-11-15 Created: 2023-11-15 Last updated: 2024-03-08Bibliographically approved
In thesis
1. Dynamic modeling of MEA-based CO2 capture in biomass-fired CHP plants
Open this publication in new window or tab >>Dynamic modeling of MEA-based CO2 capture in biomass-fired CHP plants
2024 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Global warming is a significant threat to our planet. Adopting the Paris Agreement is a global action that aims to reduce greenhouse gas emissions. An extensive deployment of negative emission technologies (NETs) is required to achieve the targets set by the Paris Agreement. Bioenergy with carbon capture and storage (BECCS) is emerging as one of the most promising NETs. Among different biomass utilization processes, integrating BECCS with biomass-fired and waste-fired combined heat and power (bio-CHP and w-CHP) plants has been considered the most feasible solution. Bio/w-CHP plants are characterized by high fluctuations in operation, which can result in more dynamic variations of flue gas (FG) flowrates and compositions and available heat for CO2 capture. Such changes can clearly affect the performance of CO2 capture; therefore, doing dynamic simulations becomes crucial.

This thesis aims to investigate the performance of different dynamic physical model-based approaches and provide suggestions for approach selection. In addition, the data-driven modeling approach, which is an emerging technology, has also been tested.

Three physical model-based approaches include the ideal static model (IST), the dynamic approach without control (Dw/oC), and the dynamic approach with control (DwC). To compare their performance, the operating data from an actual waste CHP plant is employed. Various cases have been defined considering different critical operating parameters, including the FG flowrate, the CO2 concentration (CO2vol%), and the available heat for CO2 capture. Apparent differences can be observed in the results from different approaches. For example, when the CO2vol% drops from 15.7 % to 9.7 % (about 38 %) within 4 hours, the difference in the captured CO2 can be up to 22% between DwC and Dw/oC. It is worth noting that when there are both increases and decreases in the variations of parameters, the differences become smaller. 

Based on the comparison, the recommendations on approaches have been summarized. Dw/oC is recommended for checking the boundary of safety operation by the response analysis. DwC is recommended for designing the control system, observing the flexible dynamic operation, estimating the short-term CO2 capture potential, and optimizing the hourly dynamic operation. IST is recommended for estimating the long-term CO2 capture potential, and optimizing the long-term dynamic operation when the input parameters vary not as often as hourly.

A data-driven model, Informer, is developed to model CO2 capture dynamically. The dataset is generated by using a physical model. The FG flowrate, the CO2vol%, the lean solvent flowrate, and the available heat for CO2 capture are employed as input parameters, and the CO2 capture rate and the energy penalty are chosen as outputs. The results show that Informer can accurately predict dynamic CO2 capture. The mean absolute percentage error (MAPE) was found to be 6.2% and 2.7% for predicting the CO2 capture rate and energy penalty, respectively.

Place, publisher, year, edition, pages
Mälardalens universitet, 2024
Series
Mälardalen University Press Licentiate Theses, ISSN 1651-9256 ; 356
National Category
Environmental Engineering
Identifiers
urn:nbn:se:mdh:diva-66188 (URN)978-91-7485-637-8 (ISBN)
Presentation
2024-04-22, Milos, Mälardalens University, Västerås, 09:00 (English)
Opponent
Supervisors
Funder
Swedish Energy Agency, 51592–1
Available from: 2024-03-08 Created: 2024-03-08 Last updated: 2024-04-01Bibliographically approved

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Dong, BeibeiSkvaril, JanThorin, EvaLi, Hailong

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