https://www.mdu.se/

mdu.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
AI-based Dynamic Modelling for CO2 Capture
Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
Mälardalen University, School of Business, Society and Engineering, Future Energy Center.ORCID iD: 0000-0002-6279-4446
2023 (English)In: Energy Proceedings, 2023, Vol. 37Conference paper, Published paper (Refereed)
Abstract [en]

Integrating CO2 capture with biomass/waste fired combined heat and power plants (CHPs) is a promising method to achieve negative emission. However, the use of versatile biomass/waste and dynamic operation of CHPs result in big fluctuations in the flue gas (FG) and heat input to CO2 capture. Dynamic modelling is essential to investigate the interactions between key process parameters in producing the dynamic response of the CO2 capture process. In order to facilitate developing robust control strategies for flexible operation in CO2 capture plants and optimizing the operation of CO2 capture plants, artificial intelligence (AI) models are superior to mechanical models due to the easy implementation into the control and optimization. This paper aims to develop an AI model, Informer, to predict the dynamic responses of MEA based CO2 capture performance from waste-fired CHP plants. Dynamic modelling was first developed in Aspen HYSYS software and validated against the reference. The operation data from the simulated CO2 capture process was then used to develop and verify Informer. The following variables were employed as inputs: inlet flue gas flow rate, CO2 concentration in inlet flue gas, lean solvent flow rate, heat input to CO2 capture. It was found that Informer could predict CO2 capture rate and energy consumption with the mean absolute percentage error of 6.2% and 2.7% respectively.

Place, publisher, year, edition, pages
2023. Vol. 37
Series
Energy Proceedings, ISSN 2004-2965
Keywords [en]
artificial intelligence (AI), dynamic modelling, bioenergy with carbon capture and storage (BECCS), combined heat and power (CHP) plants, energy consumption
National Category
Environmental Engineering Energy Systems
Identifiers
URN: urn:nbn:se:mdh:diva-66187DOI: 10.46855/energy-proceedings-10770Scopus ID: 2-s2.0-85190650594OAI: oai:DiVA.org:mdh-66187DiVA, id: diva2:1843263
Conference
The International Conference on Energy, Ecology and Environment
Funder
Swedish Energy Agency, 51592-1Available from: 2024-03-08 Created: 2024-03-08 Last updated: 2024-11-28Bibliographically 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

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records

Dong, BeibeiShi, XiaodanLi, Hailong

Search in DiVA

By author/editor
Dong, BeibeiShi, XiaodanLi, Hailong
By organisation
Future Energy Center
Environmental EngineeringEnergy Systems

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 176 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf