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
Multivariate time series prediction for CO2 concentration and flowrate of flue gas from biomass-fired power plants
College of artificial intelligence, China University of Petroleum-Beijing, Fuxue Road No.18, Changping District, Beijing 102249, China.ORCID iD: 0009-0001-5789-1938
Mälardalen University, School of Business, Society and Engineering, Future Energy Center.ORCID iD: 0000-0001-5125-1860
Mälardalen University, School of Business, Society and Engineering, Future Energy Center.ORCID iD: 0000-0002-3907-1987
Mälardalen University, School of Business, Society and Engineering, Future Energy Center.ORCID iD: 0000-0002-5341-3656
Show others and affiliations
2024 (English)In: Fuel, ISSN 0016-2361, E-ISSN 1873-7153, Vol. 359, article id 130344Article in journal (Refereed) Published
Abstract [en]

Integrating CO2 capture with biomass-fired combined heat and power (bio-CHP) plants is a promising method to achieve negative emissions. However, the use of versatile biomass, including waste, and the dynamic operation of bio-CHP plants leads to large fluctuations in the flowrate and CO2 concentration of the flue gas (FG), which further affect the operation of post-combustion CO2 capture. To optimize the dynamic operation of CO2 capture, a reliable model to predict the FG flowrate and CO2 concentration in real time is essential. In this paper, a data-driven model based on the Transformer architecture is developed. The model validation shows that the root mean squared error (RMSE), mean absolute percentage error (MAPE), and Pearson correlation coefficient (PPMCC) of Transformer are 0.3553, 0.0189, and 0.8099 respectively for the prediction of FG flowrate; and 13.137, 0.0318, and 0.8336 respectively for the prediction of CO2 concentration. The potential impact of various meteorological parameters on model accuracy is also assessed by analyzing the Shapley value. It is found that temperature and direct horizontal irradiance (DHI) are the most important factors, which should be selected as input features. In addition, using the near-infrared (NIR) spectral data as input features is also found to be an effective way to improve the prediction accuracy. It can reduce RMSE and MAPE for CO2 concentration from 0.2982 to 0.2887 and 0.0158 to 0.0157 respectively, and RMSE and MAPE for FG flowrate from 4.9854 to 4.7537 and 0.0141 to 0.0121 respectively. The Transformer model is also compared to other models, including long short-term memory network (LSTM) and artificial neural network (ANN), which results show that the Transformer model is superior in predicting complex dynamic patterns and nonlinear relationships.

Place, publisher, year, edition, pages
2024. Vol. 359, article id 130344
Keywords [en]
Biomass fired combined heat and power plants, CO2 capture, Flue gas flowrate, CO2 concentration, Transformer model, Deep learning
National Category
Energy Engineering
Research subject
Energy- and Environmental Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-64937DOI: 10.1016/j.fuel.2023.130344ISI: 001125955300001Scopus ID: 2-s2.0-85178313710OAI: oai:DiVA.org:mdh-64937DiVA, id: diva2:1817334
Projects
AI-BECCS
Funder
Swedish Energy AgencySwedish Energy AgencyAvailable from: 2023-12-05 Created: 2023-12-05 Last updated: 2024-01-24Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records

Shi, XiaodanDong, BeibeiSkvaril, JanLi, Hailong

Search in DiVA

By author/editor
Pan, ShiyuanShi, XiaodanDong, BeibeiSkvaril, JanZhang, HaoranLi, Hailong
By organisation
Future Energy Center
In the same journal
Fuel
Energy Engineering

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 122 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