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Multivariate time s...
Multivariate time series prediction for CO2 concentration and flowrate of flue gas from biomass-fired power plants
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- Pan, Shiyuan (författare)
- College of artificial intelligence, China University of Petroleum-Beijing, Fuxue Road No.18, Changping District, Beijing 102249, China
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- Shi, Xiaodan (författare)
- Mälardalens universitet,Framtidens energi
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- Dong, Beibei (författare)
- Mälardalens universitet,Framtidens energi
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- Skvaril, Jan, Dr. 1982- (författare)
- Mälardalens universitet,Framtidens energi
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- Zhang, Haoran (författare)
- School of Urban Planning and Design, Peking University, No.2199 Lishui Road, Nanshan District, Shenzhen, Guangdong, 518055, China
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- Liang, Yongtu (författare)
- Beijing Key Laboratory of Urban oil and Gas Distribution Technology, China University of Petroleum-Beijing, Fuxue Road No.18, Changping District, Beijing 102249, China
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- Li, Hailong, 1976- (författare)
- Mälardalens universitet,Framtidens energi
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College of artificial intelligence, China University of Petroleum-Beijing, Fuxue Road No18, Changping District, Beijing 102249, China Framtidens energi (creator_code:org_t)
- 2024
- 2024
- Engelska.
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Ingår i: Fuel. - 0016-2361 .- 1873-7153. ; 359
- Relaterad länk:
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https://doi.org/10.1...
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Maskinteknik -- Energiteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Mechanical Engineering -- Energy Engineering (hsv//eng)
Nyckelord
- Biomass fired combined heat and power plants
- CO2 capture
- Flue gas flowrate
- CO2 concentration
- Transformer model
- Deep learning
- Energy- and Environmental Engineering
- energi- och miljöteknik
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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