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Prediction of NOx emission concentration from coal-fired power plant based on joint knowledge and data driven

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  • Wu, Zheng
  • Zhang, Yue
  • Dong, Ze

Abstract

Accurate NOx concentration prediction is of great significance for the pollutant emission control and safe operation of coal-fired power plants. The global properties of the research object cannot be adequately described by a single data driven model, which hinders generalization performance. We propose a NOx emission concentration prediction method based on joint knowledge and data driven. First, we introduce a knowledge driven combined feature selection method to provide a global feature basis for data driven modeling. Second, we enable adaptive decomposition of the variational modal decomposition (VMD) using the modal energy difference and sample entropy. The method can extract deep time-frequency information in nonlinear and non-smooth features. Finally, we use the Informer combined with an adaptive time series segmentation method to predict NOx concentration. The experimental results indicate that the proposed method predicts the NOx concentration better than several comparative models.

Suggested Citation

  • Wu, Zheng & Zhang, Yue & Dong, Ze, 2023. "Prediction of NOx emission concentration from coal-fired power plant based on joint knowledge and data driven," Energy, Elsevier, vol. 271(C).
    • Handle: RePEc:eee:energy:v:271:y:2023:i:c:s0360544223004383
    DOI: 10.1016/j.energy.2023.127044
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    References listed on IDEAS

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    1. Zhang, Jinliang & Tan, Zhongfu & Wei, Yiming, 2020. "An adaptive hybrid model for short term electricity price forecasting," Applied Energy, Elsevier, vol. 258(C).
    2. Tang, Zhenhao & Wang, Shikui & Chai, Xiangying & Cao, Shengxian & Ouyang, Tinghui & Li, Yang, 2022. "Auto-encoder-extreme learning machine model for boiler NOx emission concentration prediction," Energy, Elsevier, vol. 256(C).
    3. Gong, Mingju & Zhao, Yin & Sun, Jiawang & Han, Cuitian & Sun, Guannan & Yan, Bo, 2022. "Load forecasting of district heating system based on Informer," Energy, Elsevier, vol. 253(C).
    4. Tuttle, Jacob F. & Blackburn, Landen D. & Andersson, Klas & Powell, Kody M., 2021. "A systematic comparison of machine learning methods for modeling of dynamic processes applied to combustion emission rate modeling," Applied Energy, Elsevier, vol. 292(C).
    5. Sun, Zexian & Zhao, Mingyu & Zhao, Guohong, 2022. "Hybrid model based on VMD decomposition, clustering analysis, long short memory network, ensemble learning and error complementation for short-term wind speed forecasting assisted by Flink platform," Energy, Elsevier, vol. 261(PB).
    6. Jiang, Yu & Lee, Byoung-Hwa & Oh, Dong-Hun & Jeon, Chung-Hwan, 2022. "Influence of various air-staging on combustion and NOX emission characteristics in a tangentially fired boiler under the 50% load condition," Energy, Elsevier, vol. 244(PB).
    7. Zhang, Jinliang & Wei, Yiming & Tan, Zhongfu, 2020. "An adaptive hybrid model for short term wind speed forecasting," Energy, Elsevier, vol. 190(C).
    8. Jin, Yi & Scherer, Laura & Sutanudjaja, Edwin H. & Tukker, Arnold & Behrens, Paul, 2022. "Climate change and CCS increase the water vulnerability of China's thermoelectric power fleet," Energy, Elsevier, vol. 245(C).
    9. Neeraj Bokde & Andrés Feijóo & Daniel Villanueva & Kishore Kulat, 2019. "A Review on Hybrid Empirical Mode Decomposition Models for Wind Speed and Wind Power Prediction," Energies, MDPI, vol. 12(2), pages 1-42, January.
    10. Liu, Jiangyan & Li, Guannan & Liu, Bin & Li, Kuining & Chen, Huanxin, 2019. "Knowledge discovery of data-driven-based fault diagnostics for building energy systems: A case study of the building variable refrigerant flow system," Energy, Elsevier, vol. 174(C), pages 873-885.
    11. Yin, Jiateng & Ren, Xianliang & Liu, Ronghui & Tang, Tao & Su, Shuai, 2022. "Quantitative analysis for resilience-based urban rail systems: A hybrid knowledge-based and data-driven approach," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
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    Cited by:

    1. Gao, Wei & Liu, Ming & Yin, Junjie & Zhao, Yongliang & Chen, Weixiong & Yan, Junjie, 2023. "An improved control strategy for a denitrification system using cooperative control of NH3 injection and flue gas temperature for coal-fired power plants," Energy, Elsevier, vol. 282(C).
    2. Tang, Zhenhao & Sui, Mengxuan & Wang, Xu & Xue, Wenyuan & Yang, Yuan & Wang, Zhi & Ouyang, Tinghui, 2024. "Theory-guided deep neural network for boiler 3-D NOx concentration distribution prediction," Energy, Elsevier, vol. 299(C).

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