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Auto-encoder-extreme learning machine model for boiler NOx emission concentration prediction

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  • Tang, Zhenhao
  • Wang, Shikui
  • Chai, Xiangying
  • Cao, Shengxian
  • Ouyang, Tinghui
  • Li, Yang

Abstract

An automatic encoder (AE) extreme learning machine (ELM)-AE-ELM model is proposed to predict the NOx emission concentration based on the combination of mutual information algorithm (MI), AE, and ELM. First, the importance of practical variables is computed by the MI algorithm, and the mechanism is analyzed to determine the variables related to the NOx emission concentration. Then, the time delay correlations between the selected variables and NOx emission concentration are further analyzed to reconstruct the modeling data. Subsequently, the AE is applied to extract hidden features within the input variables. Finally, an ELM algorithm establishes the relationship between the NOx emission concentration and deep features. The experimental results on practical data indicate that the proposed model shows promising performance compared to state-of-art models.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:256:y:2022:i:c:s0360544222014554
    DOI: 10.1016/j.energy.2022.124552
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    References listed on IDEAS

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    Cited by:

    1. 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).
    2. Han, Zhezhe & Tang, Xiaoyu & Xie, Yue & Liang, Ruiyu & Bao, Yongqiang, 2024. "Prediction of heavy-oil combustion emissions with a semi-supervised learning model considering variable operation conditions," Energy, Elsevier, vol. 288(C).
    3. Zhu, Yukun & Yu, Cong & Fan, Wei & Yu, Haiquan & Jin, Wei & Chen, Shuo & Liu, Xia, 2023. "A novel NOx emission prediction model for multimodal operational utility boilers considering local features and prior knowledge," Energy, Elsevier, vol. 280(C).
    4. Wen, Xiaoqiang & Li, Kaichuang & Wang, Jianguo, 2023. "NOx emission predicting for coal-fired boilers based on ensemble learning methods and optimized base learners," Energy, Elsevier, vol. 264(C).
    5. Yu, Jianxi & Petersen, Nils & Liu, Pei & Li, Zheng & Wirsum, Manfred, 2022. "Hybrid modelling and simulation of thermal systems of in-service power plants for digital twin development," Energy, Elsevier, vol. 260(C).
    6. Beichen Zhao & Binshan Ju & Chaoxiang Wang, 2023. "Initial-Productivity Prediction Method of Oil Wells for Low-Permeability Reservoirs Based on PSO-ELM Algorithm," Energies, MDPI, vol. 16(11), pages 1-17, June.

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