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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

    as
    1. Yu, Bolin & Fang, Debin & Meng, Jingxuan, 2021. "Analysis of the generation efficiency of disaggregated renewable energy and its spatial heterogeneity influencing factors: A case study of China," Energy, Elsevier, vol. 234(C).
    2. Xie, Peiran & Gao, Mingming & Zhang, Hongfu & Niu, Yuguang & Wang, Xiaowen, 2020. "Dynamic modeling for NOx emission sequence prediction of SCR system outlet based on sequence to sequence long short-term memory network," Energy, Elsevier, vol. 190(C).
    3. Li, Qingwei & Yao, Guihuan, 2017. "Improved coal combustion optimization model based on load balance and coal qualities," Energy, Elsevier, vol. 132(C), pages 204-212.
    4. Tang, Zhenhao & Zhao, Gengnan & Ouyang, Tinghui, 2021. "Two-phase deep learning model for short-term wind direction forecasting," Renewable Energy, Elsevier, vol. 173(C), pages 1005-1016.
    5. Tang, Zhenhao & Zhang, Zijun, 2019. "The multi-objective optimization of combustion system operations based on deep data-driven models," Energy, Elsevier, vol. 182(C), pages 37-47.
    6. Tan, Peng & Xia, Ji & Zhang, Cheng & Fang, Qingyan & Chen, Gang, 2016. "Modeling and reduction of NOX emissions for a 700 MW coal-fired boiler with the advanced machine learning method," Energy, Elsevier, vol. 94(C), pages 672-679.
    7. Miura, Ayako & Tomoeda, Akiyasu & Nishinari, Katsuhiro, 2020. "Formularization of entropy and anticipation of metastable states using mutual information in one-dimensional traffic flow," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 560(C).
    8. Song, Malin & Wang, Jianlin, 2018. "Environmental efficiency evaluation of thermal power generation in China based on a slack-based endogenous directional distance function model," Energy, Elsevier, vol. 161(C), pages 325-336.
    9. Wang, Chunlin & Liu, Yang & Zheng, Song & Jiang, Aipeng, 2018. "Optimizing combustion of coal fired boilers for reducing NOx emission using Gaussian Process," Energy, Elsevier, vol. 153(C), pages 149-158.
    10. Pang, Zhihong & Niu, Fuxin & O’Neill, Zheng, 2020. "Solar radiation prediction using recurrent neural network and artificial neural network: A case study with comparisons," Renewable Energy, Elsevier, vol. 156(C), pages 279-289.
    11. 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).
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    Cited by:

    1. 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).
    2. 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.
    3. 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).
    4. 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).
    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).

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