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Dynamic NOX emission concentration prediction based on the combined feature selection algorithm and deep neural network

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  • Tang, Zhenhao
  • Wang, Shikui
  • Li, Yue

Abstract

The development of an accurate nitrogen oxide (NOx) prediction model is difficult because of multiple parameters, strong coupling, and long delay time of selective catalytic reduction (SCR) systems. In this study, a modeling scheme based on combined feature selection, the JAYA optimization algorithm, and deep neural network (DNN) was developed. First, historical operating data preprocessing, including eliminating outlier points and normalization, was completed. Next, a combined feature selection algorithm based on classification and regression tree, random forest, extreme gradient boosting, and maximal information coefficient (MIC) was developed to select critical input variables. Subsequently, the delay time of the input variables was calculated based on the MIC and JAYA algorithm, and the modeling data were reconstructed. Finally, the real-time dynamic prediction of the SCR outlet NOx concentration was realized based on the DNN model. Experimental results based on operation data of 1000 MW ultra-supercritical boiler revealed that the prediction errors of the established models were less than 5%. Thus, could accurately predict the NOx emission concentration at the outlet of SCR system.

Suggested Citation

  • Tang, Zhenhao & Wang, Shikui & Li, Yue, 2024. "Dynamic NOX emission concentration prediction based on the combined feature selection algorithm and deep neural network," Energy, Elsevier, vol. 292(C).
    • Handle: RePEc:eee:energy:v:292:y:2024:i:c:s0360544224003803
    DOI: 10.1016/j.energy.2024.130608
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    References listed on IDEAS

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    1. Fan, Cheng & Xiao, Fu & Zhao, Yang, 2017. "A short-term building cooling load prediction method using deep learning algorithms," Applied Energy, Elsevier, vol. 195(C), pages 222-233.
    2. Sharma, Mithun J. & Yu, Song Jin, 2015. "Stepwise regression data envelopment analysis for variable reduction," Applied Mathematics and Computation, Elsevier, vol. 253(C), pages 126-134.
    3. 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).
    4. Lv, You & Hong, Feng & Yang, Tingting & Fang, Fang & Liu, Jizhen, 2017. "A dynamic model for the bed temperature prediction of circulating fluidized bed boilers based on least squares support vector machine with real operational data," Energy, Elsevier, vol. 124(C), pages 284-294.
    5. Zheng, Wei & Wang, Chao & Yang, Yajun & Zhang, Yongfei, 2020. "Multi-objective combustion optimization based on data-driven hybrid strategy," Energy, Elsevier, vol. 191(C).
    6. Salcedo-Sanz, S. & Cornejo-Bueno, L. & Prieto, L. & Paredes, D. & García-Herrera, R., 2018. "Feature selection in machine learning prediction systems for renewable energy applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 90(C), pages 728-741.
    7. Lv, You & Lv, Xuguang & Fang, Fang & Yang, Tingting & Romero, Carlos E., 2020. "Adaptive selective catalytic reduction model development using typical operating data in coal-fired power plants," Energy, Elsevier, vol. 192(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. Hong, Feng & Long, Dongteng & Chen, Jiyu & Gao, Mingming, 2020. "Modeling for the bed temperature 2D-interval prediction of CFB boilers based on long-short term memory network," Energy, Elsevier, vol. 194(C).
    Full references (including those not matched with items on IDEAS)

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