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Combustion optimization of ultra supercritical boiler based on artificial intelligence

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  • Shi, Yan
  • Zhong, Wenqi
  • Chen, Xi
  • Yu, A.B.
  • Li, Jie

Abstract

A method for optimizing the combustion in an ultra-supercritical boiler is developed and evaluated in a 660 MWe ultra-supercritical coal fired power plant. In this method, Artificial Neural Networks (ANN) models are established for predicting the boiler operating and emission properties. To enhance the generalization of the ANN models, Computational Fluid Dynamics (CFD) simulation is performed to generate some data as training samples for ANN modeling, together with the historical operating data. The inputs of the ANN models are unit load, coal properties, excess air and air distribution scheme, and the outputs are thermal efficiency and NOx emission. Based on the ANN models, Genetic Algorithm (GA) is used to optimize the air distribution scheme to achieve a higher thermal efficiency and lower NOx emission simultaneously. The predictions of the thermal efficiency and NOx emissions show a good agreement with the plant data, with mean errors of 0.04% and 3.56 mg/Nm3, respectively. The results indicate that the use of CFD data can help generalize the ANN models. The application to a practical plant demonstrates that the proposed approach provides an effective tool for multi-objective optimization of pulverized-coal boiler performance with improved thermal efficiency and NOx emission control.

Suggested Citation

  • Shi, Yan & Zhong, Wenqi & Chen, Xi & Yu, A.B. & Li, Jie, 2019. "Combustion optimization of ultra supercritical boiler based on artificial intelligence," Energy, Elsevier, vol. 170(C), pages 804-817.
    • Handle: RePEc:eee:energy:v:170:y:2019:i:c:p:804-817
    DOI: 10.1016/j.energy.2018.12.172
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    References listed on IDEAS

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    Cited by:

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    9. Lei Han & Lingmei Wang & Hairui Yang & Chengzhen Jia & Enlong Meng & Yushan Liu & Shaoping Yin, 2023. "Optimization of Circulating Fluidized Bed Boiler Combustion Key Control Parameters Based on Machine Learning," Energies, MDPI, vol. 16(15), pages 1-23, July.
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    11. Bo Zhu & Bichen Shang & Xiao Guo & Chao Wu & Xiaoqiang Chen & Lingling Zhao, 2022. "Study on Combustion Characteristics and NOx Formation in 600 MW Coal-Fired Boiler Based on Numerical Simulation," Energies, MDPI, vol. 16(1), pages 1-30, December.
    12. Xu, Wentao & Huang, Yaji & Song, Siheng & Yue, Junfeng & Chen, Bo & Liu, Yuqing & Zou, Yiran, 2023. "A new on-line combustion optimization approach for ultra-supercritical coal-fired boiler to improve boiler efficiency, reduce NOx emission and enhance operating safety," Energy, Elsevier, vol. 282(C).
    13. Chen, Xi & Zhong, Wenqi & Li, Tianyu, 2023. "Fast prediction of temperature and chemical species distributions in pulverized coal boiler using POD reduced-order modeling for CFD," Energy, Elsevier, vol. 276(C).
    14. Chuanpeng Zhu & Pu Huang & Yiguo Li, 2022. "Closed-Loop Combustion Optimization Based on Dynamic and Adaptive Models with Application to a Coal-Fired Boiler," Energies, MDPI, vol. 15(14), pages 1-16, July.
    15. Laubscher, Ryno, 2019. "Time-series forecasting of coal-fired power plant reheater metal temperatures using encoder-decoder recurrent neural networks," Energy, Elsevier, vol. 189(C).
    16. Zhou, Jian & Zhang, Wei, 2023. "Coal consumption prediction in thermal power units: A feature construction and selection method," Energy, Elsevier, vol. 273(C).
    17. 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).
    18. Xu, Qilong & Wang, Shuai & Luo, Kun & Mu, Yanfei & Pan, Lu & Fan, Jianren, 2023. "Process modelling and optimization of a 250 MW IGCC system: Model setup, validation, and preliminary predictions," Energy, Elsevier, vol. 272(C).

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