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Modeling and reduction of NOX emissions for a 700 MW coal-fired boiler with the advanced machine learning method

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  • Tan, Peng
  • Xia, Ji
  • Zhang, Cheng
  • Fang, Qingyan
  • Chen, Gang

Abstract

This paper focuses on modeling and reducing NOX emissions for a coal-fired boilers with advanced machine learning approaches. The novel ELM (extreme learning machine) model was introduced to model the correlation between operational parameters and NOX emissions of the boiler. Approximately ten days of real data from the SIS (supervisory information system) of a 700 MW coal-fired power plant were acquired to train and verify the ELM-based NOX model. Based on the NOX model, HS (harmony search) algorithm was then employed to optimize the operational parameters to finally realize NOX emission reduction. The modeling results indicated that the ELM model was more precise and faster in modeling NOX emissions than the popular artificial neural network and support vector regression. The searching process of HS was convergent and consumed only 0.7 s of CPU (Central Processing Unit) time on a personal computer. 16.5% and 19.3% NOX emission reductions for the two selected cases were achieved according to the simulation result. Additionally, the simulation result was experimentally justified, which demonstrated that the experimental results corresponded well with the computational: the experimental NOX reduction percentages were 14.8% and 15.7%, respectively. The proposed integrated method was capable of providing desired and feasible solutions within 1 s.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:energy:v:94:y:2016:i:c:p:672-679
    DOI: 10.1016/j.energy.2015.11.020
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    References listed on IDEAS

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    Cited by:

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    3. Tan, Peng & He, Biao & Zhang, Cheng & Rao, Debei & Li, Shengnan & Fang, Qingyan & Chen, Gang, 2019. "Dynamic modeling of NOX emission in a 660 MW coal-fired boiler with long short-term memory," Energy, Elsevier, vol. 176(C), pages 429-436.
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    5. 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).
    6. Wang, Zhi & Peng, Xianyong & Zhou, Huaichun & Cao, Shengxian & Huang, Wenbo & Yan, Weijie & Li, Kuangyu & Fan, Siyuan, 2024. "A dynamic modeling method using channel-selection convolutional neural network: A case study of NOx emission," Energy, Elsevier, vol. 290(C).
    7. 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).
    8. Hyuk Choi & Ju-Hong Lee & Ji-Hoon Yu & Un-Chul Moon & Mi-Jong Kim & Kwang Y. Lee, 2023. "One-Step Ahead Control Using Online Interpolated Transfer Function for Supplementary Control of Air-Fuel Ratio in Thermal Power Plants," Energies, MDPI, vol. 16(21), pages 1-18, November.
    9. 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.
    10. 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).
    11. 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.
    12. Yang, Guotian & Wang, Yingnan & Li, Xinli, 2020. "Prediction of the NOx emissions from thermal power plant using long-short term memory neural network," Energy, Elsevier, vol. 192(C).
    13. 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).
    14. Fan, Yuchen & Liu, Xin & Zhang, Chaoqun & Li, Chi & Li, Xinying & Wang, Heyang, 2024. "Dynamic prediction of boiler NOx emission with graph convolutional gated recurrent unit model optimized by genetic algorithm," Energy, Elsevier, vol. 294(C).
    15. Xinying Xu & Qi Chen & Mifeng Ren & Lan Cheng & Jun Xie, 2019. "Combustion Optimization for Coal Fired Power Plant Boilers Based on Improved Distributed ELM and Distributed PSO," Energies, MDPI, vol. 12(6), pages 1-24, March.
    16. Nan Li & You Lv & Yong Hu, 2022. "Prediction of NOx Emissions from a Coal-Fired Boiler Based on Convolutional Neural Networks with a Channel Attention Mechanism," Energies, MDPI, vol. 16(1), pages 1-11, December.

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