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Modeling and optimization of the NOx emission characteristics of a tangentially fired boiler with artificial neural networks

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  • Zhou, Hao
  • Cen, Kefa
  • Fan, Jianren

Abstract

The present work introduces an approach to predict the nitrogen oxides (NOx) emission characteristics of a large capacity pulverized coal fired boiler with artificial neural networks (ANN). The NOx emission and carbon burnout characteristics were investigated through parametric field experiments. The effects of over-fire-air (OFA) flow rates, coal properties, boiler load, air distribution scheme and nozzle tilt were studied. On the basis of the experimental results, an ANN was used to model the NOx emission characteristics and the carbon burnout characteristics. Compared with the other modeling techniques, such as computational fluid dynamics (CFD) approach, the ANN approach is more convenient and direct, and can achieve good prediction effects under various operating conditions. A modified genetic algorithm (GA) using the micro-GA technique was employed to perform a search to determine the optimum solution of the ANN model, determining the optimal setpoints for the current operating conditions, which can suggest operators’ correct actions to decrease NOx emission.

Suggested Citation

  • Zhou, Hao & Cen, Kefa & Fan, Jianren, 2004. "Modeling and optimization of the NOx emission characteristics of a tangentially fired boiler with artificial neural networks," Energy, Elsevier, vol. 29(1), pages 167-183.
    • Handle: RePEc:eee:energy:v:29:y:2004:i:1:p:167-183
    DOI: 10.1016/j.energy.2003.08.004
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    Cited by:

    1. Fan, Weidong & Lin, Zhengchun & Li, Youyi & Zhang, Mingchuan, 2010. "Experimental flow field characteristics of OFA for large-angle counter flow of fuel-rich jet combustion technology," Applied Energy, Elsevier, vol. 87(8), pages 2737-2745, August.
    2. 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).
    3. Yu, Youhong & Chen, Lingen & Sun, Fengrui & Wu, Chih, 2007. "Neural-network based analysis and prediction of a compressor's characteristic performance map," Applied Energy, Elsevier, vol. 84(1), pages 48-55, January.
    4. Liukkonen, Mika & Hälikkä, Eero & Hiltunen, Teri & Hiltunen, Yrjö, 2012. "Dynamic soft sensors for NOx emissions in a circulating fluidized bed boiler," Applied Energy, Elsevier, vol. 97(C), pages 483-490.
    5. Dios, M. & Souto, J.A. & Casares, J.J., 2013. "Experimental development of CO2, SO2 and NOx emission factors for mixed lignite and subbituminous coal-fired power plant," Energy, Elsevier, vol. 53(C), pages 40-51.
    6. 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).
    7. 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.
    8. Rossi, Francesco & Velázquez, David, 2015. "A methodology for energy savings verification in industry with application for a CHP (combined heat and power) plant," Energy, Elsevier, vol. 89(C), pages 528-544.
    9. 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).
    10. Tan, Houzhang & Niu, Yanqing & Wang, Xuebin & Xu, Tongmo & Hui, Shien, 2011. "Study of optimal pulverized coal concentration in a four-wall tangentially fired furnace," Applied Energy, Elsevier, vol. 88(4), pages 1164-1168, April.
    11. Liukkonen, M. & Heikkinen, M. & Hiltunen, T. & Hälikkä, E. & Kuivalainen, R. & Hiltunen, Y., 2011. "Artificial neural networks for analysis of process states in fluidized bed combustion," Energy, Elsevier, vol. 36(1), pages 339-347.
    12. 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.
    13. Smrekar, J. & Potočnik, P. & Senegačnik, A., 2013. "Multi-step-ahead prediction of NOx emissions for a coal-based boiler," Applied Energy, Elsevier, vol. 106(C), pages 89-99.
    14. Xiao Wu & Jiong Shen & Yiguo Li & Kwang Y. Lee, 2015. "Steam power plant configuration, design, and control," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 4(6), pages 537-563, November.
    15. 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.
    16. Lv, You & Liu, Jizhen & Yang, Tingting & Zeng, Deliang, 2013. "A novel least squares support vector machine ensemble model for NOx emission prediction of a coal-fired boiler," Energy, Elsevier, vol. 55(C), pages 319-329.
    17. Tang, Yuting & Ma, Xiaoqian & Lai, Zhiyi & Zhou, Daoxi & Lin, Hai & Chen, Yong, 2012. "NOx and SO2 emissions from municipal solid waste (MSW) combustion in CO2/O2 atmosphere," Energy, Elsevier, vol. 40(1), pages 300-306.
    18. Wei, Zhongbao & Li, Xiaolu & Xu, Lijun & Cheng, Yanting, 2013. "Comparative study of computational intelligence approaches for NOx reduction of coal-fired boiler," Energy, Elsevier, vol. 55(C), pages 683-692.
    19. 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.
    20. Bekat, Tugce & Erdogan, Muharrem & Inal, Fikret & Genc, Ayten, 2012. "Prediction of the bottom ash formed in a coal-fired power plant using artificial neural networks," Energy, Elsevier, vol. 45(1), pages 882-887.
    21. Ding, Xiaosong & Feng, Chong & Yu, Peiling & Li, Kaiwen & Chen, Xi, 2023. "Gradient boosting decision tree in the prediction of NOx emission of waste incineration," Energy, Elsevier, vol. 264(C).
    22. Mikulandrić, Robert & Lončar, Dražen & Cvetinović, Dejan & Spiridon, Gabriel, 2013. "Improvement of existing coal fired thermal power plants performance by control systems modifications," Energy, Elsevier, vol. 57(C), pages 55-65.
    23. 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.
    24. Azimi, Seyyed Shahabeddin & Namazi, Mohammad Hosain, 2015. "Modeling of combustion of gas oil and natural gas in a furnace: Comparison of combustion characteristics," Energy, Elsevier, vol. 93(P1), pages 458-465.

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