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A novel least squares support vector machine ensemble model for NOx emission prediction of a coal-fired boiler

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  • Lv, You
  • Liu, Jizhen
  • Yang, Tingting
  • Zeng, Deliang

Abstract

Real operation data of power plants are inclined to be concentrated in some local areas because of the operators’ habits and control system design. In this paper, a novel least squares support vector machine (LSSVM)-based ensemble learning paradigm is proposed to predict NOx emission of a coal-fired boiler using real operation data. In view of the plant data characteristics, a soft fuzzy c-means cluster algorithm is proposed to decompose the original data and guarantee the diversity of individual learners. Subsequently the base LSSVM is trained in each individual subset to solve the subtask. Finally, partial least squares (PLS) is applied as the combination strategy to eliminate the collinear and redundant information of the base learners. Considering that the fuzzy membership also has an effect on the ensemble output, the membership degree is added as one of the variables of the combiner. The single LSSVM and other ensemble models using different decomposition and combination strategies are also established to make a comparison. The result shows that the new soft FCM-LSSVM-PLS ensemble method can predict NOx emission accurately. Besides, because of the divide and conquer frame, the total time consumed in the searching the parameters and training also decreases evidently.

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  • 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.
    • Handle: RePEc:eee:energy:v:55:y:2013:i:c:p:319-329
    DOI: 10.1016/j.energy.2013.02.062
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    1. Kalogirou, Soteris A., 2000. "Applications of artificial neural-networks for energy systems," Applied Energy, Elsevier, vol. 67(1-2), pages 17-35, September.
    2. Smrekar, J. & Assadi, M. & Fast, M. & Kuštrin, I. & De, S., 2009. "Development of artificial neural network model for a coal-fired boiler using real plant data," Energy, Elsevier, vol. 34(2), pages 144-152.
    3. Fast, M. & Palmé, T., 2010. "Application of artificial neural networks to the condition monitoring and diagnosis of a combined heat and power plant," Energy, Elsevier, vol. 35(2), pages 1114-1120.
    4. Wang, Junchao & Fan, Weidong & Li, Yu & Xiao, Meng & Wang, Kang & Ren, Peng, 2012. "The effect of air staged combustion on NOx emissions in dried lignite combustion," Energy, Elsevier, vol. 37(1), pages 725-736.
    5. 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.
    6. Tang, Ling & Yu, Lean & Wang, Shuai & Li, Jianping & Wang, Shouyang, 2012. "A novel hybrid ensemble learning paradigm for nuclear energy consumption forecasting," Applied Energy, Elsevier, vol. 93(C), pages 432-443.
    7. 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.
    8. Kusiak, Andrew & Li, Mingyang & Zhang, Zijun, 2010. "A data-driven approach for steam load prediction in buildings," Applied Energy, Elsevier, vol. 87(3), pages 925-933, March.
    9. Kavaklioglu, Kadir, 2011. "Modeling and prediction of Turkey's electricity consumption using Support Vector Regression," Applied Energy, Elsevier, vol. 88(1), pages 368-375, January.
    10. Wang, Shuai & Yu, Lean & Tang, Ling & Wang, Shouyang, 2011. "A novel seasonal decomposition based least squares support vector regression ensemble learning approach for hydropower consumption forecasting in China," Energy, Elsevier, vol. 36(11), pages 6542-6554.
    11. 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.
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    16. Yang Du & Ke Yan & Zixiao Ren & Weidong Xiao, 2018. "Designing Localized MPPT for PV Systems Using Fuzzy-Weighted Extreme Learning Machine," Energies, MDPI, vol. 11(10), pages 1-10, October.
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    18. 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).
    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. Xu, Wentao & Huang, Yaji & Song, Siheng & Chen, Yuzhu & Cao, Gehan & Yu, Mengzhu & Chen, Bo & Zhang, Rongchu & Liu, Yuqing & Zou, Yiran, 2023. "A new online optimization method for boiler combustion system based on the data-driven technique and the case-based reasoning principle," Energy, Elsevier, vol. 263(PE).
    21. Peng, Gongzhuang & Wang, Hongwei & Song, Xiao & Zhang, Heming, 2017. "Intelligent management of coal stockpiles using improved grey spontaneous combustion forecasting models," Energy, Elsevier, vol. 132(C), pages 269-279.

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