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Combustion Optimization for Coal Fired Power Plant Boilers Based on Improved Distributed ELM and Distributed PSO

Author

Listed:
  • Xinying Xu

    (College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, China)

  • Qi Chen

    (College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, China)

  • Mifeng Ren

    (College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, China)

  • Lan Cheng

    (College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, China)

  • Jun Xie

    (College of Information and Computer, Taiyuan University of Technology, Taiyuan 030024, China)

Abstract

Increasing the combustion efficiency of power plant boilers and reducing pollutant emissions are important for energy conservation and environmental protection. The power plant boiler combustion process is a complex multi-input/multi-output system, with a high degree of nonlinearity and strong coupling characteristics. It is necessary to optimize the boiler combustion model by means of artificial intelligence methods. However, the traditional intelligent algorithms cannot deal effectively with the massive and high dimensional power station data. In this paper, a distributed combustion optimization method for boilers is proposed. The MapReduce programming framework is used to parallelize the proposed algorithm model and improve its ability to deal with big data. An improved distributed extreme learning machine is used to establish the combustion system model aiming at boiler combustion efficiency and NO x emission. The distributed particle swarm optimization algorithm based on MapReduce is used to optimize the input parameters of boiler combustion model, and weighted coefficient method is used to solve the multi-objective optimization problem (boiler combustion efficiency and NO x emissions). According to the experimental analysis, the results show that the method can optimize the boiler combustion efficiency and NO x emissions by combining different weight coefficients as needed.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:6:p:1036-:d:214636
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    References listed on IDEAS

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    1. 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.
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    Cited by:

    1. 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.
    2. Cui, Ying & Ye, Lin & Yao, Zhongran & Gu, Xiaoyong & Wang, Xinwang, 2024. "Performance optimization of cement calciner based on CFD simulation and machine learning algorithm," Energy, Elsevier, vol. 302(C).
    3. Piotr Duda & Łukasz Felkowski & Adam Zieliński & Andrzej Duda, 2019. "An Analysis of a Reheater Failure and a Proposal to Upgrade the Device Design," Energies, MDPI, vol. 12(12), pages 1-10, June.
    4. Jixuan Wang & Wensheng Liu & Xin Meng & Xiaozhen Liu & Yanfeng Gao & Zuodong Yu & Yakai Bai & Xin Yang, 2020. "Study on the Coupling Effect of a Solar-Coal Unit Thermodynamic System with Carbon Capture," Energies, MDPI, vol. 13(18), pages 1-14, September.

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