IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i6p1036-d214636.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/6/1036/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/6/1036/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. 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.
    3. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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).
    2. 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.
    3. 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).
    4. Sabarathinam Srinivasan & Suresh Kumarasamy & Zacharias E. Andreadakis & Pedro G. Lind, 2023. "Artificial Intelligence and Mathematical Models of Power Grids Driven by Renewable Energy Sources: A Survey," Energies, MDPI, vol. 16(14), pages 1-56, July.
    5. 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).
    6. 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).
    7. Li, Shicheng & Ma, Suxia & Wang, Fang, 2023. "A combined NOx emission prediction model based on semi-empirical model and black box models," Energy, Elsevier, vol. 264(C).
    8. 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).
    9. Li, Qingwei & Wu, Jiang & Wei, Hongqi, 2018. "Reduction of elemental mercury in coal-fired boiler flue gas with computational intelligence approach," Energy, Elsevier, vol. 160(C), pages 753-762.
    10. 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.
    11. 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.
    12. 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.
    13. 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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:12:y:2019:i:6:p:1036-:d:214636. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.