IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i21p7411-d1273251.html
   My bibliography  Save this article

One-Step Ahead Control Using Online Interpolated Transfer Function for Supplementary Control of Air-Fuel Ratio in Thermal Power Plants

Author

Listed:
  • Hyuk Choi

    (School of Electrical and Electronics Engineering, Chung-Ang University, Seoul 06974, Republic of Korea)

  • Ju-Hong Lee

    (School of Electrical and Electronics Engineering, Chung-Ang University, Seoul 06974, Republic of Korea)

  • Ji-Hoon Yu

    (School of Electrical and Electronics Engineering, Chung-Ang University, Seoul 06974, Republic of Korea)

  • Un-Chul Moon

    (School of Electrical and Electronics Engineering, Chung-Ang University, Seoul 06974, Republic of Korea)

  • Mi-Jong Kim

    (Kepco Kps Overseas Maintenance Service Center, 211 Munhwa-ro, Naju-si 58326, Jeollanam-do, Republic of Korea)

  • Kwang Y. Lee

    (Department of Electrical and Computer Engineering, Baylor University, Waco, TX 76798-7356, USA)

Abstract

Recently, the environmental problem has become a global issue. The air to fuel ratio (AFR) in the combustion of thermal power plants directly influences pollutants and thermal efficiency. A research result was published showing that the AFR control performance of thermal power plants can be improved through supplementary control using dynamic matrix control (DMC). However, online optimization of DMC needs an extra computer server in implementation. This paper proposes a practical AFR control with one-step ahead control which does not use online optimization and can be implemented directly in existing distributed control system (DCS) of thermal power plants. Closed-loop transfer function models at three operating points are independently developed offline. Then, an online transfer function using interpolation of offline models is applied at each sampling step. A simple one-step ahead control with online transfer function is applied as a supplementary control of AFR. Simulations with two different type power plants, a 600 MW oil-fired drum-type power plant and a 1000 MW ultra supercritical (USC) coal-fired once-through type power plant, are performed to show the effectiveness of the proposed control structure. Simulation results show that the proposed supplementary control can effectively improve the conventional AFR control performance of power plants.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:21:p:7411-:d:1273251
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/21/7411/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/16/21/7411/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Hyuk Choi & Yeongseok Choi & Un-Chul Moon & Kwang Y. Lee, 2023. "Supplementary Control of Conventional Coordinated Control for 1000 MW Ultra-Supercritical Thermal Power Plant Using One-Step Ahead Control," Energies, MDPI, vol. 16(17), pages 1-15, August.
    3. 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.
    4. Yang, Tingting & Liu, Ziyuan & Zeng, Deliang & Zhu, Yansong, 2023. "Simulation and evaluation of flexible enhancement of thermal power unit coupled with flywheel energy storage array," Energy, Elsevier, vol. 281(C).
    5. Lei Zhang & Jiaqing Ma & Qinmu Wu & Zhiqin He & Tao Qin & Changsheng Chen, 2023. "Research on PMSM Speed Performance Based on Fractional Order Adaptive Fuzzy Backstepping Control," Energies, MDPI, vol. 16(19), pages 1-12, October.
    Full references (including those not matched with items on IDEAS)

    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. 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).
    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. 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).
    4. 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).
    5. 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.
    6. 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).
    7. 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.
    8. Li, Ruilian & Zeng, Deliang & Li, Tingting & Ti, Baozhong & Hu, Yong, 2023. "Real-time prediction of SO2 emission concentration under wide range of variable loads by convolution-LSTM VE-transformer," Energy, Elsevier, vol. 269(C).
    9. 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.
    10. Wu, Yixi & Wang, Ziqi & Shi, Chenli & Jin, Xiaohang & Xu, Zhengguo, 2024. "A novel data-driven approach for coal-fired boiler under deep peak shaving to predict and optimize NOx emission and heat exchange performance," Energy, Elsevier, vol. 304(C).
    11. 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).
    12. 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).
    13. 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).
    14. Xiao, Feng & Yang, Zhengguang & Wei, Bo, 2024. "Distributed fixed-time cooperative control for flywheel energy storage systems with state-of-energy constraints," Energy, Elsevier, vol. 293(C).
    15. Halil Akbaş & Gültekin Özdemir, 2020. "An Integrated Prediction and Optimization Model of a Thermal Energy Production System in a Factory Producing Furniture Components," Energies, MDPI, vol. 13(22), pages 1-29, November.
    16. Yingai Jin & Yanwei Sun & Yuanbo Zhang & Zhipeng Jiang, 2022. "Research on Air Distribution Control Strategy of Supercritical Boiler," Energies, MDPI, vol. 16(1), pages 1-19, December.
    17. Tongu, Daiki & Obara, Shin'ya, 2024. "Formation temperature range expansion and energy storage properties of CO2 hydrates," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    18. Tongtong Li & Liang Tao & Binzi Xu, 2024. "Linear Parameter Varying Observer-Based Adaptive Dynamic Surface Sliding Mode Control for PMSM," Mathematics, MDPI, vol. 12(8), pages 1-26, April.
    19. Darbandi, Masoud & Fatin, Ali & Bordbar, Hadi, 2020. "Numerical study on NOx reduction in a large-scale heavy fuel oil-fired boiler using suitable burner adjustments," Energy, Elsevier, vol. 199(C).
    20. 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).

    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:16:y:2023:i:21:p:7411-:d:1273251. 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.