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On the Flexible Operation of Supercritical Circulating Fluidized Bed: Burning Carbon Based Decentralized Active Disturbance Rejection Control

Author

Listed:
  • Fan Zhang

    (State Key Lab of Power System, Department of Energy and Power Engineering, Tsinghua University, Beijing 100084, China)

  • Yali Xue

    (State Key Lab of Power System, Department of Energy and Power Engineering, Tsinghua University, Beijing 100084, China)

  • Donghai Li

    (State Key Lab of Power System, Department of Energy and Power Engineering, Tsinghua University, Beijing 100084, China)

  • Zhenlong Wu

    (State Key Lab of Power System, Department of Energy and Power Engineering, Tsinghua University, Beijing 100084, China)

  • Ting He

    (State Key Lab of Power System, Department of Energy and Power Engineering, Tsinghua University, Beijing 100084, China)

Abstract

Supercritical circulating fluidized bed (CFB) is one of the prominent clean coal technologies owing to the advantages of high efficiency, fuel flexibility, and low cost of emission control. The fast and flexible load-tracking performance of the supercritical CFB boiler-turbine unit presents a promising prospect in facilitating the sustainability of the power systems. However, features such as large inertia, strong nonlinearity, and multivariable coupling make it a challenging task to harmonize the boiler’s slow dynamics with the turbine’s fast dynamics. To improve the operational flexibility of the supercritical CFB unit, a burning carbon based decentralized active disturbance rejection control is proposed. Since burning carbon in the furnace responds faster than throttle steam pressure when the fuel flow rate changes, it is utilized to compensate the dynamics of the corresponding loop. The parameters of the controllers are tuned by optimizing the weighted integrated absolute error index of each loop via genetic algorithm. Simulations of the proposed method on a 600 MW supercritical CFB unit verify the merits of load following and disturbance rejection in terms of less settling time and overshoot.

Suggested Citation

  • Fan Zhang & Yali Xue & Donghai Li & Zhenlong Wu & Ting He, 2019. "On the Flexible Operation of Supercritical Circulating Fluidized Bed: Burning Carbon Based Decentralized Active Disturbance Rejection Control," Energies, MDPI, vol. 12(6), pages 1-18, March.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:6:p:1132-:d:216477
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    References listed on IDEAS

    as
    1. Ting He & Zhenlong Wu & Rongqi Shi & Donghai Li & Li Sun & Lingmei Wang & Song Zheng, 2019. "Maximum Sensitivity-Constrained Data-Driven Active Disturbance Rejection Control with Application to Airflow Control in Power Plant," Energies, MDPI, vol. 12(2), pages 1-23, January.
    2. Zhuo, Xusheng & Lou, Chun & Zhou, Huaichun & Zhuo, Jinxuan & Fu, Peifang, 2018. "Hierarchical Takagi-Sugeno fuzzy hyperbolic tangent static model control for a circulating fluidized bed boiler thermal power unit," Energy, Elsevier, vol. 162(C), pages 910-917.
    3. Sun, Li & Shen, Jiong & Hua, Qingsong & Lee, Kwang Y., 2018. "Data-driven oxygen excess ratio control for proton exchange membrane fuel cell," Applied Energy, Elsevier, vol. 231(C), pages 866-875.
    4. Brouwer, Anne Sjoerd & van den Broek, Machteld & Seebregts, Ad & Faaij, André, 2015. "Operational flexibility and economics of power plants in future low-carbon power systems," Applied Energy, Elsevier, vol. 156(C), pages 107-128.
    5. Lv, You & Hong, Feng & Yang, Tingting & Fang, Fang & Liu, Jizhen, 2017. "A dynamic model for the bed temperature prediction of circulating fluidized bed boilers based on least squares support vector machine with real operational data," Energy, Elsevier, vol. 124(C), pages 284-294.
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    Citations

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    Cited by:

    1. Boyu Deng & Yi Zhang & Hairui Yang, 2022. "Operation Optimization of Circulating Fluidized Bed Boilers Integration of Variable Renewables," Energies, MDPI, vol. 15(16), pages 1-3, August.
    2. Hong, Feng & Wang, Rui & Song, Jie & Gao, Mingming & Liu, Jizhen & Long, Dongteng, 2022. "A performance evaluation framework for deep peak shaving of the CFB boiler unit based on the DBN-LSSVM algorithm," Energy, Elsevier, vol. 238(PA).
    3. Gengjin Shi & Zhenlong Wu & Jian Guo & Donghai Li & Yanjun Ding, 2020. "Superheated Steam Temperature Control Based on a Hybrid Active Disturbance Rejection Control," Energies, MDPI, vol. 13(7), pages 1-26, April.
    4. Hui-Yu Jin & Yang Chen, 2023. "First-Order Linear Active Disturbance Rejection Control for Turbofan Engines," Energies, MDPI, vol. 16(6), pages 1-17, March.

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