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Nonlinear Modeling and Inferential Multi-Model Predictive Control of a Pulverizing System in a Coal-Fired Power Plant Based on Moving Horizon Estimation

Author

Listed:
  • Xiufan Liang

    (Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education, Southeast University, Nanjing 210096, China)

  • Yiguo Li

    (Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education, Southeast University, Nanjing 210096, China)

  • Xiao Wu

    (Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education, Southeast University, Nanjing 210096, China)

  • Jiong Shen

    (Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education, Southeast University, Nanjing 210096, China)

Abstract

Fuel preparation is the control bottleneck in coal-fired power plants due to the unmeasurable nature or inaccurate measurement of key controlled variables. This paper proposes an inferential multi-model predictive control scheme based on moving horizon estimation for the fuel preparation system in coal-fired power plants, i.e., the pulverizing system, aimed at improving control precision of key operating variables that are unmeasurable or inaccurately measured, and improving system tracking performance across a wide operating range. We develop a first principle model of the pulverizing system considering the nonlinear dynamics of primary air, and then employ the genetic algorithm to identify the unknown model parameters. The outputs of the identified first principle model agree well with measured data from a real pulverizing system. Thereafter we derive a moving horizon estimation approach to estimate the desired, but unmeasurable or inaccurately measured, controlled variables. Estimation constraints are explicitly considered to reduce the influence of measurement uncertainty. Finally, nonlinearity of the pulverizing system is analyzed and a multi-model inferential predictive controller is developed using the extended input-output state space model to achieve offset-free performance. Simulation results show that the proposed soft sensor can provide improved estimates than conventional extended Kalman filter, and the proposed inferential control scheme can significantly improve performance of the pulverizing system.

Suggested Citation

  • Xiufan Liang & Yiguo Li & Xiao Wu & Jiong Shen, 2018. "Nonlinear Modeling and Inferential Multi-Model Predictive Control of a Pulverizing System in a Coal-Fired Power Plant Based on Moving Horizon Estimation," Energies, MDPI, vol. 11(3), pages 1-27, March.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:3:p:589-:d:135290
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    References listed on IDEAS

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    1. Duo Zhang & Guohai Liu & Wenxiang Zhao & Penghu Miao & Yan Jiang & Huawei Zhou, 2014. "A Neural Network Combined Inverse Controller for a Two-Rear-Wheel Independently Driven Electric Vehicle," Energies, MDPI, vol. 7(7), pages 1-15, July.
    2. Yu-Jia Zhai & Ding-Li Yu & Ke-Jun Qian & Sanghyuk Lee & Nipon Theera-Umpon, 2017. "A Soft Sensor-Based Fault-Tolerant Control on the Air Fuel Ratio of Spark-Ignition Engines," Energies, MDPI, vol. 10(1), pages 1-15, January.
    3. Zeng, De-Liang & Hu, Yong & Gao, Shan & Liu, Ji-Zhen, 2015. "Modelling and control of pulverizing system considering coal moisture," Energy, Elsevier, vol. 80(C), pages 55-63.
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    Cited by:

    1. Mohammad Qasem & Omar Mohamed & Wejdan Abu Elhaija, 2022. "Parameter Identification and Sliding Pressure Control of a Supercritical Power Plant Using Whale Optimizer," Sustainability, MDPI, vol. 14(13), pages 1-25, June.

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