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Review on the Recent Progress in Nuclear Plant Dynamical Modeling and Control

Author

Listed:
  • Zhe Dong

    (Institute of Nuclear and New Energy Technology, Collaborative Innovation Centre of Advanced Nuclear Energy Technology, Key Laboratory of Advanced Reactor Engineering and Safety of Ministry of Education, Tsinghua University, Beijing 100084, China)

  • Zhonghua Cheng

    (Institute of Nuclear and New Energy Technology, Collaborative Innovation Centre of Advanced Nuclear Energy Technology, Key Laboratory of Advanced Reactor Engineering and Safety of Ministry of Education, Tsinghua University, Beijing 100084, China)

  • Yunlong Zhu

    (Institute of Nuclear and New Energy Technology, Collaborative Innovation Centre of Advanced Nuclear Energy Technology, Key Laboratory of Advanced Reactor Engineering and Safety of Ministry of Education, Tsinghua University, Beijing 100084, China)

  • Xiaojin Huang

    (Institute of Nuclear and New Energy Technology, Collaborative Innovation Centre of Advanced Nuclear Energy Technology, Key Laboratory of Advanced Reactor Engineering and Safety of Ministry of Education, Tsinghua University, Beijing 100084, China)

  • Yujie Dong

    (Institute of Nuclear and New Energy Technology, Collaborative Innovation Centre of Advanced Nuclear Energy Technology, Key Laboratory of Advanced Reactor Engineering and Safety of Ministry of Education, Tsinghua University, Beijing 100084, China)

  • Zuoyi Zhang

    (Institute of Nuclear and New Energy Technology, Collaborative Innovation Centre of Advanced Nuclear Energy Technology, Key Laboratory of Advanced Reactor Engineering and Safety of Ministry of Education, Tsinghua University, Beijing 100084, China)

Abstract

Nuclear plant modeling and control is an important subject in nuclear power engineering, giving the dynamic model from process mechanics and/or operational data as well as guaranteeing satisfactory transient and steady-state operational performance by well-designed plant control laws. With the fast development of small modular reactors (SMRs) and in the context of massive integration of intermittent renewables, it is required to operate the nuclear plants more reliably, efficiently, flexibly and smartly, motivating the recent exciting progress in nuclear plant modeling and control. In this paper, the main progress during the last several years in dynamical modeling and control of nuclear plants is reviewed. The requirement of nuclear plant operation to the subject of modeling and control is first given. By categorizing the results to the aspects of mechanism-based, data-based and hybrid modeling methods, the advances in dynamical modeling are then given, where the modeling of SMR plants, learning-based modeling and state-observers are typical hot topics. In addition, from the directions of intelligent control, nonlinear control, online control optimization and multimodular coordinated control, the advanced results in nuclear plant control methods are introduced, where the hot topics include fuzzy logic inference, neural-network control, reinforcement learning, sliding mode, feedback linearization, passivation and decoupling. Based upon the review of recent progress, the future directions in nuclear plant modeling and control are finally given.

Suggested Citation

  • Zhe Dong & Zhonghua Cheng & Yunlong Zhu & Xiaojin Huang & Yujie Dong & Zuoyi Zhang, 2023. "Review on the Recent Progress in Nuclear Plant Dynamical Modeling and Control," Energies, MDPI, vol. 16(3), pages 1-19, February.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:3:p:1443-:d:1053892
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    References listed on IDEAS

    as
    1. Dong, Zhe & Pan, Yifei & Zhang, Zuoyi & Dong, Yujie & Huang, Xiaojin, 2018. "Dynamical modeling and simulation of the six-modular high temperature gas-cooled reactor plant HTR-PM600," Energy, Elsevier, vol. 155(C), pages 971-991.
    2. Yunlong Zhu & Zhe Dong & Duo Li & Xiaojin Huang & Yujie Dong & Yajun Zhang & Zuoyi Zhang, 2022. "A Finite-Time Differentiator with Application to Nuclear Reactor Inverse Period Measurement," Energies, MDPI, vol. 15(12), pages 1-15, June.
    3. Nguyen, Hoang-Phuong & Baraldi, Piero & Zio, Enrico, 2021. "Ensemble empirical mode decomposition and long short-term memory neural network for multi-step predictions of time series signals in nuclear power plants," Applied Energy, Elsevier, vol. 283(C).
    4. Jiang, Di & Dong, Zhe, 2019. "Practical dynamic matrix control of MHTGR-based nuclear steam supply systems," Energy, Elsevier, vol. 185(C), pages 695-707.
    5. Dong, Zhe & Pan, Yifei & Zhang, Zuoyi & Dong, Yujie & Huang, Xiaojin, 2017. "Model-free adaptive control law for nuclear superheated-steam supply systems," Energy, Elsevier, vol. 135(C), pages 53-67.
    6. Dong, Zhe & Li, Bowen & Li, Junyi & Guo, Zhiwu & Huang, Xiaojin & Zhang, Yajun & Zhang, Zuoyi, 2021. "Flexible control of nuclear cogeneration plants for balancing intermittent renewables," Energy, Elsevier, vol. 221(C).
    7. Dong, Zhe & Zhang, Zuoyi & Dong, Yujie & Huang, Xiaojin, 2018. "Multi-layer perception based model predictive control for the thermal power of nuclear superheated-steam supply systems," Energy, Elsevier, vol. 151(C), pages 116-125.
    8. Jiang, Di & Dong, Zhe, 2020. "Dynamic matrix control for thermal power of multi-modular high temperature gas-cooled reactor plants," Energy, Elsevier, vol. 198(C).
    9. Hui, Jiuwu & Yuan, Jingqi, 2022. "Neural network-based adaptive fault-tolerant control for load following of a MHTGR with prescribed performance and CRDM faults," Energy, Elsevier, vol. 257(C).
    10. Zhu, Yunlong & Dong, Zhe & Cheng, Zhonghua & Huang, Xiaojin & Dong, Yujie & Zhang, Zuoyi, 2023. "Neural network extended state-observer for energy system monitoring," Energy, Elsevier, vol. 263(PA).
    11. Yunlong Zhu & Zhe Dong & Xiaojin Huang & Yujie Dong & Yajun Zhang & Zuoyi Zhang, 2022. "Passivity-Based Power-Level Control of Nuclear Reactors," Energies, MDPI, vol. 15(14), pages 1-11, July.
    12. Di Jiang & Zhe Dong & Miao Liu & Xiaojin Huang, 2018. "Dynamic Matrix Control for the Thermal Power of MHTGR-Based Nuclear Steam Supply System," Energies, MDPI, vol. 11(10), pages 1-15, October.
    13. Dong, Zhe & Liu, Miao & Zhang, Zuoyi & Dong, Yujie & Huang, Xiaojin, 2019. "Automatic generation control for the flexible operation of multimodular high temperature gas-cooled reactor plants," Renewable and Sustainable Energy Reviews, Elsevier, vol. 108(C), pages 11-31.
    14. Dong, Zhe & Huang, Xiaojin & Dong, Yujie & Zhang, Zuoyi, 2020. "Multilayer perception based reinforcement learning supervisory control of energy systems with application to a nuclear steam supply system," Applied Energy, Elsevier, vol. 259(C).
    15. Wang, Pengfei & Zhang, Jiaxuan & Wan, Jiashuang & Wu, Shifa, 2022. "A fault diagnosis method for small pressurized water reactors based on long short-term memory networks," Energy, Elsevier, vol. 239(PC).
    16. Dong, Zhe & Li, Bowen & Li, Junyi & Jiang, Di & Guo, Zhiwu & Huang, Xiaojin & Zhang, Zuoyi, 2021. "Passivity based control of heat exchanger networks with application to nuclear heating," Energy, Elsevier, vol. 223(C).
    17. Dong, Zhe & Liu, Miao & Guo, Zhiwu & Huang, Xiaojin & Zhang, Yajun & Zhang, Zuoyi, 2019. "Adaptive state-observer for monitoring flexible nuclear reactors," Energy, Elsevier, vol. 171(C), pages 893-909.
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    Cited by:

    1. Yao Tong & Duo Zhang & Zhijiang Shao & Xiaojin Huang, 2023. "Global Model Calibration of High-Temperature Gas-Cooled Reactor Pebble-Bed Module Using an Adaptive Experimental Design," Energies, MDPI, vol. 16(12), pages 1-25, June.
    2. Shupeng Zheng & Zecheng Luo & Jiwu Wu & Lunyuan Zhang & Yijian He, 2024. "Study on Multivariable Dynamic Matrix Control for a Novel Solar Hybrid STIGT System," Energies, MDPI, vol. 17(6), pages 1-27, March.
    3. Piotr Bórawski & Aneta Bełdycka-Bórawska & Bogdan Klepacki & Lisa Holden & Tomasz Rokicki & Andrzej Parzonko, 2024. "Changes in Gross Nuclear Electricity Production in the European Union," Energies, MDPI, vol. 17(14), pages 1-31, July.

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