IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v18y2025i3p567-d1576858.html
   My bibliography  Save this article

Collaborative Control and Intelligent Optimization of a Lead–Bismuth Cooled Reactor Based on a Modified PSO Method

Author

Listed:
  • Shoujun Yan

    (China Institute of Nuclear Industry Strategy, Beijing 100048, China)

  • Lijie Zhou

    (China Institute of Nuclear Industry Strategy, Beijing 100048, China)

  • Lifeng Song

    (China Institute of Nuclear Industry Strategy, Beijing 100048, China)

  • Huiyu Guo

    (School of Resource & Environment and Safety Engineering, University of South China, Hengyang 421001, China)

  • Junliang Wu

    (School of Resource & Environment and Safety Engineering, University of South China, Hengyang 421001, China)

  • Run Luo

    (School of Resource & Environment and Safety Engineering, University of South China, Hengyang 421001, China)

  • Fuyu Zhao

    (School of Nuclear Science and Technology, Xi’an Jiaotong University, Xi’an 710049, China)

Abstract

Accelerator-driven subcritical (ADS) reactors with lead–bismuth eutectic (LBE) coolants are some of the Gen-IV nuclear energy systems that can generate clean electricity and potentially transmute spent fuel. The dynamic characteristics and control strategy of an ADS reactor are substantially different from those of traditional nuclear reactors. In this paper, a new collaborative control strategy is proposed using an accelerator beam and a control rod, and the control system’s parameters are optimized using a modified particle swarm optimization (PSO) method. To test the control performance, a simulation platform is developed with a nonlinear reactor dynamic model, a power compensation control system and a coolant temperature control system. Four typical control transients are used, including a ±10% full-power (FP) step change load and a ±5% FP/min linear variable load. The simulation results show that the collaborative control strategy has a better load tracking capability and a higher power control accuracy than the beam single-control strategy and the rod single-control strategy. The results also show that the performance of the collaborative control system in terms of the reactor’s power and coolant temperature is significantly improved based on the modified PSO parameter optimization.

Suggested Citation

  • Shoujun Yan & Lijie Zhou & Lifeng Song & Huiyu Guo & Junliang Wu & Run Luo & Fuyu Zhao, 2025. "Collaborative Control and Intelligent Optimization of a Lead–Bismuth Cooled Reactor Based on a Modified PSO Method," Energies, MDPI, vol. 18(3), pages 1-20, January.
  • Handle: RePEc:gam:jeners:v:18:y:2025:i:3:p:567-:d:1576858
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/18/3/567/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/18/3/567/
    Download Restriction: no
    ---><---

    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:18:y:2025:i:3:p:567-:d:1576858. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.