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Parameter prediction of lead-bismuth fast reactor under various accidents with recurrent neural network

Author

Listed:
  • Duan, Wenshun
  • Zhang, Kefan
  • Wang, Weixiang
  • Dong, Sifan
  • Pan, Rui
  • Qin, Chong
  • Chen, Hongli

Abstract

Advanced nuclear reactor plays an important role in the sustainable development of green energy, and lead-cooled fast reactors are one of the most promising types. To further improve the safety of lead‑bismuth fast reactors, it is necessary to predict the key parameters and their changing trends under various working conditions quickly and accurately. The prediction method based on the neural network can achieve this goal. In this paper, by using the data of lead‑bismuth reactor NCLFR-Oil under four types of typical accidents, the generalized accident prediction model of lead‑bismuth fast reactor is established with the neural network. First, by comparing the performance differences between the prediction models based on six different neural networks, the gated recurrent neural network with the addition of attention mechanism (AT_GRU) performs the best. Then, a prediction model is established based on the AT_GRU coupled grey wolf optimization algorithm (GWO), and the parameter prediction analysis is carried out for 160 cases of four types of accidents. The results show that the prediction results of the four kinds of accidents are good, even the MAPE, RMSE and R2 of the accidents with relatively poor performance can reach 0.165 %, 1.334 °C and 0.9980, respectively. Whether it is a single-type accident model or a general model, the average prediction time of a single case is between 0.014 and 0.035 s, which can be said that the model has realized real-time prediction. Since this paper is not about the prediction of a single working condition, the prediction model obtained is more generalized and has more practical significance.

Suggested Citation

  • Duan, Wenshun & Zhang, Kefan & Wang, Weixiang & Dong, Sifan & Pan, Rui & Qin, Chong & Chen, Hongli, 2025. "Parameter prediction of lead-bismuth fast reactor under various accidents with recurrent neural network," Applied Energy, Elsevier, vol. 378(PA).
    • Handle: RePEc:eee:appene:v:378:y:2025:i:pa:s0306261924021731
    DOI: 10.1016/j.apenergy.2024.124790
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    References listed on IDEAS

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    1. Song, Houde & Liu, Xiaojing & Song, Meiqi, 2023. "Comparative study of data-driven and model-driven approaches in prediction of nuclear power plants operating parameters," Applied Energy, Elsevier, vol. 341(C).
    2. 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).
    3. Polzin, Friedemann & Egli, Florian & Steffen, Bjarne & Schmidt, Tobias S., 2019. "How do policies mobilize private finance for renewable energy?—A systematic review with an investor perspective," Applied Energy, Elsevier, vol. 236(C), pages 1249-1268.
    4. Wang, Zhen & Lam, Jasmine Siu Lee & Huo, Jiazhen, 2024. "The bidding strategy for renewable energy auctions under government subsidies," Applied Energy, Elsevier, vol. 353(PB).
    5. Fang, Guochang & Chen, Gang & Yang, Kun & Yin, Weijun & Tian, Lixin, 2024. "How does green fiscal expenditure promote green total factor energy efficiency? — Evidence from Chinese 254 cities," Applied Energy, Elsevier, vol. 353(PA).
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