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

Wenshun Duan, Kefan Zhang, Weixiang Wang, Sifan Dong, Rui Pan, Chong Qin and Hongli Chen

Applied Energy, 2025, vol. 378, issue PA, No S0306261924021731

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.

Keywords: Lead‑bismuth fast reactor; NCLFR-oil; Parameter prediction; Recurrent neural network; Typical accidents (search for similar items in EconPapers)
Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261924021731
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:378:y:2025:i:pa:s0306261924021731

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2024.124790

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().