Research on Regression Prediction of Pressurizer Liquid Level under Ocean Conditions Based on SSA-LSTM Neural Network

Li Dongyang; Quan Zixuan; Zhang Biao; Li Jiangkuan; Tan Sichao; Tian Ruifeng

doi:10.13832/j.jnpe.2024.050045

Volume 46 Issue 2

Apr. 2025

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Article Navigation > Nuclear Power Engineering > 2025 > 46(2): 293-299

Li Dongyang, Quan Zixuan, Zhang Biao, Li Jiangkuan, Tan Sichao, Tian Ruifeng. Research on Regression Prediction of Pressurizer Liquid Level under Ocean Conditions Based on SSA-LSTM Neural Network[J]. Nuclear Power Engineering, 2025, 46(2): 293-299. doi: 10.13832/j.jnpe.2024.050045

Citation:

Li Dongyang, Quan Zixuan, Zhang Biao, Li Jiangkuan, Tan Sichao, Tian Ruifeng. Research on Regression Prediction of Pressurizer Liquid Level under Ocean Conditions Based on SSA-LSTM Neural Network[J]. Nuclear Power Engineering, 2025, 46(2): 293-299. doi: 10.13832/j.jnpe.2024.050045

Citation:

Li Dongyang, Quan Zixuan, Zhang Biao, Li Jiangkuan, Tan Sichao, Tian Ruifeng. Research on Regression Prediction of Pressurizer Liquid Level under Ocean Conditions Based on SSA-LSTM Neural Network[J]. Nuclear Power Engineering, 2025, 46(2): 293-299. doi: 10.13832/j.jnpe.2024.050045

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Research on Regression Prediction of Pressurizer Liquid Level under Ocean Conditions Based on SSA-LSTM Neural Network

doi: 10.13832/j.jnpe.2024.050045

Li Dongyang^{1, 2
,},
Quan Zixuan^{1, 2},
Zhang Biao³,
Li Jiangkuan^{1, 2},
Tan Sichao^{1, 2},
Tian Ruifeng^{1, 2}

1.
Heilongjiang Provincial Key Laboratory of Nuclear Power System & Equipment , Harbin Engineering University, Harbin, 150001, China
2.
Key Laboratory of Nuclear Safety and Advanced Nuclear Energy Technology, Ministry of Industry and Information Technology, Harbin Engineering University, Harbin, 150001, China
3.
Wuhan Second Ship Design and Research Institute, Wuhan, 430062, China

Received Date: 2024-05-15
Rev Recd Date: 2024-06-17

Available Online: 2025-01-15

Publish Date: 2025-04-02

Abstract

Abstract

To ensure the safe operation of the nuclear reactor system in the ocean environment, it is necessary to establish a set of computational models to obtain the real-time liquid level in the pressurizer. By building an experimental system to collect relevant data, the Long-Short Term Memory (LSTM) neural network is optimized based on the sparrow search algorithm (SSA), and the liquid level regression prediction model is established according to the measured key parameters such as pressure and motion attitude. The research results show that the prediction accuracy of the established liquid level regression prediction model is excellent, which is obviously better than other traditional neural networks. The model has good generalization ability, and the prediction accuracy of fresh samples is still acceptable. By integrating the model into the control system, the liquid level can be output in real time, which can improve the safety of nuclear power operation under ocean conditions and provide reference for the intelligent operation and maintenance of nuclear power.
- Ocean conditions,
- Sparrow search algorithm (SSA),
- Long-Short Term Memory (LSTM) neural network,
- Liquid level regression prediction

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References(16)

References

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