Skip to content
Licensed Unlicensed Requires Authentication Published by De Gruyter November 22, 2022

Design and optimization of molten salt reactor monitoring system based on digital twin technology

Wenqian Liu , Lifeng Han EMAIL logo and Li Huang
From the journal Kerntechnik

Abstract

The nuclear power industry is developing rapidly toward intelligence and scale, the digital twin was combined with the industrial interconnection technology to solve the key problems in the application of the digital twin, such as the three-dimensional model presentation, real-time data docking, and the improvement of intelligence degree. Based on the example of Thorium Molten Salt Reactor-Solid Fuel (TMSR-SF0). Firstly, the three-dimensional twin of nuclear power equipment is constructed and the real-time update of twin data is realized based on the Node-EPICS event driver and Websocket communication protocol; Then, the communication interface with MySQL database is developed to realize the storage and management of data; Finally, the PID control system of molten salt circuit pipeline is integrated with back propagation neural network algorithm, and the efficiency and precision of temperature control system are improved by self-modification of weight. The results show that this system has the functions of three-dimensional display, network communication, data storage, and parameter optimization, and the data update cycle is raised to 100 ms, which can provide a certain reference value for the digital transformation of the nuclear monitoring field.


Corresponding author: Lifeng Han, Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai, China, E-mail:

Funding source: the Strategic Priority Research Program of Chinese Academy of Sciences

Award Identifier / Grant number: XDA02010300

Acknowledgements

This work is supported by the Strategic Priority Research Program of Chinese Academy of Sciences (No.XDA02010300).

  1. Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Research funding: None declared.

  3. Conflict of interest statement: The authors declare no conflicts of interest regarding this article.

References

Bu, Q., Cai, J., Liu, Y., Cao, M., Dong, L., Ruan, R., and Mao, H. (2021). The effect of fuzzy PID temperature control on thermal behavior analysis and kinetics study of biomass microwave pyrolysis. J. Anal. Appl. Pyrol. 158: 105176, https://doi.org/10.1016/j.jaap.2021.105176.Search in Google Scholar

Cai, X., Dai, Z., and Xu, H. (2016). Thorium molten salt reactor nuclear energy system. Physics 45: 578–590, https://doi.org/10.7693/wl20160904.Search in Google Scholar

Hosseini, S.A., Shirani, A.S., Lotfi, M., and Menhaj, M.B. (2020). Design and application of supervisory control based on neural network PID controllers for pressurizer system. Prog. Nucl. Energy 130: 103570, https://doi.org/10.1016/j.pnucene.2020.103570.Search in Google Scholar

Hu, M., Kong, F., Yu, D., and Yang, J. (2021). Key technology and prospects of digital twin in field of advanced nuclear energy. Power Syst. Technol. 45: 2514–2522, https://doi.org/10.13335/j.1000-3673.pst.2021.0335.Search in Google Scholar

Jiang, M., Xu, H., and Dai, Z. (2012). Advanced fission energy program-TMSR nuclear energy system. Bull. Chin. Acad. Sci. 27: 366–374, https://doi.org/10.3969/j.issn.1000-3045.2012.03.016.Search in Google Scholar

Kuftinova, N.G., Ostroukh, A.V., Maksimychev, O.I., Vasil’ev, Y.E., and Klimenko, V.A. (2022). Digital twins in smart data management at a manufacturing enterprise. Russ. Eng. Res. 42: 162–164, https://doi.org/10.3103/s1068798x22020149.Search in Google Scholar

Liang, H., Sang, Z.-K., Wu, Y.-Z., Zhang, Y.-H., and Zhao, R. (2021). High precision temperature control performance of a PID neural network-controlled heater under complex outdoor conditions. Appl. Therm. Eng. 195: 117234, https://doi.org/10.1016/j.applthermaleng.2021.117234.Search in Google Scholar

Lixin, H. and Jie, H. (2021). Design and implementation of communication scheme between TMSR-SF0 control system and protection system. Nucl. Tech. 44: 020601, https://doi.org/10.11889/j.0253-3219.2021.hjs.44.020601.Search in Google Scholar

Meng, Z., Zhang, L., Li, H., Zhou, R., Bu, H., Shan, Y., Ma, X., and Ma, R. (2022). Design and application of liquid fertilizer pH regulation controller based on BP-PID-Smith predictive compensation algorithm. Appl. Sci. 12: 6162, https://doi.org/10.3390/app12126162.Search in Google Scholar

Mingming, L., Nan, G., Quandong, L., Xinglian, J., Xu, Z., and Zhao, C. (2019). Application and exploration of virtual reality technology in main control room design of nuclear power plant. J. Shanghai Jiaot. Univ. 53: 29.Search in Google Scholar

Nuerlan, A., Wang, P., Rizwan, U., and Zhao, F. (2020). A neural network based inverse system control strategy to decouple turbine power in multi-reactor and multi-turbine nuclear power plant. Prog. Nucl. Energy 129: 103500, https://doi.org/10.1016/j.pnucene.2020.103500.Search in Google Scholar

Pan, Y.H., Wu, N.Q., Qu, T., Li, P.Z., Zhang, K., and Guo, H.F. (2020). Digital-twin-driven production logistics synchronization system for vehicle routing problems with pick-up and delivery in industrial park. Int. J. Comput. Integrated Manuf. 34: 814–828, https://doi.org/10.1080/0951192x.2020.1829059.Search in Google Scholar

Wang, K., Ma, Q., Yang, Z., and Gao, N. (2019). Design framework and application exploration in nuclear I&C DCS based on digital twin technology. Instrumentation 26: 43–47, https://doi.org/10.3969/j.issn.1671-1041.2019.11.012.Search in Google Scholar

Wei, X., Wang, P., and Zhao, F. (2016). Design of a decoupled AP1000 reactor core control system using digital proportional–integral–derivative (PID) control based on a quasi-diagonal recurrent neural network (QDRNN). Nucl. Eng. Des. 304: 40–49, https://doi.org/10.1016/j.nucengdes.2016.04.022.Search in Google Scholar

Wu, L., Han, L., Huang, W., Li, J., Li, D., Zhang, L., and Chen, Y. (2018). Application of real-time Web techniques in online radiation monitoring system. J. Comput. Appl. 38: 337–340.Search in Google Scholar

Zhao, L., Gu, H., Xu, J., Cui, Y., and Shuai, C. (2021). Research on Simufact simulation data processing system based on QT and MySQL. Appl. math. nonlinear sci. 6: 291–298, https://doi.org/10.2478/amns.2021.2.00042.Search in Google Scholar

Received: 2022-05-29
Published Online: 2022-11-22
Published in Print: 2022-12-16

© 2022 Walter de Gruyter GmbH, Berlin/Boston

Downloaded on 31.1.2023 from https://www.degruyter.com/document/doi/10.1515/kern-2022-0055/html
Scroll Up Arrow