Skip to main content

Performance Evaluation of Research Reactors Under Different Predictive Controllers

  • Conference paper
  • First Online:
The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2019) (AMLTA 2019)

Abstract

This paper is concerned with the evaluation of nuclear research reactor under two types of predictive controllers. The first one is Receding Horizon Predictive Controller (RHPC) which is considered a simple linear predictive controller. The other one is Neural Network Predictive Controller (NNPC) which is a type of nonlinear predictive controller. These controllers are applied over multi-point reactor core model. This model takes into consideration the nonlinearity of the reactor. It also takes into consideration some important physical phenomena like temperature effect, time variant fuel depletion and nonlinear xenon poisoning concentration. Simulation results showed the superiority of RHPC in both tracking and regulation.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. IAEA, Applications of Research Reactors. International Atomic Energy Agency, Vienna International Centre (2014)

    Google Scholar 

  2. Mahmoud, I.I: Modeling and automatic control of nuclear reactors. In: IEEE Proceedings of the Nineteenth National Radio Science Conference, vol. 1–2. IEEE, New York (2002)

    Google Scholar 

  3. Emara, H.M., et al.: Power stabilization of nuclear research reactor via fuzzy controllers. In: Proceedings of the 2002 American Control Conference, vol. 1–6. IEEE, New York (2002)

    Google Scholar 

  4. Das, S., Pan, I., Das, S.: Fractional order fuzzy control of nuclear reactor power with thermal-hydraulic effects in the presence of random network induced delay and sensor noise having long range dependence. Energy Convers. Manag. 68, 200–218 (2013)

    Article  Google Scholar 

  5. Kim, J.H., Park, S.H., Na, M.G.: Design of a model predictive load-following controller by discrete optimization of control rod speed for PWRs. Ann. Nucl. Energy 71, 343–351 (2014)

    Article  Google Scholar 

  6. Liu, X.J., Wang, M.Y.: Nonlinear fuzzy model predictive control for a PWR nuclear power plant. In: Mathematical Problem in Engineering, p. 10 (2014)

    Google Scholar 

  7. Abdel-Ghaffar, H., et al.: Neural generalized predictive controller stability analysis. In: The 15th International Conference on System Theory, Control and Computing (2011)

    Google Scholar 

  8. Li, G., et al.: Modeling and control of nuclear reactor cores for electricity generation: a review of advanced technologies. Renew. Sustain. Energy Rev. 60, 116–128 (2016)

    Article  Google Scholar 

  9. Andraws, M.S., et al.: Performance of receding horizon predictive controller for research reactors. In: 12th International Conference on Computer Engineering and Systems (ICCES) (2017)

    Google Scholar 

  10. Cameron, I.R.: Nuclear Fission Reactors. Springer, Boston (1982)

    Book  Google Scholar 

  11. Schultz, M.A.: Control of Nuclear Reactors and Power Plants. McGraw-Hill, New York (1961)

    Google Scholar 

  12. Keepin, G.R.: Physics of Nuclear Kinetics. Addison-Wesley, Reading, Palo Alto, London (1965)

    Google Scholar 

  13. Lamarsh, J.R., Anthony J.B.: Introduction to Nuclear Engineering. Prentice Hall, Upper Saddle River (2001)

    Google Scholar 

  14. Gupta, M.M., Jin, L., Homma, N.: Static and Dynamic Neural Networks. Wiley, New Jersey (2005)

    Google Scholar 

  15. Abdel-Ghaffar, H., et al.: Neural generalized predictive controller and internal model principle. In: The 17th International Conference on Automation and Computing (2011)

    Google Scholar 

  16. Beale, M.H., Hagan, M.T., Demuth, H.B.: Neural Network Toolbox User’s Guide: The MathWorks (2018)

    Google Scholar 

  17. Abdel-Ghaffar, H.: Neural networked control systems. In: Computer and Systems Engineering Conference, Ain Shams University: Cairo, Egypt (2013)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mina S. Andraws .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Andraws, M.S., Abd El-Hamid, A.A., Yousef, A.H., Mahmoud, I.I., Hammad, S.A. (2020). Performance Evaluation of Research Reactors Under Different Predictive Controllers. In: Hassanien, A., Azar, A., Gaber, T., Bhatnagar, R., F. Tolba, M. (eds) The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2019). AMLTA 2019. Advances in Intelligent Systems and Computing, vol 921. Springer, Cham. https://doi.org/10.1007/978-3-030-14118-9_47

Download citation

Publish with us

Policies and ethics