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Tuning of PID Controller Using Particle Swarm Optimization for Cross Flow Heat Exchanger Based on CFD System Identification

  • Omar Khaled Sallam
  • Ahmad Taher AzarEmail author
  • Amr Guaily
  • Hossam Hassan Ammar
Conference paper
  • 229 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1058)

Abstract

This paper illustrates the design of proportional–integral–derivative controller (PID) controller of 10 KW air heaters for achieving the set point temperature as fast as possible with minimum response overshoot. Computational fluid dynamic (CFD) numerical simulations are utilized to predict the natural response of 10 KW input power for the air heater. CFD results are validated with experimental empirical correlations that insure the reliability of open loop results. The open loop response of CFD transient simulations is used to model the air heater transfer function and design the classical PID controllers. Particle swarm optimization (PSO) technique is used to tune the PID controller with various error fitness functions which leads to improve the closed loop response of the temperature control system compared to the classical tuning methods.

Keywords

PID controller Particle swarm optimization Heat exchanger Computational fluid dynamic Turbulence models 

References

  1. 1.
    Çengel, Y.A.: Heat Transfer Practical approach, 2nd edn. McGraw-Hill, New York (2002)Google Scholar
  2. 2.
    Cengel, Y.A., Cimbala, J.M.: Fluid Mechanics Fundamentals and Applications. McGraw-Hill, Boston (2006)Google Scholar
  3. 3.
    Ogata, K.: Modern Control Engineering, 5th edn. Prentice Hall, NJ (2010)zbMATHGoogle Scholar
  4. 4.
    Azar, A.T., Vaidyanathan, S.: Advances in system dynamics and control. In: Advances in Computational Intelligence and Robotics (ACIR). IGI Global, USA (2018). ISBN 9781522540779Google Scholar
  5. 5.
    Azar, A.T., Vaidyanathan, S.: Handbook of research on advanced intelligent control engineering and automation. In: Advances in Computational Intelligence and Robotics (ACIR). IGI Global, USA (2015). ISBN 9781466672482Google Scholar
  6. 6.
    Vasickaninová, A., Bakošová, M.: Control of a heat exchanger using neural network predictive controller combined with auxiliary fuzzy controller. Appl. Therm. Eng. 89(2015), 1046–1053 (2015)CrossRefGoogle Scholar
  7. 7.
    Maidi, A., Diaf, Moussa, Corriou, Jean-Pierre: Optimal linear PI fuzzy controller design of a heat exchanger. Chem. Eng. Process. 47(5), 938–945 (2008)CrossRefGoogle Scholar
  8. 8.
    Vasickaninová, A., Bakošová, M., Cirka, L., Kalúz, M., Oravec, J.: Robust controller design for a laboratory heat exchanger. Appl. Therm. Eng. 128(2018), 1297–1309 (2018)CrossRefGoogle Scholar
  9. 9.
    Wang, Y., You, S., Zheng, W., Zhang, H., Zheng, X., Miao, O.: State space model and robust control of plate heat exchanger for dynamic performance improvement. Appl. Therm. Eng. 128(2018), 1588–1604 (2018)CrossRefGoogle Scholar
  10. 10.
    Jain, M., Rani, A., Pachauri, N., Singh, V., Mittal, A.P.: Design of fractional order 2-DOF PI controller for real-time control of heat flow experiment. Eng. Sci. Technol. Int. J. 22(1), 215–228 (2019)CrossRefGoogle Scholar
  11. 11.
    Padhee, S.: Controller design for temperature control of heat exchanger system: simulation studies. WSEAS Trans. Syst. Control 9, 485–491 (2014)Google Scholar
  12. 12.
    Yu, Y., Yin, D.: Application of the BP neural network PID algorithm in heat transfer station control. In: Xie, A., Huang, X. (eds.) Advances in Computer Science and Education. AISC, vol. 140. Springer, Heidelberg (2012)Google Scholar
  13. 13.
    Sungthong, A., Assawinchaichote, W.: Particle swam optimization based optimal PID parameters for air heater temperature control system. Procedia Comput. Sci. 86(2016), 108–111 (2016)CrossRefGoogle Scholar
  14. 14.
    Hoffmann, K.A., Chiang, S.T.: Computational Fluid Dynamics, Engineering Education System, 4 edn., V4 (2000)Google Scholar
  15. 15.
    Gherasim, I., Galanis, N., Nguyen, C.T.: Heat transfer and fluid flow in a plate heat exchanger. Part II: Assessment of laminar and two-equation turbulent models. Int. J. Thermal Sci. 50(8), 1499–1511 (2011)CrossRefGoogle Scholar
  16. 16.
    Allegrini, J., Dorer, V., Defraeye, T., Carmeliet, J.: An adaptive temperature wall function for mixed convective flows at exterior surfaces of buildings in street canyons. Build. Environ. 49(2012), 55–66 (2012)CrossRefGoogle Scholar
  17. 17.
    Zukauskas, A.: Convection heat transfer in cross flow. In: Hartnett, J.P., Irvine Jr., T.F. (eds.) Advances in Heat Transfer, vol. 8, pp. 93–106. Academic Press, New York (1972)Google Scholar
  18. 18.
    Azar, A.T., Serrano, F.E.: Robust IMC-PID tuning for cascade control systems with gain and phase margin specifications. Neural Comput. Appl. 25(5), 983–995 (2014)CrossRefGoogle Scholar
  19. 19.
    Azar, A.T., Ammar, H.H., de Brito Silva, G., Razali, M.S.A.B.: Optimal Proportional Integral Derivative (PID) controller design for smart irrigation mobile robot with soil moisture sensor. In: The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA 2019). AISC, vol 921, pp. 349–359. Springer, Cham (2020)Google Scholar
  20. 20.
    Hassanien, A.E., Emary, E.: Swarm Intelligence: Principles, Advances, and Applications. CRC Press, Boca Raton (2018)Google Scholar
  21. 21.
    Azar, A.T., Serrano, F.E.: Fractional order sliding mode PID controller/observer for continuous nonlinear switched systems with PSO parameter tuning. The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA 2018). AISC, vol. 723, pp. 13–22. Springer, Cham (2018)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Omar Khaled Sallam
    • 1
  • Ahmad Taher Azar
    • 2
    • 3
    Email author
  • Amr Guaily
    • 1
    • 4
  • Hossam Hassan Ammar
    • 1
  1. 1.School of Engineering and Applied SciencesNile UniversityGizaEgypt
  2. 2.College of EngineeringPrince Sultan UniversityRiyadhKingdom of Saudi Arabia
  3. 3.Faculty of Computers and Artificial IntelligenceBenha UniversityBenhaEgypt
  4. 4.Faculty of EngineeringCairo UniversityGizaEgypt

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