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Robust model predictive control and PID control of shell-and-tube heat exchangers


  • Oravec, Juraj
  • Bakošová, Monika
  • Trafczynski, Marian
  • Vasičkaninová, Anna
  • Mészáros, Alajos
  • Markowski, Mariusz


Robust model predictive control (MPC) with integral action is designed for the shell-and-tube heat exchangers that are a part of an industrial heat-exchanger network. The advanced control strategy is used for optimizing the control performance as fouling influences operation of the heat exchangers and causes changes of the heat exchangers' parameters. The time-varying parameters of the heat exchangers are considered as parametric uncertainties and robust MPC is used as it is able to handle processes with uncertainties. Integral action is implemented in the robust MPC to assure offset-free control responses. The extensive simulation case study of the robust MPC and proportional-integral-derivative (PID) control of the shell-and-tube heat exchangers confirms significantly improved control performance and energy savings when the newly designed robust MPC with integral action is used.

Suggested Citation

  • Oravec, Juraj & Bakošová, Monika & Trafczynski, Marian & Vasičkaninová, Anna & Mészáros, Alajos & Markowski, Mariusz, 2018. "Robust model predictive control and PID control of shell-and-tube heat exchangers," Energy, Elsevier, vol. 159(C), pages 1-10.
  • Handle: RePEc:eee:energy:v:159:y:2018:i:c:p:1-10
    DOI: 10.1016/

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    References listed on IDEAS

    1. Zhang, Jianhua & Lin, Mingming & Chen, Junghui & Xu, Jinliang & Li, Kang, 2017. "PLS-based multi-loop robust H2 control for improvement of operating efficiency of waste heat energy conversion systems with organic Rankine cycle," Energy, Elsevier, vol. 123(C), pages 460-472.
    2. Dong, Zhe & Zhang, Zuoyi & Dong, Yujie & Huang, Xiaojin, 2018. "Multi-layer perception based model predictive control for the thermal power of nuclear superheated-steam supply systems," Energy, Elsevier, vol. 151(C), pages 116-125.
    3. Yang, Tingting & Wang, Wei & Zeng, Deliang & Liu, Jizhen & Cui, Can, 2017. "Closed-loop optimization control on fan speed of air-cooled steam condenser units for energy saving and rapid load regulation," Energy, Elsevier, vol. 135(C), pages 394-404.
    4. Ponce, Carolina V. & Sáez, Doris & Bordons, Carlos & Núñez, Alfredo, 2016. "Dynamic simulator and model predictive control of an integrated solar combined cycle plant," Energy, Elsevier, vol. 109(C), pages 974-986.
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    Cited by:

    1. Trafczynski, Marian & Markowski, Mariusz & Urbaniec, Krzysztof, 2019. "Energy saving potential of a simple control strategy for heat exchanger network operation under fouling conditions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 111(C), pages 355-364.
    2. Dong, Zhe & Huang, Xiaojin & Dong, Yujie & Zhang, Zuoyi, 2020. "Multilayer perception based reinforcement learning supervisory control of energy systems with application to a nuclear steam supply system," Applied Energy, Elsevier, vol. 259(C).
    3. Jiří Jaromír Klemeš & Petar Sabev Varbanov & Paweł Ocłoń & Hon Huin Chin, 2019. "Towards Efficient and Clean Process Integration: Utilisation of Renewable Resources and Energy-Saving Technologies," Energies, MDPI, Open Access Journal, vol. 12(21), pages 1-32, October.
    4. Brage Rugstad Knudsen & Hanne Kauko & Trond Andresen, 2019. "An Optimal-Control Scheme for Coordinated Surplus-Heat Exchange in Industry Clusters," Energies, MDPI, Open Access Journal, vol. 12(10), pages 1-22, May.
    5. Oravec, Juraj & Horváthová, Michaela & Bakošová, Monika, 2020. "Energy efficient convex-lifting-based robust control of a heat exchanger," Energy, Elsevier, vol. 201(C).
    6. Oravec, Juraj & Bakošová, Monika & Galčíková, Lenka & Slávik, Michal & Horváthová, Michaela & Mészáros, Alajos, 2019. "Soft-constrained robust model predictive control of a plate heat exchanger: Experimental analysis," Energy, Elsevier, vol. 180(C), pages 303-314.


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