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A New Model Predictive Control Method for Eliminating Hydraulic Oscillation and Dynamic Hydraulic Imbalance in a Complex Chilled Water System

Author

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  • Yang Yuan

    (School of Environmental Science and Engineering, Tianjin University, Tianjin 300350, China
    China Academy of Building Research, Beijing 100013, China)

  • Neng Zhu

    (School of Environmental Science and Engineering, Tianjin University, Tianjin 300350, China)

  • Haizhu Zhou

    (China Academy of Building Research, Beijing 100013, China)

  • Hai Wang

    (China Academy of Building Research, Beijing 100013, China)

Abstract

To enhance the energy performance of a central air-conditioning system, an effective control method for the chilled water system is always essential. However, it is a real challenge to distribute exact cooling energy to multiple terminal units in different floors via a complex chilled water network. To mitigate hydraulic imbalance in a complex chilled water system, many throttle valves and variable-speed pumps are installed, which are usually regulated by PID-based controllers. Due to the severe hydraulic coupling among the valves and pumps, the hydraulic oscillation phenomena often occur while using those feedback-based controllers. Based on a data-calibrated water distribution model which can accurately predict the hydraulic behaviors of a chilled water system, a new Model Predictive Control (MPC) method is proposed in this study. The proposed method is validated by a real-life chilled water system in a 22-floor hotel. By the proposed method, the valves and pumps can be regulated safely without any hydraulic oscillations. Simultaneously, the hydraulic imbalance among different floors is also eliminated, which can save 23.3% electricity consumption of the pumps.

Suggested Citation

  • Yang Yuan & Neng Zhu & Haizhu Zhou & Hai Wang, 2021. "A New Model Predictive Control Method for Eliminating Hydraulic Oscillation and Dynamic Hydraulic Imbalance in a Complex Chilled Water System," Energies, MDPI, vol. 14(12), pages 1-23, June.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:12:p:3608-:d:576632
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    References listed on IDEAS

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    1. Candanedo, J.A. & Dehkordi, V.R. & Stylianou, M., 2013. "Model-based predictive control of an ice storage device in a building cooling system," Applied Energy, Elsevier, vol. 111(C), pages 1032-1045.
    2. Shan, Kui & Fan, Cheng & Wang, Jiayuan, 2019. "Model predictive control for thermal energy storage assisted large central cooling systems," Energy, Elsevier, vol. 179(C), pages 916-927.
    3. Kusiak, Andrew & Li, Mingyang & Zhang, Zijun, 2010. "A data-driven approach for steam load prediction in buildings," Applied Energy, Elsevier, vol. 87(3), pages 925-933, March.
    4. Bianchini, Gianni & Casini, Marco & Pepe, Daniele & Vicino, Antonio & Zanvettor, Giovanni Gino, 2019. "An integrated model predictive control approach for optimal HVAC and energy storage operation in large-scale buildings," Applied Energy, Elsevier, vol. 240(C), pages 327-340.
    5. Horowitz, Shira & Mauch, Brandon & Sowell, Fallaw, 2014. "Forecasting residential air conditioning loads," Applied Energy, Elsevier, vol. 132(C), pages 47-55.
    6. Afroz, Zakia & Shafiullah, GM & Urmee, Tania & Higgins, Gary, 2018. "Modeling techniques used in building HVAC control systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 83(C), pages 64-84.
    7. Tashtoush, Bourhan & Molhim, M. & Al-Rousan, M., 2005. "Dynamic model of an HVAC system for control analysis," Energy, Elsevier, vol. 30(10), pages 1729-1745.
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