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Dynamic modeling and economic model predictive control of a liquid desiccant air conditioning

Author

Listed:
  • Jiang, Yuliang
  • Wang, Xinli
  • Zhao, Hongxia
  • Wang, Lei
  • Yin, Xiaohong
  • Jia, Lei

Abstract

As a good alternative to cooling-based air dehumidification, Liquid Desiccant Air Conditioning (LDAC) has drawn increasing attention for energy saving in large-scale public buildings. However, available investigations mostly pay attention to the dehumidification performance analysis and optimization based on steady-state models. Dynamic control issues for LDAC are rather important to the system performance and economic cost under the changing working conditions, such as the changing setting points of indoor air conditions and sensible/latent loads in occupied space. In this study, a dynamic model based on the Adaptive Neuro-Fuzzy Inference System (ANFIS) method is established to describe the system output air temperature and humidity ratio of LDAC dynamically with varied input conditions. The verification results show that a good agreement can be found between the prediction results from the proposed model and the experimental data with the relative errors less than 2% for outlet air temperature and less than 4% for outlet air humidity ratio, respectively. An EMPC strategy is designed based on the proposed dynamic model for LDAC by solving a receding optimization problem considering tracking error, control efforts rate, and energy consumption as the objective functions with Genetic Algorithm (GA) to get optimal dynamic response and improve the energy efficiency as well. The simulation analysis shows the proposed EMPC strategy outperforms Proportional–Integral (PI) control with smaller tracking error, faster response, and higher energy efficiency. The average energy saving by the proposed EMPC strategy can reach up to 9.5%.

Suggested Citation

  • Jiang, Yuliang & Wang, Xinli & Zhao, Hongxia & Wang, Lei & Yin, Xiaohong & Jia, Lei, 2020. "Dynamic modeling and economic model predictive control of a liquid desiccant air conditioning," Applied Energy, Elsevier, vol. 259(C).
  • Handle: RePEc:eee:appene:v:259:y:2020:i:c:s0306261919318616
    DOI: 10.1016/j.apenergy.2019.114174
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    References listed on IDEAS

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    Cited by:

    1. Jiang, Yuliang & Zhu, Shanying & Xu, Qimin & Yang, Bo & Guan, Xinping, 2023. "Hybrid modeling-based temperature and humidity adaptive control for a multi-zone HVAC system," Applied Energy, Elsevier, vol. 334(C).
    2. Homod, Raad Z. & Gaeid, Khalaf S. & Dawood, Suroor M. & Hatami, Alireza & Sahari, Khairul S., 2020. "Evaluation of energy-saving potential for optimal time response of HVAC control system in smart buildings," Applied Energy, Elsevier, vol. 271(C).
    3. Huang, Xianghui & Li, Kuining & Xie, Yi & Liu, Bin & Liu, Jiangyan & Liu, Zhaoming & Mou, Lunjie, 2022. "A novel multistage constant compressor speed control strategy of electric vehicle air conditioning system based on genetic algorithm," Energy, Elsevier, vol. 241(C).
    4. Gao, D.C. & Sun, Y.J. & Ma, Z. & Ren, H., 2021. "A review on integration and design of desiccant air-conditioning systems for overall performance improvements," Renewable and Sustainable Energy Reviews, Elsevier, vol. 141(C).
    5. Raman, Naren Srivaths & Devaprasad, Karthikeya & Chen, Bo & Ingley, Herbert A. & Barooah, Prabir, 2020. "Model predictive control for energy-efficient HVAC operation with humidity and latent heat considerations," Applied Energy, Elsevier, vol. 279(C).
    6. Luo, Jielin & Yang, Hongxing, 2022. "A state-of-the-art review on the liquid properties regarding energy and environmental performance in liquid desiccant air-conditioning systems," Applied Energy, Elsevier, vol. 325(C).
    7. Dai, Yuze & Liu, Feng & Sui, Jun & Wang, Dandan & Han, Wei & Jin, Hongguang, 2020. "Hybrid liquid desiccant air-conditioning system combined with marine aerosol removal driven by low-temperature heat source," Applied Energy, Elsevier, vol. 275(C).
    8. Raman, Naren Srivaths & Chen, Bo & Barooah, Prabir, 2022. "On energy-efficient HVAC operation with Model Predictive Control: A multiple climate zone study," Applied Energy, Elsevier, vol. 324(C).
    9. Yang, Hongxing & Shi, Wenchao & Chen, Yi & Min, Yunran, 2021. "Research development of indirect evaporative cooling technology: An updated review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).

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