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A global optimized operation strategy for energy savings in liquid desiccant air conditioning using self-adaptive differential evolutionary algorithm

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  • Wang, Xinli
  • Cai, Wenjian
  • Yin, Xiaohong

Abstract

This study proposes a global optimized operation strategy to reduce energy consumption of a liquid desiccant air conditioning (LDAC) driven by chiller and electric heater. Energy models of chiller, electric heater, pumps and fans are developed to predict their energy consumptions under different operating conditions with different control settings. Heat transfer models of cooling heat exchanger, heating heat exchanger and recovery heat exchanger are established to analyze the heat transfer processes in these components. An optimization problem considering system constraints and interactions between components is built to optimize the energy usage of the whole liquid desiccant air conditioning and simultaneously maintaining the required indoor air quality (IAQ) level. Nine controllable variables related to the performance and energy usage of LDAC are selected as control settings. Self-adaptive differential evolutionary (SADE) algorithm with fast convergence rate is employed to solve the optimization problem to obtain optimal control settings and to develop optimal operation strategies. Compare study is carried out on a fabricated testing facility to show the energy saving performance of the proposed global optimized operation strategy. Compared with the conventional strategy, 18.5% energy saving can be achieved by using the proposed global optimized operation strategy. The proposed global optimized operation strategy is a valid operation strategy that is suitable for application in energy reduction of the existing LDAC system in building.

Suggested Citation

  • Wang, Xinli & Cai, Wenjian & Yin, Xiaohong, 2017. "A global optimized operation strategy for energy savings in liquid desiccant air conditioning using self-adaptive differential evolutionary algorithm," Applied Energy, Elsevier, vol. 187(C), pages 410-423.
    • Handle: RePEc:eee:appene:v:187:y:2017:i:c:p:410-423
    DOI: 10.1016/j.apenergy.2016.11.073
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    Cited by:

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    3. Juan Prieto & Antonio Atienza-Márquez & Alberto Coronas, 2021. "Modeling and Dynamic Simulation of a Hybrid Liquid Desiccant System with Non-Adiabatic Falling-Film Air-Solution Contactors for Air Conditioning Applications in Buildings," Energies, MDPI, vol. 14(2), pages 1-20, January.
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    5. Wang, Yingying & Fan, Ying & Wang, Dengjia & Liu, Yanfeng & Qiu, Zhenghao & Liu, Jiaping, 2020. "Optimization of the areas of solar collectors and photovoltaic panels in liquid desiccant air-conditioning systems using solar energy in isolated low-latitude islands," Energy, Elsevier, vol. 198(C).
    6. Zhang, Ning & Yin, Shao-You & Li, Min, 2018. "Model-based optimization for a heat pump driven and hollow fiber membrane hybrid two-stage liquid desiccant air dehumidification system," Applied Energy, Elsevier, vol. 228(C), pages 12-20.
    7. Ding, Yan & Wang, Qiaochu & Kong, Xiangfei & Yang, Kun, 2019. "Multi-objective optimisation approach for campus energy plant operation based on building heating load scenarios," Applied Energy, Elsevier, vol. 250(C), pages 1600-1617.
    8. 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).
    9. Zhang, Qunli & Li, Yanxin & Zhang, Qiuyue & Ma, Fengge & Lü, Xiaoshu, 2024. "Application of deep dehumidification technology in low-humidity industry: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 193(C).
    10. Su, Bosheng & Han, Wei & Sui, Jun & Jin, Hongguang, 2017. "A two-stage liquid desiccant dehumidification system by the cascade utilization of low-temperature heat for industrial applications," Applied Energy, Elsevier, vol. 207(C), pages 643-653.

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