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Model-based optimization strategy of chiller driven liquid desiccant dehumidifier with genetic algorithm

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  • Wang, Xinli
  • Cai, Wenjian
  • Lu, Jiangang
  • Sun, Youxian
  • Zhao, Lei

Abstract

This study presents a model-based optimization strategy for an actual chiller driven dehumidifier of liquid desiccant dehumidification system operating with lithium chloride solution. By analyzing the characteristics of the components, energy predictive models for the components in the dehumidifier are developed. To minimize the energy usage while maintaining the outlet air conditions at the pre-specified set-points, an optimization problem is formulated with an objective function, the constraints of mechanical limitations and components interactions. Model-based optimization strategy using genetic algorithm is proposed to obtain the optimal set-points for desiccant solution temperature and flow rate, to minimize the energy usage in the dehumidifier. Experimental studies on an actual system are carried out to compare energy consumption between the proposed optimization and the conventional strategies. The results demonstrate that energy consumption using the proposed optimization strategy can be reduced by 12.2% in the dehumidifier operation.

Suggested Citation

  • Wang, Xinli & Cai, Wenjian & Lu, Jiangang & Sun, Youxian & Zhao, Lei, 2015. "Model-based optimization strategy of chiller driven liquid desiccant dehumidifier with genetic algorithm," Energy, Elsevier, vol. 82(C), pages 939-948.
    • Handle: RePEc:eee:energy:v:82:y:2015:i:c:p:939-948
    DOI: 10.1016/j.energy.2015.01.103
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    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. Ou, Xianhua & Cai, Wenjian & He, Xiongxiong & Zhai, Deqing, 2018. "Experimental investigations on heat and mass transfer performances of a liquid desiccant cooling and dehumidification system," Applied Energy, Elsevier, vol. 220(C), pages 164-175.
    8. Li, Fan & Sun, Bo & Zhang, Chenghui & Liu, Che, 2019. "A hybrid optimization-based scheduling strategy for combined cooling, heating, and power system with thermal energy storage," Energy, Elsevier, vol. 188(C).
    9. Daghigh, Roonak & Arshad, Siamand Azizi & Ensafjoee, Koosha & Hajialigol, Najmeh, 2024. "A data-driven model for a liquid desiccant regenerator equipped with an evacuated tube solar collector: Random forest regression, support vector regression and artificial neural network," Energy, Elsevier, vol. 295(C).

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