A state-of-art review of dew point evaporative cooling technology and integrated applications

Xin Xiao and Jinjin Liu

Renewable and Sustainable Energy Reviews, 2024, vol. 191, issue C

Abstract: Energy consumption of air conditioning accounts for a large proportion of energy consumption of buildings, and it is indispensable to reduce the operational cost of air conditioning. Indirect evaporative cooling (IEC), especially dew point evaporative cooling (DPEC) technology, which takes away heat through water evaporation for cooling, becomes an effective technology to improve energy efficiency. In this review, the indirect DPEC technology is introduced and the current research states are summarized in detail. The improvements of performance and model analysis of DPEC are investigated by numerical simulations, experimental works and machine learnings, which are elaborately described respectively. DPEC can reduce the air temperature to the dew point to achieve higher cooling effectiveness than the conventional IEC. The impacts of the design, intake conditions, geometry and water distribution, membrane materials on the performances of DPEC heat exchanger are also explored, including the wet and dry-bulb efficiencies, cooling capacity, coefficient of performance and other parameters to evaluate the performances. Based on the experimental researches, the mathematical models with different theories such as finite volume method, enthalpy method, and other methods are built, which are based on Energyplus, Matlab, Fluent and other tools. DPEC is suitable for hot and dry regions. The applications of DPEC in hot and humid region are extended by integrated with dehumidifier to meet requirements of moisture removal. The optimal analyses of machine learning for the combination mode of DPEC and hybrid system are described subsequently. Finally, the future research directions and application occasions of DPEC are proposed.

Keywords: Indirect evaporative cooling; Dew point temperature; Structural design; Systematic performance; Numerical analysis; Machine learning (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1016/j.rser.2023.114142

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