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AI-assisted maldistribution minimization of membrane-based heat/mass exchangers for compact absorption cooling

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  • Sui, Zengguang
  • Wu, Wei

Abstract

Flow maldistribution has been a major challenge for heat/mass exchangers, which is a particular concern in compact membrane-based absorbers used in absorption refrigeration systems driven by renewable/waste energy. Herein, we construct an artificial intelligence (AI) tool coupling a 3D CFD model, a discrete model, and an optimization algorithm for the development of highly efficient and compact plate-and-frame membrane-based absorbers (PFMAs). In the AI-assisted tool, CFD simulations demonstrate that the PFMA suffers from more severe flow maldistribution as the number of channels increases. The average absorption rate is decreased by 21.44% as the number of channels increases from 5 to 21. The heat and mass transfer performance of the 5-channel and 21-channel models is reduced by 3% and 22%, respectively. Meanwhile, a simple and universal discrete model is developed and validated to predict the flow distribution in PFMAs, with a maximum deviation of 10.18%. To minimize the flow maldistribution, an optimization structure with a uniform distributed flow field is determined by developing and coupling a rapid optimization algorithm. After optimization, a reduction of about 10 times in the flow maldistribution can be achieved, and the heat and mass transfer performance deterioration caused by the flow maldistribution can be minimized to about 1%.

Suggested Citation

  • Sui, Zengguang & Wu, Wei, 2023. "AI-assisted maldistribution minimization of membrane-based heat/mass exchangers for compact absorption cooling," Energy, Elsevier, vol. 263(PC).
    • Handle: RePEc:eee:energy:v:263:y:2023:i:pc:s0360544222028080
    DOI: 10.1016/j.energy.2022.125922
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    References listed on IDEAS

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