AI-assisted maldistribution minimization of membrane-based heat/mass exchangers for compact absorption cooling. (15th January 2023)

• Record Type: Journal Article
• Authors: Sui, Zengguang; Wu, Wei
• Is Part Of: Energy. Volume 263:Part C(2023)
• Publication Date: 2023-01-15
• Subjects: Renewable/waste energy -- Membrane-based absorbers -- AI-Assisted tool -- Flow maldistribution -- Discrete model -- Heat and mass transfer performance; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2022.125922
• Languages: English
• ISSNs: 0360-5442

Abstract

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%. Highlights: Flow maldistribution of membrane-based absorbers used in ARSs is studied. AI-assisted tool to predict and minimize flow maldistribution is developed. A reduction of about 10 times in flow maldistribution can be achieved. Relative cooling power is kept at about 0.99 after optimization.