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Layer pattern thermal design and optimization for multistream plate-fin heat exchangers-A review

Journal

RENEWABLE & SUSTAINABLE ENERGY REVIEWS
Volume 53, Issue -, Pages 500-514

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.rser.2015.09.003

Keywords

Layer pattern; Multistream plate-fin heat exchangers; Design criterion and evaluation; Optimization design; Intelligent algorithm

Funding

  1. Funds of National Key Technology R&D Program of China [2012BAA08B03]
  2. National Natural Science Foundation of China [51376142]
  3. Specialized Research Fund for the Doctoral Program of University by MOE China [20130201110069]

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Cryogenic processes involve air separation and liquefaction. To reduce energy consumption in these processes, many compact and efficient equipments that can handle fluid heat transfer have been developed. Of these, a multistream plate-fin heat exchanger is one of the best solutions. Studies of plate-fin heat exchangers are currently focusing on four areas: heat transfer calculation, surface analysis, flow resistance and design optimization. It is also important to optimize the layer patterns design of multistream plate-fin heat exchangers. Several techniques have been proposed for this purpose thus far; however, most of these are based on qualitative or trial-and-error approaches. Therefore, no universally accepted methodology exists designing for the layer pattern of multistream plate-fin heat exchangers. This article starts by reviewing traditional design approaches for multistream plate-fin heat exchangers and then focuses on the development of layer pattern design methods. It highlights three types of thermal design and evaluation criteria. It then discusses some suggestions and new methods for the optimization of the layer pattern design that have emerged in recent years. Further, newly emerging intelligent heuristic algorithms for optimizing the layer pattern thermal design are discussed. In addition to these basic design methodologies, the layer pattern ring model and dual objective function optimization methods developed by the author's research team are discussed. Finally, the status of this research area is summarized, and emerging trends are noted. (C) 2015 Elsevier Ltd. All rights reserved.

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