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An efficient method based on the uniformity principle for synthesis of large-scale heat exchanger networks

机译:一种基于均匀性原理的大规模换热网络综合方法

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The optimal design of large-scale heat exchanger networks is a difficult task due to the inherent non-linear characteristics and the combinatorial nature of heat exchangers. To solve large-scale heat exchanger network synthesis (HENS) problems, two dimensionless uniformity factors to describe the heat exchanger network (HEN) uniformity in terms of the temperature difference and the accuracy of process stream grouping are deduced. Additionally, a novel algorithm that combines deterministic and stochastic optimizations to obtain an optimal sub-network with a suitable heat load for a given group of streams is proposed, and is named the Powell particle swarm optimization (PPSO). As a result, the synthesis of large-scale heat exchanger networks is divided into two corresponding sub-parts, namely, the grouping of process streams and the optimization of sub-networks. This approach reduces the computational complexity and increases the efficiency of the proposed method. The robustness and effectiveness of the proposed method are demonstrated by solving a large-scale HENS problem involving 39 process streams, and the results obtained are better than those previously published in the literature. (C) 2016 Elsevier Ltd. All rights reserved.
机译:由于热交换器固有的非线性特性和组合特性,对大型热交换器网络进行优化设计是一项艰巨的任务。为了解决大规模的换热网络综合问题,推导了两个无量纲的均匀性因子来描述换热网络的均匀性,即温度差和工艺流分组的准确性。此外,提出了一种新颖的算法,该算法结合了确定性和随机性优化功能,以针对给定的一组流获得具有合适热负荷的最优子网,并将其称为Powell粒子群优化(PPSO)。结果,大型换热器网络的合成被分为两个相应的子部分,即过程流的分组和子网络的优化。这种方法降低了计算复杂度并提高了所提出方法的效率。通过解决涉及39个过程流的大规模HENS问题,证明了所提方法的鲁棒性和有效性,并且所获得的结果比以前在文献中发表的结果要好。 (C)2016 Elsevier Ltd.保留所有权利。

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