An efficient method based on the uniformity principle for synthesis of large-scale heat exchanger networks. (25th August 2016)

• Record Type: Journal Article
• Authors: Zhang, Chunwei; Cui, Guomin; Chen, Shang
• Is Part Of: Applied thermal engineering. Volume 107(2016:Aug.)
• Page Start: 565
• Page End: 574
• Publication Date: 2016-08-25
• Subjects: Large-scale heat exchanger network -- Uniformity factor -- Powell method; Heat engineering -- Periodicals; Heating -- Equipment and supplies -- Periodicals; Periodicals; 621.40205
• DOI: 10.1016/j.applthermaleng.2016.07.009
• Languages: English
• ISSNs: 1359-4311

Abstract

Highlights: Two dimensionless uniformity factors are presented to heat exchange network. The grouping of process streams reduces the computational complexity of large-scale HENS problems. The optimal sub-network can be obtained by Powell particle swarm optimization algorithm. The method is illustrated by a case study involving 39 process streams, with a better solution. Abstract: 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.