A hybrid algorithm based on parallel computing for heat exchanger network optimization with stream splits

被引：0
|
作者：
Zhou Z. [1 ,2 ]
Cui G. [1 ,2 ]
Yang L. [1 ,2 ]
Ma X. [1 ,2 ]
Xiao Y. [1 ,2 ]
Yang Q. [1 ,2 ]
机构：
[1] School of Energy and Power Engineering, University of Shanghai for Science and Technology, Shanghai
[2] Shanghai Key Laboratory of Multiphase Flow and Heat Transfer in Power Engineering, Shanghai
来源：
Huagong Xuebao/CIESC Journal | 2022年 / 73卷 / 02期
关键词：
Heat exchanger network; Hybrid algorithm; Multithreading; Optimization; Parallel computing;
D O I：
10.11949/0438-1157.20210909
中图分类号：
学科分类号：
摘要：
Heat exchange network optimization is a research difficulty in the field of chemical process system. Its mathematical model is highly nonconvex and nonlinear, so it often has limitations when using a single heuristic algorithm. The objective of the research is to minimize the annual cost of heat exchange network. In order to solve the problem of individual independent evolution and lack of communication between individuals when random walk algorithm compulsive evolution (RWCE) is used to optimize heat exchanger network, a hybrid algorithm with genetic algorithm (GA) and RWCE is proposed. The mixed algorithm maintains the individual evolution of the individuals in the first half of the dominant population, and replaces the inferior population by generating offspring through periodic crossover and mutation operations, thereby enhancing the original algorithm's ability to optimize integer variables. It makes up for the lack of renewal of vulnerable individuals. In order to improve the computational efficiency when optimizing heat exchanger network with splits under large population and save cost of time, the parallel design of the hybrid algorithm is realized by OpenMP system. The parallel hybrid algorithm is verified by three heat exchange network problems of different scales. The results show that the algorithm can greatly shorten the calculation time compared with the serial algorithm on the premise of effectively improving the optimization quality, and two of the examples have broken through the current optimal solution in the literature.
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页码：801 / 813
页数：12
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