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.
引用
收藏
页码：801 / 813
页数：12
相关论文
共 35 条
  • [11] Lotfi R, Boozarjomehry R B., Superstructure optimization in heat exchanger network (HEN) synthesis using modular simulators and a genetic algorithm framework, Industrial & Engineering Chemistry Research, 49, 10, pp. 4731-4737, (2010)
  • [12] Silva A P, Ravagnani M A S S, Biscaia E C., Particle swarm optimisation applied in retrofit of heat exchanger networks, Computer Aided Chemical Engineering, 27, pp. 1035-1040, (2009)
  • [13] Chen S, Luo N., Adaptive competitive swarm optimization for heat exchanger networks without split streams, CIESC Journal, 67, 11, pp. 4716-4723, (2016)
  • [14] Zhang C W, Cui G M, Peng F Y., A novel hybrid chaotic ant swarm algorithm for heat exchanger networks synthesis, Applied Thermal Engineering, 104, pp. 707-719, (2016)
  • [15] Yerramsetty K M, Murty C V S., Synthesis of cost-optimal heat exchanger networks using differential evolution, Computers & Chemical Engineering, 32, 8, pp. 1861-1876, (2008)
  • [16] Yu H M, Fang H P, Yao P J, Et al., A combined genetic algorithm/simulated annealing algorithm for large scale system energy integration, Computers & Chemical Engineering, 24, 8, pp. 2023-2035, (2000)
  • [17] Santos L F, Costa C B B, Caballero J A, Et al., Synthesis and optimization of work and heat exchange networks using an MINLP model with a reduced number of decision variables, Applied Energy, 262, (2020)
  • [18] Xiao Y, Cui G M., A novel random walk algorithm with compulsive evolution for heat exchanger network synthesis, Applied Thermal Engineering, 115, pp. 1118-1127, (2017)
  • [19] Kang L X, Jiang N, Xia M X, Et al., Parallel genetic algorithm for design of HEN based on OpenMP system, Journal of Chemical Engineering of Chinese Universities, 30, 2, pp. 431-438, (2016)
  • [20] Pavao L V, Costa C B B, Ravagnani M A D S S., Automated heat exchanger network synthesis by using hybrid natural algorithms and parallel processing, Computers & Chemical Engineering, 94, pp. 370-386, (2016)
1234