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A novel random walk algorithm with compulsive evolution combined with an optimum-protection strategy for heat exchanger network synthesis

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  • Bao, Zhongkai
  • Cui, Guoming
  • Chen, Jiaxing
  • Sun, Tao
  • Xiao, Yuan

Abstract

Random walk algorithm with compulsive evolution is a novel stochastic method with strong global search ability for heat exchanger network synthesis; however, its mutation behavior of accepting bad solutions might substitute excellent solutions with bad ones and consequently cost-optimal structures cannot be guaranteed. Therefore, an optimum-protection strategy is proposed to protect and exploit excellent solutions. In the presented method, a basic population is set to generate numerous candidate solutions based on the evolution principle of original algorithm, where the excellent solutions including current optimums and pseudo optimums are delivered to a protective population. For higher convergence precision, a dimensionality-reduction random walk technique is designed for the protective population to perform a complete local optimization for the protected solutions. The presented method consisting of two populations can maintain the normal evolution of original algorithm and exploit the potentialities of the excellent solutions, which can satisfy the needs of global and local search abilities. Moreover, a leader–follower optimization technique is presented to reduce computational time when considering stream splits. Five different-sized cases available in the literature are systematically examined and some more economical solutions compared to the reported ones are found within reasonable time.

Suggested Citation

  • Bao, Zhongkai & Cui, Guoming & Chen, Jiaxing & Sun, Tao & Xiao, Yuan, 2018. "A novel random walk algorithm with compulsive evolution combined with an optimum-protection strategy for heat exchanger network synthesis," Energy, Elsevier, vol. 152(C), pages 694-708.
    • Handle: RePEc:eee:energy:v:152:y:2018:i:c:p:694-708
    DOI: 10.1016/j.energy.2018.03.170
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    References listed on IDEAS

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    1. Xu, Yun-Chao & Chen, Qun & Guo, Zeng-Yuan, 2015. "Entransy dissipation-based constraint for optimization of heat exchanger networks in thermal systems," Energy, Elsevier, vol. 86(C), pages 696-708.
    2. Liu, Pu & Cui, Guomin & Xiao, Yuan & Chen, Jiaxing, 2018. "A new heuristic algorithm with the step size adjustment strategy for heat exchanger network synthesis," Energy, Elsevier, vol. 143(C), pages 12-24.
    3. Gadalla, Mamdouh A., 2015. "A new graphical method for Pinch Analysis applications: Heat exchanger network retrofit and energy integration," Energy, Elsevier, vol. 81(C), pages 159-174.
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    Citations

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    Cited by:

    1. Jiaxing Chen & Guomin Cui & Mei Cao & Heri Kayange & Jian Li, 2021. "Heat Exchanger Network Optimization Based on the Participatory Evolution Strategy for Streams," Energies, MDPI, vol. 14(24), pages 1-17, December.
    2. Liu, Liuchen & Cui, Guomin & Chen, Jiaxing & Huang, Xiaohuang & Li, Di, 2022. "Two-stage superstructure model for optimization of distributed energy systems (DES) part I: Model development and verification," Energy, Elsevier, vol. 245(C).
    3. Wang, Bohong & Klemeš, Jiří Jaromír & Li, Nianqi & Zeng, Min & Varbanov, Petar Sabev & Liang, Yongtu, 2021. "Heat exchanger network retrofit with heat exchanger and material type selection: A review and a novel method," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    4. Kayange, Heri Ambonisye & Cui, Guomin & Xu, Yue & Li, Jian & Xiao, Yuan, 2020. "Non-structural model for heat exchanger network synthesis allowing for stream splitting," Energy, Elsevier, vol. 201(C).
    5. Orosz, Ákos & Friedler, Ferenc, 2020. "Multiple-solution heat exchanger network synthesis for enabling the best industrial implementation," Energy, Elsevier, vol. 208(C).
    6. Xiao, Wu & Wang, Kaifeng & Jiang, Xiaobin & Li, Xiangcun & Wu, Xuemei & Hao, Ze & He, Gaohong, 2019. "Simultaneous optimization strategies for heat exchanger network synthesis and detailed shell-and-tube heat-exchanger design involving phase changes using GA/SA," Energy, Elsevier, vol. 183(C), pages 1166-1177.

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