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Multi-objective optimization and loss analysis of multistage centrifugal pumps

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  • Wu, TianXin
  • Wu, DengHao
  • Gao, ShuYu
  • Song, Yu
  • Ren, Yun
  • Mou, JieGang

Abstract

Multistage centrifugal pumps are widely used, and improving their efficiency is an indispensable part of energy conservation. A multi-objective optimization method combining experimental design, surrogate model, and optimization algorithm is proposed to re-design impellers and diffusers for improving pump performance. In the paper, nine variables were selected by the Plackett–Burman design with the head and minimum efficiency index (MEI) as the optimization objectives. The Gaussian process regression (GPR) was used to establish the surrogate model, and multi-objective optimization of the impeller and diffuser was carried out by non-dominated sorting genetic algorithm II (NSGA-II). The optimization results show that head and efficiency at the designed point 1.0Qd increased by 8.8% and 2.8% respectively, and CMEI decreased by 1.34%. Meanwhile, the energy loss and flow characteristics of the original and optimization models were analyzed with the entropy production theory. Compared with the original model, the energy loss was reduced, and the flow in the interaction area between the impeller and diffuser becomes more stable for the optimized model. Moreover, the influencing mechanism of the pump geometrical parameters on the hydraulic performance and flow characteristics were discussed and analyzed.

Suggested Citation

  • Wu, TianXin & Wu, DengHao & Gao, ShuYu & Song, Yu & Ren, Yun & Mou, JieGang, 2023. "Multi-objective optimization and loss analysis of multistage centrifugal pumps," Energy, Elsevier, vol. 284(C).
    • Handle: RePEc:eee:energy:v:284:y:2023:i:c:s0360544223020327
    DOI: 10.1016/j.energy.2023.128638
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    References listed on IDEAS

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    1. Cui, Yunfei & Geng, Zhiqiang & Zhu, Qunxiong & Han, Yongming, 2017. "Review: Multi-objective optimization methods and application in energy saving," Energy, Elsevier, vol. 125(C), pages 681-704.
    2. Zhou, Ling & Hang, Jianwei & Bai, Ling & Krzemianowski, Zbigniew & El-Emam, Mahmoud A. & Yasser, Eman & Agarwal, Ramesh, 2022. "Application of entropy production theory for energy losses and other investigation in pumps and turbines: A review," Applied Energy, Elsevier, vol. 318(C).
    3. Ahmadi, Mohammad H. & Hosseinzade, Hadi & Sayyaadi, Hoseyn & Mohammadi, Amir H. & Kimiaghalam, Farshad, 2013. "Application of the multi-objective optimization method for designing a powered Stirling heat engine: Design with maximized power, thermal efficiency and minimized pressure loss," Renewable Energy, Elsevier, vol. 60(C), pages 313-322.
    4. Konak, Abdullah & Coit, David W. & Smith, Alice E., 2006. "Multi-objective optimization using genetic algorithms: A tutorial," Reliability Engineering and System Safety, Elsevier, vol. 91(9), pages 992-1007.
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    1. Su, Zixiang & Yang, Liu & Wang, Hao & Song, Jianzhong & Jiang, Weixue, 2024. "Exergoenvironmental optimization and thermoeconomic assessment of an innovative multistage Brayton cycle with dual expansion and cooling for ultra-high temperature solar power," Energy, Elsevier, vol. 286(C).

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