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Approaching a modified adaptive swarm intelligence to energy efficiency enhancement of an inline pump

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  • Gan, Xingcheng
  • Xu, Yuanhui
  • Pei, Ji
  • Wang, Wenjie
  • Yuan, Shouqi

Abstract

Pumps, as widely used general-purpose machinery, consume significant amounts of energy, making improvements in their operational efficiency and stability critically important. However, current mainstream intelligent optimization methods are limited by prediction accuracy and algorithm performance. This study proposes an adaptive improved particle swarm optimization algorithm with rapid convergence capabilities, integrating population classification, adaptive acceleration strategies, and self-learning mechanisms. Combined with computational fluid dynamics, the algorithm was applied to directly optimize a vertical inline pump with a specific speed of 107. The study performed statistical correlation analysis on 4000 design samples generated during the optimization process and utilized flow field loss visualization techniques to explore the causes of performance improvements. The results indicate that the proposed improved algorithm achieved approximately a 6 % performance improvement within 25 iterations and stabilized within 85 iterations. Experimental validation confirmed the enhanced performance of the optimized model, with a 10.71 % increase in nominal efficiency, a 5.09 % increase in head, and an improvement in Minimum Efficiency Index (MEI) from 0.39 to 1.09, indicating significant expansion of the high-efficiency operational range. The method proposed in this study significantly improves the optimization efficiency and accuracy for multi-parameter centrifugal pumps, providing theoretical support and technical solutions for high-efficiency and high-stability hydraulic designs.

Suggested Citation

  • Gan, Xingcheng & Xu, Yuanhui & Pei, Ji & Wang, Wenjie & Yuan, Shouqi, 2025. "Approaching a modified adaptive swarm intelligence to energy efficiency enhancement of an inline pump," Energy, Elsevier, vol. 325(C).
  • Handle: RePEc:eee:energy:v:325:y:2025:i:c:s0360544225018493
    DOI: 10.1016/j.energy.2025.136207
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    References listed on IDEAS

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    1. Xingcheng Gan & Wenjie Wang & Ji Pei & Shouqi Yuan & Yajing Tang & Majeed Koranteng Osman, 2020. "Direct Shape Optimization and Parametric Analysis of a Vertical Inline Pump via Multi-Objective Particle Swarm Optimization," Energies, MDPI, vol. 13(2), pages 1-18, January.
    2. Zhao, Jiantao & Pei, Ji & Wang, Wenjie & Gan, Xingcheng, 2024. "Blade redesign based on inverse design method for energy performance improvement and hydro-induced vibration suppression of a multi-stage centrifugal pump," Energy, Elsevier, vol. 308(C).
    3. Wang, Wenjie & Guo, Hailong & Zhang, Chenying & Shen, Jiawei & Pei, Ji & Yuan, Shouqi, 2023. "Transient characteristics of PAT in micro pumped hydro energy storage during abnormal shutdown process," Renewable Energy, Elsevier, vol. 209(C), pages 401-412.
    4. Gu, Yandong & Pei, Ji & Yuan, Shouqi & Wang, Wenjie & Zhang, Fan & Wang, Peng & Appiah, Desmond & Liu, Yong, 2019. "Clocking effect of vaned diffuser on hydraulic performance of high-power pump by using the numerical flow loss visualization method," Energy, Elsevier, vol. 170(C), pages 986-997.
    5. Abdelaziz, E.A. & Saidur, R. & Mekhilef, S., 2011. "A review on energy saving strategies in industrial sector," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(1), pages 150-168, January.
    6. Garg, Harish, 2016. "A hybrid PSO-GA algorithm for constrained optimization problems," Applied Mathematics and Computation, Elsevier, vol. 274(C), pages 292-305.
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