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Energy Performance Curves Prediction of Centrifugal Pumps Based on Constrained PSO-SVR Model

Author

Listed:
  • Huican Luo

    (College of Metrology and Measurement Engineering, China Jiliang University, Hangzhou 310018, China)

  • Peijian Zhou

    (College of Metrology and Measurement Engineering, China Jiliang University, Hangzhou 310018, China
    Zhejiang Engineering Research Center of Smart Fluid Equipment & Measurement and Control Technology, Hangzhou 310018, China)

  • Lingfeng Shu

    (Power China Huadong Engineering Corporation Limited, Hangzhou 311122, China)

  • Jiegang Mou

    (College of Metrology and Measurement Engineering, China Jiliang University, Hangzhou 310018, China
    Zhejiang Engineering Research Center of Smart Fluid Equipment & Measurement and Control Technology, Hangzhou 310018, China)

  • Haisheng Zheng

    (College of Metrology and Measurement Engineering, China Jiliang University, Hangzhou 310018, China)

  • Chenglong Jiang

    (College of Metrology and Measurement Engineering, China Jiliang University, Hangzhou 310018, China)

  • Yantian Wang

    (College of Metrology and Measurement Engineering, China Jiliang University, Hangzhou 310018, China)

Abstract

It is of great significance to predict the energy performance of centrifugal pumps for the improvement of the pump design. However, the complex internal flow always affects the performance prediction of centrifugal pumps, particularly under low-flow operating conditions. Relying on the data-fitting method, a multi-condition performance prediction method for centrifugal pumps is proposed, where the performance relationship is incorporated into the particle swarm optimization algorithm, and the prediction model is optimized by automatically meeting the performance constraints. Compared with the experimental results, the performance under multiple operating conditions is well predicted by introducing performance constraints with the mean absolute relative error (MARE) for the head, power and efficiency of 0.85%, 1.53%,1.15%, respectively. By comparing the extreme gradient boosting and support vector regression models, the support vector regression is more suitable for the prediction of performance curves. Finally, by introducing performance constraints, the proposed model demonstrates a dramatic decrease in the head, power and efficiency of MARE by 98.64%, 82.06%, and 85.33%, respectively, when compared with the BP neural network.

Suggested Citation

  • Huican Luo & Peijian Zhou & Lingfeng Shu & Jiegang Mou & Haisheng Zheng & Chenglong Jiang & Yantian Wang, 2022. "Energy Performance Curves Prediction of Centrifugal Pumps Based on Constrained PSO-SVR Model," Energies, MDPI, vol. 15(9), pages 1-19, May.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:9:p:3309-:d:807267
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    References listed on IDEAS

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    Cited by:

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