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Research on the Prediction Method of Centrifugal Pump Performance Based on a Double Hidden Layer BP Neural Network

Author

Listed:
  • Wei Han

    (Department of Energy and Power Engineering, Lanzhou University of Technology, Lanzhou 730050, China
    Key Laboratory of Fluid Machinery and Systems, Lanzhou University of Technology, Lanzhou 730050, China)

  • Lingbo Nan

    (Department of Energy and Power Engineering, Lanzhou University of Technology, Lanzhou 730050, China)

  • Min Su

    (Department of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China)

  • Yu Chen

    (Department of Energy and Power Engineering, Lanzhou University of Technology, Lanzhou 730050, China)

  • Rennian Li

    (Department of Energy and Power Engineering, Lanzhou University of Technology, Lanzhou 730050, China
    Key Laboratory of Fluid Machinery and Systems, Lanzhou University of Technology, Lanzhou 730050, China)

  • Xuejing Zhang

    (Department of Architecture and Transportation Engineering, Guilin University of Electronic Technology, Guilin 541004, China)

Abstract

With the aim of improving the shortcomings of the traditional single hidden layer back propagation (BP) neural network structure and learning algorithm, this paper proposes a centrifugal pump performance prediction method based on the combination of the Levenberg–Marquardt (LM) training algorithm and double hidden layer BP neural network. MATLAB was used to establish a double hidden layer BP neural network prediction model to predict the head and efficiency of a centrifugal pump. The average relative error of the head between the experimental and prediction obtained by the double hidden layer BP neural network model was 4.35%, the average relative error of the model prediction efficiency and the experimental efficiency was 2.94%, and the convergence time was 1/42 of that of the single hidden layer. The double hidden layer BP neural network model effectively solves the problems of low learning efficiency and easy convergence into local minima—issues that were common in the traditional single hidden layer BP neural network training. Furthermore, the proposed model realizes hydraulic performance prediction during the design process of a centrifugal pump.

Suggested Citation

  • Wei Han & Lingbo Nan & Min Su & Yu Chen & Rennian Li & Xuejing Zhang, 2019. "Research on the Prediction Method of Centrifugal Pump Performance Based on a Double Hidden Layer BP Neural Network," Energies, MDPI, vol. 12(14), pages 1-14, July.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:14:p:2709-:d:248640
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    Citations

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    Cited by:

    1. 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.
    2. Ji-Quan Wang & Hong-Yu Zhang & Hao-Hao Song & Pan-Li Zhang & Jin-Ling Bei, 2022. "Prediction of Pork Supply Based on Improved Mayfly Optimization Algorithm and BP Neural Network," Sustainability, MDPI, vol. 14(24), pages 1-21, December.
    3. Jia Li & Xin Wang & Yue Wang & Wancheng Wang & Baibing Chen & Xiaolong Chen, 2020. "Effects of a Combination Impeller on the Flow Field and External Performance of an Aero-Fuel Centrifugal Pump," Energies, MDPI, vol. 13(4), pages 1-16, February.
    4. Xiongchao Lin & Wenshuai Xi & Jinze Dai & Caihong Wang & Yonggang Wang, 2020. "Prediction of Slag Characteristics Based on Artificial Neural Network for Molten Gasification of Hazardous Wastes," Energies, MDPI, vol. 13(19), pages 1-18, October.
    5. Lixin Wei & Yu Zhang & Lili Ji & Lin Ye & Xuanchen Zhu & Jin Fu, 2022. "Pressure Drop Prediction of Crude Oil Pipeline Based on PSO-BP Neural Network," Energies, MDPI, vol. 15(16), pages 1-12, August.
    6. Zhang, Yiming & Li, Jingxiang & Fei, Liangyu & Feng, Zhiyan & Gao, Jingzhou & Yan, Wenpeng & Zhao, Shengdun, 2023. "Operational performance estimation of vehicle electric coolant pump based on the ISSA-BP neural network," Energy, Elsevier, vol. 268(C).
    7. Eslam Mohammed Abdelkader & Nehal Elshaboury & Abobakr Al-Sakkaf, 2022. "On the Utilization of an Ensemble of Meta-Heuristics for Simulating Energy Consumption in Buildings," International Journal of Applied Metaheuristic Computing (IJAMC), IGI Global, vol. 13(1), pages 1-31, January.
    8. Min Yi & Wei Xie & Li Mo, 2021. "Short-Term Electricity Price Forecasting Based on BP Neural Network Optimized by SAPSO," Energies, MDPI, vol. 14(20), pages 1-17, October.
    9. Grzegorz Filo, 2023. "Artificial Intelligence Methods in Hydraulic System Design," Energies, MDPI, vol. 16(8), pages 1-19, April.

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