IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i9p3309-d807267.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/9/3309/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/9/3309/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Bozorgasareh, Hamidreza & Khalesi, Javad & Jafari, Mohammad & Gazori, Heshmat Olah, 2021. "Performance improvement of mixed-flow centrifugal pumps with new impeller shrouds: Numerical and experimental investigations," Renewable Energy, Elsevier, vol. 163(C), pages 635-648.
    2. Tahani, Mojtaba & Kandi, Ali & Moghimi, Mahdi & Houreh, Shahram Derakhshan, 2020. "Rotational speed variation assessment of centrifugal pump-as-turbine as an energy utilization device under water distribution network condition," Energy, Elsevier, vol. 213(C).
    3. Liu, Ming & Tan, Lei & Cao, Shuliang, 2019. "Theoretical model of energy performance prediction and BEP determination for centrifugal pump as turbine," Energy, Elsevier, vol. 172(C), pages 712-732.
    4. Gijsbert Toet & Jack Johnson & John Montague & Ken Torres & José Garcia-Bravo, 2019. "The Determination of the Theoretical Stroke Volume of Hydrostatic Positive Displacement Pumps and Motors from Volumetric Measurements," Energies, MDPI, vol. 12(3), pages 1-15, January.
    5. Mariana Simão & Helena M. Ramos, 2019. "Micro Axial Turbine Hill Charts: Affinity Laws, Experiments and CFD Simulations for Different Diameters," Energies, MDPI, vol. 12(15), pages 1-15, July.
    6. 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.
    7. Unknown, 2016. "Energy for Sustainable Development," Conference Proceedings 253270, Guru Arjan Dev Institute of Development Studies (IDSAsr).
    8. Huang, Renfang & Zhang, Zhen & Zhang, Wei & Mou, Jiegang & Zhou, Peijian & Wang, Yiwei, 2020. "Energy performance prediction of the centrifugal pumps by using a hybrid neural network," Energy, Elsevier, vol. 213(C).
    9. Rossi, Mosè & Renzi, Massimiliano, 2018. "A general methodology for performance prediction of pumps-as-turbines using Artificial Neural Networks," Renewable Energy, Elsevier, vol. 128(PA), pages 265-274.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wenqiang Zhou & Peijian Zhou & Chun Xiang & Yang Wang & Jiegang Mou & Jiayi Cui, 2023. "A Review of Bionic Structures in Control of Aerodynamic Noise of Centrifugal Fans," Energies, MDPI, vol. 16(11), pages 1-24, May.
    2. Maosen Xu & Guorui Zeng & Dazhuan Wu & Jiegang Mou & Jianfang Zhao & Shuihua Zheng & Bin Huang & Yun Ren, 2022. "Structural Optimization of Jet Fish Pump Design Based on a Multi-Objective Genetic Algorithm," Energies, MDPI, vol. 15(11), pages 1-16, June.
    3. Zhiqiang Yin & Lin Shi & Junru Luo & Shoukun Xu & Yang Yuan & Xinxin Tan & Jiaqun Zhu, 2023. "Pump Feature Construction and Electrical Energy Consumption Prediction Based on Feature Engineering and LightGBM Algorithm," Sustainability, MDPI, vol. 15(1), pages 1-17, January.
    4. Heng Qian & Denghao Wu & Chun Xiang & Junwei Jiang & Zhibing Zhu & Peijian Zhou & Jiegang Mou, 2022. "A Visualized Experimental Study on the Influence of Reflux Hole on the Double Blades Self-Priming Pump Performance," Energies, MDPI, vol. 15(13), pages 1-11, June.
    5. Lei Xu & Tao Jiang & Chuan Wang & Dongtao Ji & Wei Shi & Bo Xu & Weigang Lu, 2022. "Experiment and Numerical Simulation on Hydraulic Loss and Flow Pattern of Low Hump Outlet Conduit with Different Inlet Water Rotation Speeds," Energies, MDPI, vol. 15(15), pages 1-21, July.
    6. Hao Wang & Peijian Zhou & Ting Chen & Jiegang Mou & Jiayi Cui & Huiming Zhang, 2023. "Optimization of Liquid−Liquid Mixing in a Novel Mixer Based on Hybrid SVR-DE Model," Energies, MDPI, vol. 16(4), pages 1-17, February.
    7. Huiyan Zhang & Daohang Zou & Xuelong Yang & Jiegang Mou & Qiwei Zhou & Maosen Xu, 2022. "Liquid–Gas Jet Pump: A Review," Energies, MDPI, vol. 15(19), pages 1-15, September.
    8. 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).
    9. Wanming Pan & Junkang Li & Guotao Zhang & Le Zhou & Ming Tu, 2022. "Multi-Objective Optimization of Organic Rankine Cycle (ORC) for Tractor Waste Heat Recovery Based on Particle Swarm Optimization," Energies, MDPI, vol. 15(18), pages 1-24, September.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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).
    2. Kandi, Ali & Moghimi, Mahdi & Tahani, Mojtaba & Derakhshan, Shahram, 2021. "Optimization of pump selection for running as turbine and performance analysis within the regulation schemes," Energy, Elsevier, vol. 217(C).
    3. Kandi, Ali & Meirelles, Gustavo & Brentan, Bruno, 2022. "Employing demand prediction in pump as turbine plant design regarding energy recovery enhancement," Renewable Energy, Elsevier, vol. 187(C), pages 223-236.
    4. Rossi, Mosè & Nigro, Alessandra & Renzi, Massimiliano, 2019. "Experimental and numerical assessment of a methodology for performance prediction of Pumps-as-Turbines (PaTs) operating in off-design conditions," Applied Energy, Elsevier, vol. 248(C), pages 555-566.
    5. Wang, Yuqi & Du, Qiuwan & Li, Yunzhu & Zhang, Di & Xie, Yonghui, 2022. "Field reconstruction and off-design performance prediction of turbomachinery in energy systems based on deep learning techniques," Energy, Elsevier, vol. 238(PB).
    6. Li, Jinxing & Liu, Tianyuan & Zhu, Guangya & Li, Yunzhu & Xie, Yonghui, 2023. "Uncertainty quantification and aerodynamic robust optimization of turbomachinery based on graph learning methods," Energy, Elsevier, vol. 273(C).
    7. Ávila, Carlos Andrés Macías & Sánchez-Romero, Francisco-Javier & López-Jiménez, P. Amparo & Pérez-Sánchez, Modesto, 2021. "Optimization tool to improve the management of the leakages and recovered energy in irrigation water systems," Agricultural Water Management, Elsevier, vol. 258(C).
    8. Zhang, Liwen & Wang, Xin & Wu, Peng & Huang, Bin & Wu, Dazhuan, 2023. "Optimization of a centrifugal pump to improve hydraulic efficiency and reduce hydro-induced vibration," Energy, Elsevier, vol. 268(C).
    9. Tahani, Mojtaba & Kandi, Ali & Moghimi, Mahdi & Houreh, Shahram Derakhshan, 2020. "Rotational speed variation assessment of centrifugal pump-as-turbine as an energy utilization device under water distribution network condition," Energy, Elsevier, vol. 213(C).
    10. Jiang, Chiju & Zhang, Weihao & Li, Ya & Li, Lele & Wang, Yufan & Huang, Dongming, 2023. "Multi-scale Pix2Pix network for high-fidelity prediction of adiabatic cooling effectiveness in turbine cascade," Energy, Elsevier, vol. 265(C).
    11. Shojaeefard, Mohammad Hassan & Saremian, Salman, 2022. "Effects of impeller geometry modification on performance of pump as turbine in the urban water distribution network," Energy, Elsevier, vol. 255(C).
    12. Diamantis Karakatsanis & Nicolaos Theodossiou, 2022. "Smart Hydropower Water Distribution Networks, Use of Artificial Intelligence Methods and Metaheuristic Algorithms to Generate Energy from Existing Water Supply Networks," Energies, MDPI, vol. 15(14), pages 1-21, July.
    13. Stefanizzi, M. & Filannino, D. & Capurso, T. & Camporeale, S.M. & Torresi, M., 2023. "Optimal hydraulic energy harvesting strategy for PaT installation in Water Distribution Networks," Applied Energy, Elsevier, vol. 344(C).
    14. Maria Castorino, Giulia Anna & Manservigi, Lucrezia & Barbarelli, Silvio & Losi, Enzo & Venturini, Mauro, 2023. "Development and validation of a comprehensive methodology for predicting PAT performance curves," Energy, Elsevier, vol. 274(C).
    15. Stefanizzi, Michele & Capurso, Tommaso & Balacco, Gabriella & Binetti, Mario & Camporeale, Sergio Mario & Torresi, Marco, 2020. "Selection, control and techno-economic feasibility of Pumps as Turbines in Water Distribution Networks," Renewable Energy, Elsevier, vol. 162(C), pages 1292-1306.
    16. Pugliese, Francesco & Fontana, Nicola & Marini, Gustavo & Giugni, Maurizio, 2021. "Experimental assessment of the impact of number of stages on vertical axis multi-stage centrifugal PATs," Renewable Energy, Elsevier, vol. 178(C), pages 891-903.
    17. Balacco, Gabriella & Fiorese, Gaetano Daniele & Alfio, Maria Rosaria & Totaro, Vincenzo & Binetti, Mario & Torresi, Marco & Stefanizzi, Michele, 2023. "PaT-ID: A tool for the selection of the optimal pump as turbine for a water distribution network," Energy, Elsevier, vol. 282(C).
    18. Yang, Fan & Li, Zhongbin & Yuan, Yao & Lin, Zhikang & Zhou, Guangxin & Ji, Qingwei, 2022. "Study on vortex flow and pressure fluctuation in dustpan-shaped conduit of a low head axial-flow pump as turbine," Renewable Energy, Elsevier, vol. 196(C), pages 856-869.
    19. Du, Qiuwan & Yang, Like & Li, Liangliang & Liu, Tianyuan & Zhang, Di & Xie, Yonghui, 2022. "Aerodynamic design and optimization of blade end wall profile of turbomachinery based on series convolutional neural network," Energy, Elsevier, vol. 244(PA).
    20. Lin, Tong & Li, Xiaojun & Zhu, Zuchao & Xie, Jing & Li, Yi & Yang, Hui, 2021. "Application of enstrophy dissipation to analyze energy loss in a centrifugal pump as turbine," Renewable Energy, Elsevier, vol. 163(C), pages 41-55.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:15:y:2022:i:9:p:3309-:d:807267. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.