IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v213y2020ics0360544220321125.html
   My bibliography  Save this article

Energy performance prediction of the centrifugal pumps by using a hybrid neural network

Author

Listed:
  • Huang, Renfang
  • Zhang, Zhen
  • Zhang, Wei
  • Mou, Jiegang
  • Zhou, Peijian
  • Wang, Yiwei

Abstract

It is of great significance to rapidly and accurately predict the energy performance of centrifugal pumps for the macro-control of the entire electric power system. However, some challenges are encountered, for example, the numerical simulation requires huge computing resources and calculating time, the theoretical loss model needs to improve the prediction accuracy, etc. Based on the multiple geometrical parameters and operation conditions, a hybrid neural network is proposed to predict the energy performance (i.e. the head, power and efficiency) of centrifugal pumps, where the theoretical loss model is incorporated into the back propagation neural network and then the neural network structure is optimized by automatically determining the node number of hidden layers. When compared with the experiments, the energy performance is well predicted by using the hybrid neural network with the mean-square-error (MSE) for the head, power and efficiency of 0.0062, 8.4E-4, 0.020, respectively. Besides, by considering the theoretical loss model, the hybrid neural network demonstrates a dramatic decrease in the head MSE and the efficiency MSE when compared with the original neural network. Furthermore, the hybrid neural network performs much better than the traditional linear regression in a wide flow-rate range for multiple centrifugal pumps.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:213:y:2020:i:c:s0360544220321125
    DOI: 10.1016/j.energy.2020.119005
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544220321125
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2020.119005?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Liu, Ming & Tan, Lei & Cao, Shuliang, 2019. "Dynamic mode decomposition of gas-liquid flow in a rotodynamic multiphase pump," Renewable Energy, Elsevier, vol. 139(C), pages 1159-1175.
    2. Meesenburg, Wiebke & Ommen, Torben & Elmegaard, Brian, 2018. "Dynamic exergoeconomic analysis of a heat pump system used for ancillary services in an integrated energy system," Energy, Elsevier, vol. 152(C), pages 154-165.
    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. Lee, Da Young & Seo, Byeong Mo & Hong, Sung Hyup & Choi, Jong Min & Lee, Kwang Ho, 2019. "Part load ratio characteristics and energy saving performance of standing column well geothermal heat pump system assisted with storage tank in an apartment," Energy, Elsevier, vol. 174(C), pages 1060-1078.
    5. Barbarelli, S. & Amelio, M. & Florio, G., 2016. "Predictive model estimating the performances of centrifugal pumps used as turbines," Energy, Elsevier, vol. 107(C), pages 103-121.
    6. Terreros, O. & Spreitzhofer, J. & Basciotti, D. & Schmidt, R.R. & Esterl, T. & Pober, M. & Kerschbaumer, M. & Ziegler, M., 2020. "Electricity market options for heat pumps in rural district heating networks in Austria," Energy, Elsevier, vol. 196(C).
    7. Zhang, Jianfei & Kong, Lingjian & Qu, Jingguo & Wang, Shuang & Qu, Zhiguo, 2019. "Numerical and experimental investigation on configuration optimization of the large-size ionic wind pump," Energy, Elsevier, vol. 171(C), pages 624-630.
    8. Hao, Yue & Tan, Lei, 2018. "Symmetrical and unsymmetrical tip clearances on cavitation performance and radial force of a mixed flow pump as turbine at pump mode," Renewable Energy, Elsevier, vol. 127(C), pages 368-376.
    9. Nam, Yujin & Chae, Ho-Byung, 2014. "Numerical simulation for the optimum design of ground source heat pump system using building foundation as horizontal heat exchanger," Energy, Elsevier, vol. 73(C), pages 933-942.
    10. Pudjianto, Danny & Djapic, Predrag & Aunedi, Marko & Gan, Chin Kim & Strbac, Goran & Huang, Sikai & Infield, David, 2013. "Smart control for minimizing distribution network reinforcement cost due to electrification," Energy Policy, Elsevier, vol. 52(C), pages 76-84.
    11. Wang, D. & Parkinson, S. & Miao, W. & Jia, H. & Crawford, C. & Djilali, N., 2012. "Online voltage security assessment considering comfort-constrained demand response control of distributed heat pump systems," Applied Energy, Elsevier, vol. 96(C), pages 104-114.
    12. 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.
    13. Yang, Sun-Sheng & Derakhshan, Shahram & Kong, Fan-Yu, 2012. "Theoretical, numerical and experimental prediction of pump as turbine performance," Renewable Energy, Elsevier, vol. 48(C), pages 507-513.
    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. 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. 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).
    3. 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.
    4. Lei Wang & Jiayi Cui & Lingfeng Shu & Denghui Jiang & Chun Xiang & Linwei Li & Peijian Zhou, 2022. "Research on the Vortex Rope Control Techniques in Draft Tube of Francis Turbines," Energies, MDPI, vol. 15(24), pages 1-27, December.
    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. Bai, Ling & Yang, Yang & Zhou, Ling & Li, Yuanzhe & Xiao, Yu & Shi, Weidong, 2022. "Optimal design and performance improvement of an electric submersible pump impeller based on Taguchi approach," Energy, Elsevier, vol. 252(C).
    7. Hanbing Ma & Lukas Gaisser & Stefan Riedelbauch, 2023. "Monitoring Pumping Units by Convolutional Neural Networks for Operating Point Estimations," Energies, MDPI, vol. 16(11), pages 1-12, May.
    8. 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).
    9. Qin, Yonglin & Li, Deyou & Wang, Hongjie & Liu, Zhansheng & Wei, Xianzhu & Wang, Xiaohang, 2022. "Multi-objective optimization design on high pressure side of a pump-turbine runner with high efficiency," Renewable Energy, Elsevier, vol. 190(C), pages 103-120.
    10. Li, Jinxing & Liu, Tianyuan & Wang, Yuqi & Xie, Yonghui, 2022. "Integrated graph deep learning framework for flow field reconstruction and performance prediction of turbomachinery," Energy, Elsevier, vol. 254(PC).
    11. 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).
    12. 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).
    13. 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).
    14. Wang, Yuqi & Liu, Tianyuan & Meng, Yue & Zhang, Di & Xie, Yonghui, 2022. "Integrated optimization for design and operation of turbomachinery in a solar-based Brayton cycle based on deep learning techniques," Energy, Elsevier, vol. 252(C).
    15. Du, Qiuwan & Li, Yunzhu & Yang, Like & Liu, Tianyuan & Zhang, Di & Xie, Yonghui, 2022. "Performance prediction and design optimization of turbine blade profile with deep learning method," Energy, Elsevier, vol. 254(PA).
    16. 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.
    17. Xiaoping Chen & Xiaoming Zhang & Xiaojun Li, 2022. "Evolution Characteristics of Energy Change Field in a Centrifugal Pump during Rapid Starting Period," Energies, MDPI, vol. 15(22), pages 1-15, November.

    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. 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.
    2. 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).
    3. Lin, Tong & Zhu, Zuchao & Li, Xiaojun & Li, Jian & Lin, Yanpi, 2021. "Theoretical, experimental, and numerical methods to predict the best efficiency point of centrifugal pump as turbine," Renewable Energy, Elsevier, vol. 168(C), pages 31-44.
    4. Alemi Arani, Hamed & Fathi, Mohammad & Raisee, Mehrdad & Nourbakhsh, Seyed Ahmad, 2019. "The effect of tongue geometry on pump performance in reverse mode: An experimental study," Renewable Energy, Elsevier, vol. 141(C), pages 717-727.
    5. Li, Wei & Li, Enda & Ji, Leilei & Zhou, Ling & Shi, Weidong & Zhu, Yong, 2020. "Mechanism and propagation characteristics of rotating stall in a mixed-flow pump," Renewable Energy, Elsevier, vol. 153(C), pages 74-92.
    6. Venturini, Mauro & Manservigi, Lucrezia & Alvisi, Stefano & Simani, Silvio, 2018. "Development of a physics-based model to predict the performance of pumps as turbines," Applied Energy, Elsevier, vol. 231(C), pages 343-354.
    7. 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).
    8. Liu, Ming & Tan, Lei & Cao, Shuliang, 2020. "Influence of viscosity on energy performance and flow field of a multiphase pump," Renewable Energy, Elsevier, vol. 162(C), pages 1151-1160.
    9. 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).
    10. Hongyu, Guan & Wei, Jiang & Yuchuan, Wang & Hui, Tian & Ting, Li & Diyi, Chen, 2021. "Numerical simulation and experimental investigation on the influence of the clocking effect on the hydraulic performance of the centrifugal pump as turbine," Renewable Energy, Elsevier, vol. 168(C), pages 21-30.
    11. Štefan, David & Rossi, Mosè & Hudec, Martin & Rudolf, Pavel & Nigro, Alessandra & Renzi, Massimiliano, 2020. "Study of the internal flow field in a pump-as-turbine (PaT): Numerical investigation, overall performance prediction model and velocity vector analysis," Renewable Energy, Elsevier, vol. 156(C), pages 158-172.
    12. 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).
    13. Javanshir, Nima & Syri, Sanna & Tervo, Seela & Rosin, Argo, 2023. "Operation of district heat network in electricity and balancing markets with the power-to-heat sector coupling," Energy, Elsevier, vol. 266(C).
    14. 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.
    15. Terreros, O. & Spreitzhofer, J. & Basciotti, D. & Schmidt, R.R. & Esterl, T. & Pober, M. & Kerschbaumer, M. & Ziegler, M., 2020. "Electricity market options for heat pumps in rural district heating networks in Austria," Energy, Elsevier, vol. 196(C).
    16. 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).
    17. 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).
    18. 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.
    19. Erkan, Onur & Özkan, Musa & Karakoç, T. Hikmet & Garrett, Stephen J. & Thomas, Peter J., 2020. "Investigation of aerodynamic performance characteristics of a wind-turbine-blade profile using the finite-volume method," Renewable Energy, Elsevier, vol. 161(C), pages 1359-1367.
    20. Boldrini, A. & Jiménez Navarro, J.P. & Crijns-Graus, W.H.J. & van den Broek, M.A., 2022. "The role of district heating systems to provide balancing services in the European Union," Renewable and Sustainable Energy Reviews, Elsevier, vol. 154(C).

    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:eee:energy:v:213:y:2020:i:c:s0360544220321125. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    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.