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

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  • 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.

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  • 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
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    as
    1. 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.
    2. 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.
    3. 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.
    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. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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).
    11. 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.
    12. 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.
    13. 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.
    Full references (including those not matched with items on IDEAS)

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