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Performance Prediction of a Pump as Turbine: Sensitivity Analysis Based on Artificial Neural Networks and Evolutionary Polynomial Regression

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  • Gabriella Balacco

    (Dipartimento di Ingegneria Civile, Ambientale, del Territorio, Edile e di Chimica, Politecnico di Bari, Via E. Orabona, 4, 70125 Bari, Italy)

Abstract

The research of a general methodology to predict the pump performance in a reverse mode, knowing those of a pump in a direct mode, is a question that is still open. The scientific research is making many efforts toward answering this question, but at present, there is still not much clarity. This consideration has been the starting point of this research that thanks to artificial neural networks and evolutionary polynomial regression methods have tried to investigate and define the real weight of every input parameter, representing the efficiency of a pump in a direct way, on the output parameters, and representing efficiency of a pump used like a turbine.

Suggested Citation

  • Gabriella Balacco, 2018. "Performance Prediction of a Pump as Turbine: Sensitivity Analysis Based on Artificial Neural Networks and Evolutionary Polynomial Regression," Energies, MDPI, vol. 11(12), pages 1-17, December.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:12:p:3497-:d:190684
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    References listed on IDEAS

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    1. Mauro Venturini & Stefano Alvisi & Silvio Simani & Lucrezia Manservigi, 2018. "Comparison of Different Approaches to Predict the Performance of Pumps As Turbines (PATs)," Energies, MDPI, vol. 11(4), pages 1-17, April.
    2. Bozorgi, A. & Javidpour, E. & Riasi, A. & Nourbakhsh, A., 2013. "Numerical and experimental study of using axial pump as turbine in Pico hydropower plants," Renewable Energy, Elsevier, vol. 53(C), pages 258-264.
    3. Giosio, D.R. & Henderson, A.D. & Walker, J.M. & Brandner, P.A. & Sargison, J.E. & Gautam, P., 2015. "Design and performance evaluation of a pump-as-turbine micro-hydro test facility with incorporated inlet flow control," Renewable Energy, Elsevier, vol. 78(C), pages 1-6.
    4. Lima, Gustavo Meirelles & Luvizotto, Edevar & Brentan, Bruno M., 2017. "Selection and location of Pumps as Turbines substituting pressure reducing valves," Renewable Energy, Elsevier, vol. 109(C), pages 392-405.
    5. 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.
    6. Binama, Maxime & Su, Wen-Tao & Li, Xiao-Bin & Li, Feng-Chen & Wei, Xian-Zhu & An, Shi, 2017. "Investigation on pump as turbine (PAT) technical aspects for micro hydropower schemes: A state-of-the-art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 148-179.
    7. Jain, Sanjay V. & Patel, Rajesh N., 2014. "Investigations on pump running in turbine mode: A review of the state-of-the-art," Renewable and Sustainable Energy Reviews, Elsevier, vol. 30(C), pages 841-868.
    8. Tan, Xu & Engeda, Abraham, 2016. "Performance of centrifugal pumps running in reverse as turbine: Part Ⅱ- systematic specific speed and specific diameter based performance prediction," Renewable Energy, Elsevier, vol. 99(C), pages 188-197.
    9. 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.
    10. Pugliese, Francesco & De Paola, Francesco & Fontana, Nicola & Giugni, Maurizio & Marini, Gustavo, 2016. "Experimental characterization of two Pumps As Turbines for hydropower generation," Renewable Energy, Elsevier, vol. 99(C), pages 180-187.
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    Cited by:

    1. 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.
    2. 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).
    3. 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).
    4. Martin Polák, 2021. "Innovation of Pump as Turbine According to Calculation Model for Francis Turbine Design," Energies, MDPI, vol. 14(9), pages 1-13, May.
    5. Maxime Binama & Kan Kan & Huixiang Chen & Yuan Zheng & Daqing Zhou & Alexis Muhirwa & Godfrey M. Bwimba, 2021. "Investigation into Pump Mode Flow Dynamics for a Mixed Flow PAT with Adjustable Runner Blades," Energies, MDPI, vol. 14(9), pages 1-28, May.
    6. 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.

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