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The Impact of Surrogate Models on the Multi-Objective Optimization of Pump-As-Turbine (PAT)

Author

Listed:
  • Stephen Ntiri Asomani

    (National Research Center of Pumps, Jiangsu University, Zhenjiang 212013, China)

  • Jianping Yuan

    (National Research Center of Pumps, Jiangsu University, Zhenjiang 212013, China
    Institute of Fluid Engineering Equipment, JITRI, Jiangsu University, Zhenjiang 212013, China)

  • Longyan Wang

    (National Research Center of Pumps, Jiangsu University, Zhenjiang 212013, China
    School of Chemistry, Physics and Mechanical Engineering, Queensland University of Technology, Brisbane 4001, Australia)

  • Desmond Appiah

    (National Research Center of Pumps, Jiangsu University, Zhenjiang 212013, China)

  • Kofi Asamoah Adu-Poku

    (National Research Center of Pumps, Jiangsu University, Zhenjiang 212013, China)

Abstract

Pump-as-turbine (PAT) technology permits two operating states—as a pump or turbine, depending on the demand. Nevertheless, designing the geometrical components to suit these operating states has been an unending design issue, because of the multi-conditions for the PAT technology that must be attained to enhance the hydraulic performance. Also, PAT has been known to have a narrow operating range and operates poorly at off-design conditions, due to the lack of flow control device and poor geometrical designs. Therefore, for the PAT to have a wider operating range and operate effectively at off-design conditions, the geometric parameters need to be optimized. Since it is practically impossible to optimize more than one objective function at the same time, a suitable surrogate model is needed to mimic the objective functions for it to be solvable. In this study, the Latin hypercube sampling method was used to obtain the objective function values, the Adaptive Neuro-Fuzzy Inference System (ANFIS), Artificial Neural Network (ANN) and Generalized Regression Neural Network (GRNN) were used as surrogate models to approximate the objective functions in the design space. Then, a suitable surrogate model was chosen for the optimization. The Pareto-optimal solutions were obtained by using the Pareto-based genetic algorithm (PBGA). To evaluate the results of the optimization, three representative Pareto-optimal points were selected and analyzed. Compared to the baseline model, the Pareto-optimal points showed a great improvement in the objective functions. After optimization, the geometry of the impeller was redesigned to suit the operating conditions of PAT. The findings show that the efficiencies of the optimized design variables of PAT were enhanced by 23.7%, 11.5%, and 10.4% at part load, design point, and under overload flow conditions, respectively. Moreover, the results also indicated that the chosen design variables ( b 2 , β 2 , β 1 , and z ) had a substantial impact on the objective functions, justifying the feasibility of the optimization method employed in this study.

Suggested Citation

  • Stephen Ntiri Asomani & Jianping Yuan & Longyan Wang & Desmond Appiah & Kofi Asamoah Adu-Poku, 2020. "The Impact of Surrogate Models on the Multi-Objective Optimization of Pump-As-Turbine (PAT)," Energies, MDPI, vol. 13(9), pages 1-29, May.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:9:p:2271-:d:353995
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    References listed on IDEAS

    as
    1. Kleijnen, Jack P.C., 2009. "Kriging metamodeling in simulation: A review," European Journal of Operational Research, Elsevier, vol. 192(3), pages 707-716, February.
    2. Gholap, A.K. & Khan, J.A., 2007. "Design and multi-objective optimization of heat exchangers for refrigerators," Applied Energy, Elsevier, vol. 84(12), pages 1226-1239, December.
    3. Ali Hadi Abdulwahid & Shaorong Wang, 2016. "A Novel Approach for Microgrid Protection Based upon Combined ANFIS and Hilbert Space-Based Power Setting," Energies, MDPI, vol. 9(12), pages 1-25, December.
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    Cited by:

    1. Longyan Wang & Stephen Ntiri Asomani & Jianping Yuan & Desmond Appiah, 2020. "Geometrical Optimization of Pump-As-Turbine (PAT) Impellers for Enhancing Energy Efficiency with 1-D Theory," Energies, MDPI, vol. 13(16), pages 1-30, August.
    2. Morabito, Alessandro & Vagnoni, Elena & Di Matteo, Mariano & Hendrick, Patrick, 2021. "Numerical investigation on the volute cutwater for pumps running in turbine mode," Renewable Energy, Elsevier, vol. 175(C), pages 807-824.
    3. Jian Xu & Longyan Wang & Stephen Ntiri Asomani & Wei Luo & Rong Lu, 2020. "Improvement of Internal Flow Performance of a Centrifugal Pump-As-Turbine (PAT) by Impeller Geometric Optimization," Mathematics, MDPI, vol. 8(10), pages 1-23, October.
    4. Grzegorz Filo, 2023. "Artificial Intelligence Methods in Hydraulic System Design," Energies, MDPI, vol. 16(8), pages 1-19, April.
    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.

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