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

Stephen Ntiri Asomani, Jianping Yuan, Longyan Wang, Desmond Appiah and Kofi Asamoah Adu-Poku
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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
Longyan Wang: National Research Center of Pumps, Jiangsu University, Zhenjiang 212013, China
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

Energies, 2020, vol. 13, issue 9, 1-29

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.

Keywords: pump as turbine; optimization; multi-objective; General Regression Neural Network; Adaptive Neuro-Fuzzy Inference System (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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