Energy Performance Curves Prediction of Centrifugal Pumps Based on Constrained PSO-SVR Model

Huican Luo, Peijian Zhou, Lingfeng Shu, Jiegang Mou, Haisheng Zheng, Chenglong Jiang and Yantian Wang
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Huican Luo: College of Metrology and Measurement Engineering, China Jiliang University, Hangzhou 310018, China
Peijian Zhou: College of Metrology and Measurement Engineering, China Jiliang University, Hangzhou 310018, China
Lingfeng Shu: Power China Huadong Engineering Corporation Limited, Hangzhou 311122, China
Jiegang Mou: College of Metrology and Measurement Engineering, China Jiliang University, Hangzhou 310018, China
Haisheng Zheng: College of Metrology and Measurement Engineering, China Jiliang University, Hangzhou 310018, China
Chenglong Jiang: College of Metrology and Measurement Engineering, China Jiliang University, Hangzhou 310018, China
Yantian Wang: College of Metrology and Measurement Engineering, China Jiliang University, Hangzhou 310018, China

Energies, 2022, vol. 15, issue 9, 1-19

Abstract: It is of great significance to predict the energy performance of centrifugal pumps for the improvement of the pump design. However, the complex internal flow always affects the performance prediction of centrifugal pumps, particularly under low-flow operating conditions. Relying on the data-fitting method, a multi-condition performance prediction method for centrifugal pumps is proposed, where the performance relationship is incorporated into the particle swarm optimization algorithm, and the prediction model is optimized by automatically meeting the performance constraints. Compared with the experimental results, the performance under multiple operating conditions is well predicted by introducing performance constraints with the mean absolute relative error (MARE) for the head, power and efficiency of 0.85%, 1.53%,1.15%, respectively. By comparing the extreme gradient boosting and support vector regression models, the support vector regression is more suitable for the prediction of performance curves. Finally, by introducing performance constraints, the proposed model demonstrates a dramatic decrease in the head, power and efficiency of MARE by 98.64%, 82.06%, and 85.33%, respectively, when compared with the BP neural network.

Keywords: centrifugal pump; performance relationship; support vector regression; particle swarm; performance prediction (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: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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