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

• Published in: Energies (Basel) Vol. 15; no. 9; p. 3309
• Main Authors: Luo, Huican, Zhou, Peijian, Shu, Lingfeng, Mou, Jiegang, Zheng, Haisheng, Jiang, Chenglong, Wang, Yantian
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.05.2022
• Subjects: Accuracy; centrifugal pump; Deep learning; Design; Efficiency; Energy; Fault diagnosis; Fluid dynamics; Genetic algorithms; Machine learning; Neural networks; Optimization; particle swarm; performance prediction; performance relationship; Power; Simulation; support vector regression
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en15093309

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.