Energy performance prediction of centrifugal pumps based on adaptive support vector regression

Abstract

It is of great significance to speed up the development and optimization of pumps with energy performance prediction methods. Machine learning is widely used for performance prediction of centrifugal pumps due to its fast and accurate predictions. However, the prediction model performance distinctly for the different geometry and performance parameters. This paper proposes an adaptive support vector regression (SVR) model for predicting centrifugal pump energy performance, which incorporates input-output correlation analysis and differential evolution to automatically adjust the input parameter weights. The model's performance was validated against experimental data, yielding mean absolute residuals (MAR) of 0.174 for head, 0.113 for power, and 1.658 for efficiency. Additionally, the model achieved an R² of 0.995 and a mean square error (MSE) of 2.99. In multi-operation conditions, by adjusting the parameter vector, the adaptive SVR reduced the mean absolute relative error (MARE) of head, power, and efficiency to 0.443%, 1.07%, and 6.63%, respectively, representing improvements of 79.6%, 86.2%, and 31.6% compared to the original SVR model. The proposed model also outperformed the adaptive least squares support vector regression (LSSVR).