Performance prediction and optimization of hydrogenation feed pump based on particle swarm optimization – least squares support vector regression surrogate model

Abstract

Due to high power consumption and low energy efficiency of the hydrogenation feed multistage pump, conducting structural optimization design and reducing energy losses for this pump is necessary. In this study, an optimization method based on the particle swarm optimization (PSO) algorithm and least squares support vector regression (LSSVR) surrogate model is proposed. The head and efficiency are taken as the optimization objectives of the multistage pump, and the optimization design of the two impellers is carried out. Through the optimization of the surrogate model, the head and efficiency of the multistage pump are increased by 3.99% and 2.91%, respectively. It is found that the optimized impeller has more stable flow characteristics than original impeller. Further, the entropy production method is introduced to analyze the energy loss of pump. The result shows that the pump optimized by PSO-LSSVR surrogate model can reduce 9% energy consumption under nominal flow rate. It is attributed to the inhibitory effect of the optimization scheme on the horseshoe vortex at leading edge and the wake vortex at trailing edge of impeller. This study provides a new idea for the optimization design of high efficiency and low energy consumption of multistage centrifugal pumps.