Performance modeling of centrifugal compressor using kernel partial least squares

The performance modeling of centrifugal (turbo) compressor was performed in this paper by applying kernel partial least squares (KPLS). Firstly, steady-state compressor data sets were collected from a real gas turbine power plant and a simulation study respectively. Then the two data sets were used to train the KPLS regression model for predicting the centrifugal compressor operating parameters such as pressure ratio and efficiency. The prediction performance of KPLS model was compared to that of a three-layer back-propagation (BP) neural network with validation data, and the KPLS model showed slightly better performance than the BP network. Furthermore, results showed that, with high accuracy, KPLS could be used to smoothly predict the compressor map, which was useful in the preliminary design phase of any centrifugal compression system.

► Centrifugal compressor performance modeling is performed by applying KPLS. ► Results show that KPLS can predict the compressor performance with high accuracy. ► Compared to BP neural network, KPLS shows slightly better prediction performance. ► It is demonstrated that KPLS can be used to smoothly predict the compressor map.