A hybrid artificial neural network—mechanistic model for centrifugal compressor

Abstract

A mathematical model is an important tool for design and optimization of centrifugal compressor. However, owing to the varying compressor speeds and the complexity of the flow dynamics inside the impeller and diffuser, the currently available mechanistic models may yield inaccurate results. The purpose of this paper is to present a hybrid modeling approach for developing a quantitatively accurate model for centrifugal compressor. Two novel hybrid models, that is, additive and multiplicative hybrid models each of which consists of a three-layer back-propagation artificial neural network (ANN) component and a mechanistic component suitably modified to describe the performances of multistage centrifugal compressor, were constructed and compared with the well-developed ANN model. The results from the hybrid models showed better performance compared to the ANN model. Besides, the hybrid models demonstrated much better performance than the pure mechanistic model, and the multiplicative hybrid model, in general, showed better accuracy than that of the additive hybrid model in our case.