Artificial neural network-based predictive model for supersonic ejector in refrigeration system
Hanzeng Zhu, Jiapeng Liu, Jinpeng Yu, Peng Yang
Abstract
In this paper, a novel predictive model is proposed for the supersonic ejector in the refrigeration system based on the artificial neural network technique. A composite performance prediction framework for the supersonic ejector is developed by employing a back propagation neural network with particle swarm optimization (PSO-BPNN) algorithm and a dynamic error compensation method. Firstly, we study the model construction problem for the supersonic ejector based on the traditional BPNN modeling method and the PSO algorithm. And then, a dynamic compensation technique is given to improve the predictive accuracy of the ejector model based on the adaptive neural network method. Finally, both the simulation and experimental results are given to verify our method. The experimental validation results show that most prediction errors of our model are less than 4%. Therefore, compared with the existing traditional models, our model has a higher accuracy.