Temperature modeling of wave rotor refrigeration process based on elastic net variable selection and deep belief network

Abstract

Optimal operation modeling plays an important role in wave rotor refrigeration process; however, considering covariance among multiple variables and high nonlinearity in wave rotor refrigeration process, it becomes more and more difficult to establish an accurate operation modeling using first-principles methods. This study proposed a novel modeling algorithm for the temperature parameter of the wave rotor refrigeration process based on elastic net and dingo optimization deep belief network (Enet-DOA-DBN). Firstly, to determine the correlation between the input variables and reduce the dimension of the input variables, the elastic net (Enet) algorithm is used to select the input variables that are irrelevant to the temperature parameter of the wave rotor refrigeration process. In this way, the covariance between multiple variables is eliminated and the model structure is simplified. Secondly, in order to improve the generalization of the temperature parameter model, a deep belief network (DBN) deep learning is proposed for modeling the temperature parameter of the wave rotor refrigeration process. Considering that the numerous hyperparameters of DBN algorithm have a great impact on the training and prediction results, the hyperparameters are optimized by the dingo optimization algorithm (DOA). The proposed Enet-DOA-DBN algorithm is validated by simulation using the benchmark data sets and the wave rotor refrigeration industrial process data sets. The simulation results show that the proposed Enet-DOA-DBN algorithm has good generalization ability, meanwhile it can effectively implement variable selection and simplify the model structure.