Wang Fanrong
1. School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430068, China; 2. Xiangyang Industrial Institute of Hubei University of Technology,?Xiangyang?441100,?China.Fang Zuchun
1. School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430068, China; 2. Xiangyang Industrial Institute of Hubei University of Technology,?Xiangyang?441100,?China.Liu Yuhang
School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430068, ChinaWang Junhan
School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430068, China1. School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430068, China; 2. Xiangyang Industrial Institute of Hubei University of Technology, Xiangyang 441100, China.
TM714
In order to realize the accurate identification of production processes, a process identification model based on machine learning was proposed. Time convolution network, long and short term memory network and support vector machine were selected to build the process identification model, and the model was tested and verified with the production energy consumption data of a titanium metal refining enterprise. Firstly, the historical power and process data were preprocessed, and then the model training and testing data set was constructed according to the production characteristics. Finally, the model was trained and tested based on the data set. The results shows that the recognition model based on time convolution network has a high accuracy of process identification, and the accuracy of process identification for test sets reaches 96.94%.
