Temperature Prediction and Evaluation of Mill Based on Neural Network

The inlet and outlet temperature of the mill is an important index in the process of slag powder production. The specific technological process of the production of slag powder is introduced, and the factors that influence the inlet and outlet temperature of the mill are analyzed. The actual data of production process is preprocessed by the Pauta criterion. The forecasting model of inlet and outlet temperature of mill is established to predict the temperature in an hour by using the basic BP neural network and BP neural network optimized by mind evolutionary algorithm. Through the simulation of Matlab software, the prediction effect diagram, prediction error chart, prediction mean square error, mean absolute error, mean absolute percentage error, and coefficient of decision of the two algorithms are compared. The results show that the BP neural network optimized by mind evolutionary algorithm is better than the original BP neural network in the prediction accuracy.