The Research Of BOF Endpoint Prediction Model Based On Gray RBF Neural Network

BOF is the main steelmaking methods in the modern world, some advanced iron and steel enterprises in our country has adopted dynamic control technology. BOF is an extremely complex industrial processes, many factors affect the endpoint temperature and carbon content, because of the furnace temperature is too high, the endpoint temperature and carbon content can’t timely and accurate measurements, thus establish accurate forecasting model of temperature and carbon is very important. Aimed at this problem,a BOF endpoint prediction model of neural networks is established in this thesis, according to the forecast results can fill of blowing oxygen and make reasonable adjustments for the amount of coolant, thus improve the finish shooting, in order to improve the yield and quality of converter steel-making, reduce energy consumption, reduce the cost of steelmaking.Based on the actual project of the whole process simulation project in AnSteel information industry company,this thesis dose the in-depth theoretical research and extension, the main work is as follows:Because of the end point prediction model is the core of the BOF neural network model, due to the complexity of its process and many of affecting factors, first of atl, using rough set attribute reduction method, reduce the converter input attribute, then combined the actual field data training neural networks, so as to get a better prediction model.Compared with BP neural network, RBF neural network learning time is short, and has good nonlinear prediction effect. However, due to steelmaking complex process, the variety of data, therefore, in this thesis, the traditional RBF neural network model is improved, the center of ant clustering algorithm is used to determine the basis function and the number of hidden layer nodes. In order to solve the prediction of less training samples, the problem of inaccurate, introduce the gray GM (1,N) model improved RBF neural network, the improved model integrated into a composite forecast model, gray RBF neural network prediction model.This article use various neural network prediction models and gray RBF neural network prediction model, use the actual smelting data to simulate, the improved forecasting model simulation result is obviously better than the other models, it shows that the proposed method is feasible.