Hybrid Model of Molten Steel Temperature Prediction Based on Ladle Heat Status and Artificial Neural Network

Abstract

Aiming at the characteristics of the practical steelmaking process, a hybrid model based on ladle heat status and atificial neural network has been proposed to predict molten steel temperature. The hybrid model could overcome the diculty of accurate prediction using a single mathematical model, and solve the problem of lacking the consderation of the influence of ladle heat status on the steel temperature in an intelligent model. By using the hybrid model method, forward and backward prediction models for molten steel temperature in steelmaking process are established and are used in a steelmaking plant. The forward model, starting from the end-point of BOF, predicts the temperature in argon-blowing station, starting temperature in LF, end temperature in LF and tundish temperature forwards, with the production process evolving. The backward model, starting from the required tundish temperature, calculates target end temperature in LF, target starting temperature in LF, target temperature in argon-blowing station and target BOF end-point temperature backwards. Actual application results show that the models have better prediction accuracy and are satisfying for the process of practical production.