Abstract:Hot metal silicon content has long been used as one of the most important indices to represent the hot metal quality and the thermal state of a blast furnace. In order to improve the measurement precision and attain stable operation of the blast furnace, a novel model for predicting silicon content by using Least Square Support Vector Machine(LSSVM) is presented. It adopts Genetic Algorithm(GA) to determine the optimum parameter set and therefore improves the model performance. By training and testing the operational data from blast furnace at a steel tube plant, the experimental results indicate that the proposed model can predict silicon content in hot metal with a maximum relative error of 5.8 % and correlation coefficient of 0.926 375, whose accuracy can be improved by 2.1% and 4.3% than that of the direct LSSVM and the feed-forward network with the same data set, respectively.