A Coke Quality Prediction Model Based on Support Vector Machine

Due to the complexity of coking coal, as well as the mixed nature of some single coal procured, the error is significantly larger to predict coke quality only through coal conventional indicators. Thus the coking enterprises urgently need a coke prediction method using many blend coal-related data. In view of the complexity of coking, there are some limitations as to the regression prediction method and neural network learning methods. On the base of the conventional indicators of single coal and coal rock indicators, the paper utilizes support vector machine to predict the cold and hot strength of coke. The experiments show that the accurate prediction of this method can meet the requirements of enterprises.