A New Model Based on GRA and LSSVM to Predict Silicon Content in Hot Metal

Abstract

A new model on gray relation analysis(GRA) and least square support vector machine(LSSVM) to predict silicon content in hot metal is proposed. GRA is used to extract the relationship between silicon content in hot metal and other variables, the important factors are choose based on the gray relation value sequence. The key factors are extracted as the input variables of LSSVM. The method can reduce the dimensions of the data and the complexity, and improve the efficiency of training and the accuracy of prediction. The data of the model are collected from No.6 Blast Furnace in Baotou Iron and Steel Group Co. of China. The results show that the LSSVM model based on GRA has better prediction results than the model using other variables. The hit rate of silicon content in hot metal reaches 86% at the range of 0.1 % based on the proposed model, which can meet the requirement of practical production.