Prediction of Silicon Content in Hot Metal Based on the Combined Model of EMD and SVM

Abstract

In blast furnace (BF) ironmaking process, silicon content in hot metal is an important index, which reflects the thermal state of BF. To predict the silicon content in hot metal effectively and level up the forecasting accuracy, a novel combined model based on empirical mode decomposition (EMD) and support vector machine (SVM) is proposed. Firstly, the time series data of silicon content in hot metal are decomposed into a series of stationary intrinsic mode functions (IMF) in different scale space via EMD sifting procedure. The local features of original time series data are prominent in the IMFs. Secondly, based on the analysis of Lemple-Ziv complexity and 10-fold cross validation, the right kernel functions and their parameters are chosen to build different SVMs respectively to predict each IMF. Finally, the predicted results of all IMFs are reconstructed to obtain final predicted result which shows that the prediction is successful and the hit rate increased to 90%.