Comparison of univariate and multivariate predicted method based on support vector regression for silicon content in hot metal

Abstract

Silicon content plays an important role in reflecting the thermal state of a blast furnace (BF). The commonly used methods to predict silicon content at present are univariate and multivariate predicted method. The univariate predicted method only uses the information of silicon content in hot metal and can not reflect the complexity of blast furnace ironmaking process. The multivariate predicted method takes advantage of the rich information in blast furnace, different variables usually have a strong correlation with each other, which is not only useless for prediction but also brings in a lot of noise. So it always can not obtain the special satisfactory results. In this article, we have collected the real data from a medium-size BF and done three experiments based on support vector regression. These experiments can divide into two categories, univariate predicted method (UPM) and multivariate predicted method (MPM), the latter includes local variables prediction (LVP) and whole variables prediction (WVP). Multivariate correlation analysis and Spearman's rank correlation coefficient are applied to select variables which have low correlation for LVP. Phase space reconstruction is used to obtain the input and output of UPM. The experiment results show that LVP is better than the UPM and WVP. This also powerfully illustrates that choosing more variables can indeed bring in more useful information for prediction, especially when the correlation is eliminated through the selection of variables, the prediction result will be better.