Prediction Method of Core Dead Stock Column Temperature Based on PCA and Ridge Regression

Abstract

The change of core temperature of blast furnace reflects the working status of hearth. However, the temperature of core dead stock column can not be measured by sensors directly. Therefore, a prediction model of Core Dead Stock Column Temperature is proposed in this work based on primary component analysis (PCA) and ridge regression algorithms, where PCA and person correlation coefficient are used for feature extraction and ridge regression is employed to solve multi-collinearity problems. Based on an inhouse dataset collected within a successive three months, experimental results show that the R-squared of model on the training data set can achieve 88% and the average relative error on the test data set is only 0.33%, which shows the effectiveness of the proposed model.