Research on prediction classification and compensation for silicon contents in blast furnace based on ridge regression

Abstract

In blast furnace, the accurate prediction of silicon contents in hot metal plays an important role in comprehensively judging the furnace states and making correct decisions. However, traditional prediction models are easily affected by multi-collinearity among the independent variables and imbalance sample distribution in hot metal datasets. The prediction values of silicon contents would be difficult to track the real values accurately when the furnace conditions fluctuate dramatically. In this paper, a classification and compensation model for silicon contents is proposed by combining the ridge regression and random forest classification algorithm. Firstly, the ridge regression algorithm is applied to fit and optimize the prediction values outputted by a front-end prediction model. Then, the random forest technology is expected to classify and identify the furnace states. Finally, the predicted values are compensated according to classification results. In this paper, the random forest regression and BP neural network algorithm were used to predict silicon content as front-end models. The related applications show that the proposed classification and compensation model improves the accuracy of predicted values effectively.