2021 Volume 61 Issue 6 Pages 1908-1914
According to the actual industrial data from a Consteel electric furnace, a prediction model based on the principal component analysis (PCA) and extremely randomized trees (Extra Tree model) is proposed for end-point phosphorus content. PCA is used to reduce the dimensionality of the input variable affecting the end-point phosphorus content and eliminate the collinearity among the input variables, and then the data transformed by PCA are used as input data for the established Extra tree model. Compared with other feature pre-processing methods, PCA method can greatly improve the regression prediction performance of the Extra Tree model. Finally, the validation by test set indicates that for the PCA-Extra Tree model, the hit rates of end-point phosphorus content are 98%, 96% and 89% with the prediction error range of ±0.005%, ±0.004% and ±0.003%, respectively. The combined PCA-Extra Tree model has achieved the effective prediction for end-point phosphorus content, and provided a good reference for the end-point control and judgment of Consteel electric furnace.
