Investigating Of Dephosphorization In Converter Steel-making By Applying Statistical Pattern Recognition And Artificial Neural Network

PR (Pattern Recognition) and ANN (Artificial Neural Network) are effective methods of data processing. They are especially useful to the extraction of information from complicated data set influenced by many factors. Applying PR and ANN in the converter steel-making process has the advantages, which could get over deficiencies in general models in a certain extent, improve the hit rate of the end molten steel and guide optimization in production.Through PLS(Partial Least Squares) belongs to PR, a statistical model of phosphorus distribution ratio Lp in LD converter processing was established and the optimized direction of parameters that improves Lp was confirmed from the principal components' mapping fig. For predicting end phosphorus content, an ANN model of end phosphorus content was established based on the improved BP(Background Propagation) network arithmetic. The hit rate of end phosphorus content gets 74% when error of predicting value range is from -0.002% to 0.002% through regulating network parameters and using real-time data. Moreover, four parameters points was designed using PLS-BP method, and its results accord with theory and practice preferably.