Prediction Model of End-point Manganese Content for BOF Steelmaking Process

Abstract

Through analyzing the factors that influence end-point manganese content during BOF steelmaking process, multiple linear regression model for prediction of end-point manganese content was obtained on the basis of actual production data. Given the advantages of artificial neural network, it was used to predict end-point manganese content during BOF steelmaking process, and BP neural network model was established. By means of combining the characteristics of genetic algorithm and BP neural network completely, a combined GA-BP neural network model was established. The verification and comparison of the above three models show that the combined GA-BP neural network model has the highest prediction accuracy. The hit rate of the combined GA-BP neural network model is 90% and 84% respectively when predictive errors of the model are within ±0.03% and ±0.025%. Compared with two models aboved, the combined GA-BP neural network model could provide the most accurate prediction of end-point manganese content, and thus represents a good reference for real production.

Physical and Mathematical Modeling of Multiphase Flows in a Converter

“…[49][50][51][52][53][54][55][56][57][58] Therefore, this paper will review the developments in physical modeling and computational simulations of the fluid flow behavior in BOF steelmaking process. Details of fundamental modeling methods, supersonic oxygen jet behavior, stirring and mixing characteristics in the bath, splashing and droplet generation behavior and energy transfer performance will be presented, and finally outlook and future trends will also be discussed.…”

Kinetic Study on the Oxidation of Elements in Hot Metal during Vanadium‐Extraction Process

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Parameter Estimation by Inverse Solution Methodology Using Genetic Algorithms for Real Time Temperature Prediction Model of Ladle Furnace

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