Prediction Model of End-Point Phosphorus Content in EAF Steelmaking Based on BP Neural Network with Periodical Data Optimization

Abstract

The phosphorus (P) content of molten steel is of great importance for the quality of steel products in the electric arc furnace (EAF) steelmaking process. At present, the initial conditions of smelting process in the prediction of end-point P content are still the core part. However, few studies focus on the influence between process data and end-point P content. In this research, the relationships between process data and end-point P content are explored by a BP neural network. Based on the theoretical analysis, influencing factors with high correlation were selected. The prediction model of P content coupled with process data and end-point P content is established. On this basis, the model is optimized with process data of oxygen supply and the time of the first addition of lime. Compared with the practical production data, the results indicate that the hit rate of the model optimized is 87.78% and 75.56% when prediction errors are within ±0.004 and ±0.003 of P content. The model established has achieved the effective prediction for end-point P content, and provided a reference for the control of P content in practical production.