Prediction model of BOF end-point phosphorus content and sulfur content based on LWOA-TSVR

Abstract

Precise control of the end-point phosphorus and sulfur content in converter steelmaking is critical to ensuring steel quality. An end-point prediction model based on LWOA-TSVR is established to better control the BOF end-point content of phosphorus and sulfur. The prediction impact is compared to the models BP, SVM, and TSVR. The results indicate that the LWOA-TSVR model outperforms the other three models in terms of accuracy. And the prediction model is applied to a steel mill. The results showed that the hit rates of phosphorus content and sulfur content were: 96.3%, 81.7%, and 94.8%, 76.9% in the range of ±0.005% and ±0.003%, respectively. The double hit rate was: 87.63% in the range of ±0.005%. Thus, it is demonstrated that the LWOA-TSVR prediction model performs effective prediction of end-point phosphorus and sulfur content with prediction accuracy that exceeds that required by the real steelmaking process in a steel mill.

End‐Point Prediction of Converter Steelmaking Based on Main Process Data

“…Presently, most end‐point prediction models are based on static data and employ various machine‐learning approaches, including support vector regression, [ 9 ] case‐based reasoning (CBR), [ 10 ] multilayer perceptron, [ 11 ] and ensemble models. [ 12 ] These models primarily aim to predict the carbon content and temperature at the converter's end point, [ 13 ] with phosphorus content prediction being secondary [ 14 ] ; research on predicting other elements, such as sulfur content, [ 15 ] is comparatively less developed. To improve prediction accuracy, some researchers employ principal component analysis, [ 16 ] metallurgical mechanism models, and other feature optimization techniques.…”