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Improved back propagation neural network method for predicting sulfur content in hot metal

Metallurgical Research and Technology, ISSN: 2271-3654, Vol: 121, Issue: 1
2024
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  • Mentions
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    • News Mentions
      1
      • 1

Most Recent News

Findings from University of Science and Technology Beijing Yields New Findings on Engineering (Improved Back Propagation Neural Network Method for Predicting Sulfur Content In Hot Metal)

2024 JAN 23 (NewsRx) -- By a News Reporter-Staff News Editor at Network Daily News -- New research on Engineering is the subject of a

Article Description

Blast furnace smelting is a traditional iron-making process. Its product, hot metal, is an important raw material for the production of steel. Steelmaking efficiency can be improved and steel product quality can be stabilized by using proper hot metal. Sulfur is an important indicator reflecting the quality of hot metal, it is necessary to establish an accurate prediction model to predict the sulfur content of hot metal, to effectively guide the production process. There is a non-linear relationship among the factors influencing the desulfurization effect during the blast furnace smelting process, and the back propagation neural network (BPNN) model has a strong ability to solve nonlinear problems. However, BPNN has the disadvantages of slow convergence speed and easy to fall into local minima. To improve the prediction accuracy, an improved algorithm combining Kmeans and BPNN is proposed in this paper. The study showed that compared with the BPNN model and case-based reasoning (CBR) model, the Kmeans-BPNN model has the lowest RMSE and MAPE values, which indicates a high degree of fit and a low degree of dispersion. The Kmeans-BPNN model has the largest HR value, which indicates the highest prediction accuracy. The proposed Kmeans-BPNN prediction model achieves a hit rate of 96%, which is 4.5% higher than before the improvement. It can effectively improve the prediction accuracy of hot metal sulfur content.

Bibliographic Details

Song Zhang; Maoqiang Gu; Yanbing Zong; Zhenyang Wang; Jianliang Zhang; Dewen Jiang; Jing Pang; Shushi Zhang; Ruishuai Si

EDP Sciences

Engineering; Materials Science

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