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Establishment and Application of Steel Composition Prediction Model Based on t-Distributed Stochastic Neighbor Embedding (t-SNE) Dimensionality Reduction Algorithm

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Abstract

To meet the goals of the national "Dual Carbon" strategy and reduce energy consumption in the steel industry, accurate prediction of steel composition is crucial for precise control over alloy addition in steelmaking. Several models have been created to predict the composition of the converter endpoint with a high level of accuracy. However, the different shortcomings of each have prevented large-scale application in real production environments. CBR prediction model has limited scope to solve the problem. CNN model has complex data processing and no memory. RELM model has randomly given input layer weights and hidden layer deviations. In this study, correlation analysis was used to analyze the factors influencing the carbon content at the endpoint of converter steelmaking. A feasible model was established and applied to predict the carbon content at the endpoint of converter using t-distributed stochastic neighbor embedding (t-SNE), particle swarm optimization (PSO), and backpropagation (BP) neural network. The learning rate, training times, and hidden layer nodes number of the prediction model were optimized. The prediction hit ratios for the carbon content in the error ranges of ± 0.003%, ± 0.01%, and ± 0.02% are 61%, 86%, and 98%, respectively. Meanwhile, apply the established model to actual production, the carbon content of the product can be stably controlled between the lower and median limits, the control effect is significantly better than traditional methods. The results demonstrate that the t-SNE-PSO-BP model performs better than the known models. The accurate prediction of the carbon content at the endpoint of converter can greatly contribute to realizing a “narrow composition control” of the molten steel. Realize accurate prediction of carbon content at the endpoint of converter smelting, and has been effectively applied to industrial production.

Graphical Abstract

Under the traditional method of predicting the endpoint carbon content of the converter, the hit rate of the middle and lower limits of the carbon content in the product is 48%. The t-SNE-PSO-BP model predicts the carbon content at the endpoint of the converter model, and the product carbon content can be controlled stably between 0.21–0.23%. According to the study results and actual application effects, use the t-SNE-PSO-BP model to predict the carbon content at the endpoint of the converter is appropriate, and is conducive to the “narrow composition control” of the steel composition in the converter steelmaking process.

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Acknowledgements

This work was supported by the Guangdong Basic and Applied Basic Research Foundation (No. 2022A1515110062), and Young Elite Scientists Sponsorship Program by CAST (No. 2022QNRC001). The authors wish to express their gratitude to the foundation for providing financial support.

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Authors and Affiliations

  1. State Key Lab of Advanced Metallurgy, University of Science & Technology Beijing, Beijing, 100083, People’s Republic of China

    Xin Liu, Yanping Bao & Chao Gu

  2. Metallurgical and Ecological Engineering School, University of Science & Technology Beijing, Beijing, 100083, People’s Republic of China

    Lihua Zhao

  3. Shunde Innovation School, University of Science and Technology Beijing, 528399, Guangdong, People’s Republic of China

    Chao Gu

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  1. Xin Liu
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Correspondence to Yanping Bao or Lihua Zhao.

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Liu, X., Bao, Y., Zhao, L. et al. Establishment and Application of Steel Composition Prediction Model Based on t-Distributed Stochastic Neighbor Embedding (t-SNE) Dimensionality Reduction Algorithm. J. Sustain. Metall. (2024). https://doi.org/10.1007/s40831-024-00798-2

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