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Sulfide Capacity Model for Multicomponent Molten Slag Based on Artificial Neural Network

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Abstract

Sulfide capacity is one of the essential properties of molten slag, which determines the sulfur content in the hot metal during the smelting process. In the present study, an artificial neural network model was developed to predict the sulfide capacity of molten slag over a wide range of components and temperature. A CaO–SiO2–MgO–Al2O3–FeO–TiO2–MnO slag database for sulfide capacity covering most of the previous confident literature was constructed. The influence of activation function, optimization algorithm, and various hidden layers on the performance of the models on sulfide capacity were studied using ten-fold cross-validation. The developed model shows excellent prediction performance, which is capable of accurately predicting complex slag systems, especially TiO2-bearing slag systems. The accuracy and nonlinear prediction ability of the model are verified by experiments, and the iso-sulfide capacity diagrams of CaO–SiO2–Al2O3, CaO–SiO2–TiO2–8 wt pct MgO–14 wt pct Al2O3, and CaO–SiO2–30 pct wt pct TiO2–MgO–14 wt pct Al2O3 slag systems at 1773 K were established with the model.

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Acknowledgments

This study was supported by the National Key R&D Program of China (Grant No. 2018YFC1900500) and the National Natural Science Foundation of China (Grant No. U1902217).

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Authors and Affiliations

  1. Chongqing Key Laboratory of Vanadium-Titanium Metallurgy and Advanced Materials, Chongqing University, No. 174 Shazheng Street, Shapingba District, Chongqing, 400044, P.R. China

    Hao Xie, Jia Guo & Xuewei Lv

  2. College of Materials Science and Engineering, Chongqing University, No. 174 Shazheng Street, Shapingba District, Chongqing, 400044, P.R. China

    Hao Xie, Jia Guo & Xuewei Lv

  3. CISDI Information Technology (Chongqing) Co., Ltd., No. 5-6, Building 2, No. 66 Nongke Avenue, Baishiyi Town, Jiulongpo District, Chongqing, 401122, P.R. China

    Hao Xie, Jiawei Ling & Xin Wang

  4. Baowu Group Environmental Resources Technology Co., Ltd., 1943 Baoyang Road, Baoshan District, Shanghai, 201900, P.R. China

    Zhengde Pang

  5. WMG, The University of Warwick, Coventry, CV4 7AL, UK

    Zhiming Yan

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  1. Hao Xie
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Appendix 1

Appendix 1

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Table AI Summary of the Cs Data for the CaO–SiO2–Al2O3–MgO–MnO–FeO–TiO2 System and Its Subsystems
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Xie, H., Ling, J., Guo, J. et al. Sulfide Capacity Model for Multicomponent Molten Slag Based on Artificial Neural Network. Metall Mater Trans B 54, 3324–3342 (2023). https://doi.org/10.1007/s11663-023-02912-3

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