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Prediction Model of Nickel Converter Based on Neural Network Algorithm

  • Applications of Machine Learning in Materials Development and Additive Manufacturing
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Abstract

Based on the vital position of the endpoint prediction of converting, and the fact that the determination of its endpoint still depends on workers’ experience, this paper proposes a prediction model based on BP neural networks and extreme learning machines. Five models are included: (1) back propagation (BP) neural network, (2) genetic algorithm-optimized back propagation neural network (GABP), (3) extreme learning machines (ELM), (4) genetic algorithm-optimized extreme learning machines (GAELM), and (5) particle swarm optimization of extreme learning machine (PSOELM). Based on the historical production data of the Jinchuan Group, these models have been applied for the prediction of air delivery volume and air delivery time of the converter. By comparing the root mean square error (RMSE), goodness of fit (R2) and hit rate within ± 10% relative error range of each model, GAELM has the highest accuracy with a 93.12% hit rate for air delivery volume and a 93.65% hit rate for air delivery time, indicating that GAELM can effectively predict the end of the converter blowing, and provide a good reference for endpoint control and the determination of the nickel converter.

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Acknowledgements

The authors are grateful to the financial support for this project from the National High Technology Research and Development Program of China (No. 2022YFC3902001), the Opening Project of State Key Laboratory of Nickel and Cobalt Resources Comprehensive Utilization and Guangxi Innovation-Driven Development Project (No. Gui 2021AA12006).

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Authors and Affiliations

  1. School of Metallurgy and Environment, Central South University, Changsha, 410083, Hunan, China

    Jiahao Xing, Fenglong Sun, Lei Wang & Ailiang Chen

  2. Gansu Jinchuan Group Co. LTD, Jinchang, 737100, Gansu, China

    Xijun Zhang, Yutian Ma & Sujun Lu

  3. Nandan Nanfang Nonferrous Metals Co. LTD., Nandan, Guangxi, China

    Huanwu Zhan

  4. Department of Materials Science and Engineering, Michigan Technological University, Houghton, MI, 49931, USA

    Jianyang Huang

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  1. Jiahao Xing
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Correspondence to Ailiang Chen.

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Xing, J., Sun, F., Wang, L. et al. Prediction Model of Nickel Converter Based on Neural Network Algorithm. JOM 75, 4538–4549 (2023). https://doi.org/10.1007/s11837-023-05989-y

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