Enhanced steelmaking cost optimization and real-time alloying element yield prediction: a ferroalloy model based on machine learning and linear programming

Rui-xuan Zheng, Yan-ping Bao, Li-hua Zhao, Li-dong Xing

钢铁研究学报(英文版) ›› 2025, Vol. 32 ›› Issue (4) : 904-919.

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钢铁研究学报(英文版) ›› 2025, Vol. 32 ›› Issue (4) : 904-919. DOI: 10.1007/s42243-024-01313-3

Enhanced steelmaking cost optimization and real-time alloying element yield prediction: a ferroalloy model based on machine learning and linear programming

  • Rui-xuan Zheng1, Yan-ping Bao1, Li-hua Zhao2, Li-dong Xing1
作者信息 +

Enhanced steelmaking cost optimization and real-time alloying element yield prediction: a ferroalloy model based on machine learning and linear programming

  • Rui-xuan Zheng1, Yan-ping Bao1, Li-hua Zhao2, Li-dong Xing1
Author information +
文章历史 +

Abstract

The production of ferroalloys is a resource-intensive and energy-consuming process. To mitigate its adverse environmental effects, steel companies should implement a range of measures aiming at enhancing the utilization rate of ferroalloys. Therefore, a comprehensive ferroalloy model was proposed, incorporating a prediction model for alloying element yield based on case-based reasoning and support vector machine (CBR-SVM), along with a ferroalloy batching model employing an integral linear programming algorithm. In simulation calculations, the prediction model exhibited exceptional predictive performance, with a hit rate of 96.05% within 5%. The linear programming ingredient model proved effective in reducing costs by 20.7%, which was achieved through accurate adjustments to the types and quantities of ferroalloys. The proposed method and system were successfully implemented in the actual production environment of a specific steel plant, operating seamlessly for six months. This implementation has notably increased the product quality of the enterprise, with the control rate of high-quality products increasing from 46% to 79%, effectively diminishing the consumption and expenses associated with ferroalloys. The reduced usage of ferroalloys simultaneously reduces energy consumption and mitigates the adverse environmental impact of the steel industry.

Key words

Steel industry / Ferroalloy / Case-based reasoning / Energy conservation / Consumption reduction

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Rui-xuan Zheng, Yan-ping Bao, Li-hua Zhao, . Enhanced steelmaking cost optimization and real-time alloying element yield prediction: a ferroalloy model based on machine learning and linear programming[J]. 钢铁研究学报(英文版), 2025, 32(4): 904-919 https://doi.org/10.1007/s42243-024-01313-3
Rui-xuan Zheng, Yan-ping Bao, Li-hua Zhao, et al. Enhanced steelmaking cost optimization and real-time alloying element yield prediction: a ferroalloy model based on machine learning and linear programming[J]. Journal of Iron and Steel Research International, 2025, 32(4): 904-919 https://doi.org/10.1007/s42243-024-01313-3

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