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Intelligent Recommendation Framework for Iron Ore Matching Based on SA2PSO and Machine Learning to Reduce CO2 Emissions

  • Applications of Machine Learning in Materials Development and Manufacturing
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Abstract

Optimization of sinter ore allocation is a key step in the steel production process, which has become the most important measure for steel enterprises to effectively reduce cost, improve quality, save energy and reduce emissions. In this paper, the first intelligent recommendation model for the sintering dosing scheme considering cost, quality and carbon emission in the sintering process is developed using the SA2PSO optimization algorithm combined with machine learning. To improve the optimization accuracy and speed of the PSO algorithm, the SA2PSO algorithm is based on the adaptive and simulated annealing operations to achieve different stages of particle optimization, and the convergence speed is reduced by about 5 s compared with PSO while ensuring the optimization accuracy. Based on the optimal solution, the random forest algorithm was used to achieve a high-accuracy prediction of quality. For carbon emission, this paper for the first time to our knowledge converts carbon tax into iron ore sintering cost, establishes a dosing scheme considering cost carbon emission and quality and finally establishes an intelligent recommendation model for sintering dosing based on VIKOR. The model can be used for optimal ore allocation in sintering to achieve low cost, high quality and low CO2 emission in iron ore sintering.

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Acknowledgements

The authors gratefully acknowledge the financial support provided by the National Natural Science Foundation of China (52174291), the Beijing New-star Plan of Science and Technology (Z211100002121115), the China Postdoctoral Science Foundation (2021M690369), the Central Universities Foundation of China (06500170), and the Guangdong Basic & Applied Basic Research Fund Joint Regional Funds-Youth Foundation Projects (2020A1515111008).

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Authors and Affiliations

  1. School of Automation and Electrical Engineering, University of Science and Technology Beijing, 30th Xueyuan Road, Haidian District, Beijing, 100083, People’s Republic of China

    Qing Li

  2. School of Metallurgical and Ecological Engineering, University of Science and Technology Beijing, 30th Xueyuan Road, Haidian District, Beijing, 100083, People’s Republic of China

    Jianliang Zhang, Jiabao Wang & Zhengjian Liu

  3. Institute of Artificial Intelligence, University of Science and Technology Beijing, 30th Xueyuan Road, Haidian District, Beijing, 100083, People’s Republic of China

    Yunfei Ma & Yaozu Wang

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  1. Yunfei Ma
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  2. Jianliang Zhang
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  5. Yaozu Wang
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Correspondence to Yaozu Wang.

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Ma, Y., Zhang, J., Li, Q. et al. Intelligent Recommendation Framework for Iron Ore Matching Based on SA2PSO and Machine Learning to Reduce CO2 Emissions. JOM 76, 120–129 (2024). https://doi.org/10.1007/s11837-023-06211-9

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