Abstract
Coke is the main material of blast furnace smelting. The quality of coke is directly related to the quality of finished products of blast furnace smelting, and the evaluation of coke quality often depends on the quality of finished products. However, it is impractical to evaluate coke quality based on finished product quality. Therefore, it is of great significance to establish an artificial intelligence model for quality prediction based on the indicators of coke itself. In this paper, starting from the actual production case, taking the indicators of coke as the feature vector and the quality of finished product as the label, different artificial intelligence models are established. These models predict coke quality, and compare and discuss related algorithms, which lays a foundation for further algorithm improvement.
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Acknowledgements
This work was supported by Universities'Philosophy and Social Science Researches Project in Jiangsu Province (No. 2020SJA0631 & No. 2019SJA0544), Educational Reform Research Project (No.2018XJJG28 & No.2021XJJG09) from Nanjing Normal University of Special Education, Educational science planning of Jiangsu Province(D/2021/01/23), Jiangsu University Laboratory Research Association (Grant NO.GS2022BZZ29).
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Zhang, S., Li, X., Yang, K., Zhu, Z., Wang, L. (2024). Coke Quality Prediction Based on Blast Furnace Smelting Process Data. In: Wang, B., Hu, Z., Jiang, X., Zhang, YD. (eds) Multimedia Technology and Enhanced Learning. ICMTEL 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 535. Springer, Cham. https://doi.org/10.1007/978-3-031-50580-5_10
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DOI: https://doi.org/10.1007/978-3-031-50580-5_10
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