基于灰色关联分析和机器学习的高炉铁水硅含量预测

doi:10.11896/cldb.23080170

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Abstract The silicon content in molten iron is commonly used as an important indicator to characterize the ‘chemical heat’ and economic benefits of the blast furnace. Establishment a timely and accurate prediction model for the silicon content in molten iron is of great significance for stable production of the blast furnace. In this work, an innovative prediction model was established by considering the time delay and the grey relational analysis (GRA) combining principal component analysis (PCA) and long short-term memory network (LSTM). Firstly, from the perspective of the reduction-oxidation behavior of silicon, the operating parameters, state parameters and result parameters relevant to the silicon content of blast furnace iron in production were selected. Secondly, through GRA, the degree of correlation between each parameter and the silicon content in molten iron was determined, and the main factors affecting the silicon content in molten iron were screened out. On this basis, a total of 8 544 sets of production data for a 3 200 m³ blast furnace in China were dimensionally reduced in one year. A prediction model was established using the 13 principal components obtained as input variables and the silicon content in molten iron as output variables. The prediction model has reasonable generalization ability, robustness and accuracy. The analysis of prediction results shows that the determination coefficient (R²) of the model is 0.929 7, the mean square error (MSE) and mean absolute error (MAE) are 0.001 2 and 0.025 4 respectively, realizing accurate prediction of the silicon content of blast furnace molten iron.

Key words： blast furnace grey relational analysis machine learning silicon content forecasting model

Published: 25 October 2024 Online: 2024-11-05

CLC number:	TF57
	TP29

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	Articles by authors
	QIU Guoxing
	CAI Mingchong
	ZHANG Yi
	SU Bingrui
	YANG Yongkun
	LI Xiaoming

Cite this article:

QIU Guoxing,CAI Mingchong,ZHANG Yi, et al. Prediction of Silicon Content in Blast Furnace Molten Iron Using Grey Correlation Analysis and Machine Learning[J]. Materials Reports, 2024, 38(20): 23080170-6.

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http://www.mater-rep.com/EN/10.11896/cldb.23080170 OR http://www.mater-rep.com/EN/Y2024/V38/I20/23080170

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