基于PCA决策的PSO-BP模型预测高炉铁水产量

doi:10.13228/j.boyuan.issn1006-9356.20230352

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Abstract The yield of molten iron is an important economic indicator to measure the capacity efficiency of steel plants, and its accurate prediction according to the characteristics of furnaces is conducive to capacity structure optimization of steel plants and promotes the stability and high yield of blast furnace. In order to improve the prediction accuracy of molten iron yield, combined with machine learning theory, a hybrid prediction model of particle swarm optimization-back propagation (PSO-BP) based on principal component analysis (PCA) decision-making was proposed based on the annual production and smelting data of a domestic steel plant in 2022. To begin with, principal component analysis was used to reduce the dimensionality of the original data set, and then the particle swarm search algorithm was used to optimize the weight matrix of BP neural network, which successfully solved the problem that BP neural network had slow convergence speed and was easy to fall into local optimality. Finally, combined with the ironmaking theory, the input vector and topology of the model were determined according to the results of principal component analysis. The testing results show that the prediction error of the model is smaller than that of other traditional models, and the accuracy rate is 99.8% when the error range is ±50 t, which accurately realizes the prediction of molten iron yield for blast furnace, effectively guides the transfer scheduling of molten iron ladles, and provides data support for blast furnace parameter regulation.

Key words： blast furnace prediction of molten iron yield principal component analysis PSO-BP model molten iron ladle transfer scheduling

Received: 01 June 2023

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	Articles by authors
	DUAN Yifan
	LIU Xiaojie
	LI Xin
	LIU Ran
	LI Hongwei
	ZHAO Jun

Cite this article:

DUAN Yifan,LIU Xiaojie,LI Xin等. Molten iron yield predicting of blast furnace using PSO-BP model based on PCA decision[J]. China Metallurgy, 2023, 33(11): 114-126.

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http://www.zgyj.ac.cn/EN/10.13228/j.boyuan.issn1006-9356.20230352 OR http://www.zgyj.ac.cn/EN/Y2023/V33/I11/114

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