Application of improved EMD-Elman neural network to predict silicon content in hot metal

SONG Jinghua; YANG Chunjie; ZHOU Zhe; LIU Wenhui; MA Shuyan

doi:10.11949/j.issn.0438-1157.20151847

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Application of improved EMD-Elman neural network to predict silicon content in hot metal

更新时间：2025-12-10

- Application of improved EMD-Elman neural network to predict silicon content in hot metal
- CIESC Journal Issue 3, Pages: 729-735(2016)
- 作者机构：
  
  浙江大学控制科学与工程学院,浙江,杭州,310027
- 作者简介：
- 基金信息：
- DOI：10.11949/j.issn.0438-1157.20151847
  CLC： F513/TF512
- Online First：12 January 2016，
  
  Published：12 January 2016
- 稿件说明：
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SONG Jinghua, YANG Chunjie, ZHOU Zhe, et al. Application of improved EMD-Elman neural network to predict silicon content in hot metal[J]. CIESC Journal, 2016, (3): 729-735.
DOI：

SONG Jinghua, YANG Chunjie, ZHOU Zhe, et al. Application of improved EMD-Elman neural network to predict silicon content in hot metal[J]. CIESC Journal, 2016, (3): 729-735. DOI： 10.11949/j.issn.0438-1157.20151847.

摘要

针对高炉炼铁过程的多尺度和动态特征

建立了基于经验模态分解(empirical mode decomposition

EMD)和Elman神经网络的铁水硅含量预测模型。该模型先采用EMD将硅含量序列分解成有限个、相对平稳的本征模函数(intrinsic mode function

IMF)和剩余分量;然后

分别对每个IMF和剩余分量建立Elman神经网络子模型;为了进一步提高预测精度

将子模型的结果进行加权融合

并利用粒子群算法进行权值的寻优

最终获得硅含量的预测结果。将该模型用于某钢厂铁水硅含量的预报

实验结果证实了该方法的有效性。

Abstract

To handle the multiscale and dynamic characteristics of blast furnace ironmaking process

a soft sensor model based on empirical mode decomposition (EMD) and Elman neural network is proposed. First

the original silicon content dataset is decomposed into a finite collection of intrinsic mode functions (IMFs) and a residue by EMD

obtaining relatively stationary sub-series from original data set. Second

each IMF and the residue are utilized to establish the corresponding Elman neural network model. To further improve the accuracy of prediction

the result of each sub-series is multiplied by a weight and then summed up to obtain the final silicon content. Here

all the weights are optimized by particle swarm optimization (PSO). The model was applied to the prediction of silicon content of blast furnace in a steel mill

and the result proved the effectiveness of the proposed method.

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