Research on hot metal Si-content prediction based on LSTM-RNN

LI Zelong; YANG Chunjie; LIU Wenhui; ZHOU Heng; LI Yuxuan

doi:10.11949/j.issn.0438-1157.20171534

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Research on hot metal Si-content prediction based on LSTM-RNN

更新时间：2025-12-10

- Research on hot metal Si-content prediction based on LSTM-RNN
- CIESC Journal Issue 3, Pages: 992-997(2018)
- 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.0438-1157.20171534
  CLC： Q028.8
- Online First：05 March 2018，
  
  Published：05 March 2018
- 稿件说明：
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LI Zelong, YANG Chunjie, LIU Wenhui, et al. Research on hot metal Si-content prediction based on LSTM-RNN[J]. CIESC Journal, 2018, (3): 992-997.
DOI：

LI Zelong, YANG Chunjie, LIU Wenhui, et al. Research on hot metal Si-content prediction based on LSTM-RNN[J]. CIESC Journal, 2018, (3): 992-997. DOI： 10.11949/j.issn.0438-1157.20171534.

摘要

针对高炉炼铁是一个动态过程，具有大延迟，工况复杂的特性。采用LSTM-RNN模型进行硅含量预测，充分发挥了其处理时间序列时挖掘前后关联信息的优势。首先根据时间序列趋势及相关系数选择自变量，并采用复杂工况的实际生产数据进行验证。然后用程序自动求解最优参数进行硅含量预测。最后将LSTM-RNN模型与PLS模型及RNN模型的结果进行对比，验证该方法的优势。研究发现LSTM-RNN模型预测误差稳定，预测精度较高，比传统的统计学及神经网络方法取得了更好的预测精度。

Abstract

The ironmaking in blast furnace

with large delay and complex conditions

is a dynamic process. The traditional methods for prediction of silicon content in hot metal are mostly based on the statistics or the simple neural networks

leading to lower accuracy. However

a model based on the long short-term memory-recurrent neural network (LSTM-RNN) is proposed to exploit the characteristics of the mutual information before and after the time series in this paper. The independent variables are selected according to the time series trend and the correlation coefficient. After that

the silicon content is predicted according to the input variables by optimizing the parameters automatically. In order to verify the constructed model

the extremely complex production data is used to compare the LSTM-RNN and simple RNN models. Remarkably

the result shows that the prediction error of LSTM-RNN model is stable and the prediction accuracy is high.

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