Hot metal silicon content is an important indicator for measuring the smooth operation of the blast furnace. However, the hot metal silicon content cannot be directly detected online. Hence, this paper proposes a prediction model of the hot metal silicon content based on the improved multi-layer online extreme learning machine (ML-OSELM). The improved ML-OSLEM algorithm is based on ML-OSELM, the variable forgetting factor (VFF) and the ensemble model. VFF is introduced to make the new coming data get more emphasis. The ensemble model can overcome the overfitting problem of ML-OSELM. This improved algorithm is named as EVFF-ML-OSELM. The real blast furnace production data are used to testify the established prediction model based on EVFF-ML-OSELM. Compared with the prediction models of the hot metal silicon content based on other algorithms, the simulation results demonstrate that the prediction model based on EVFF-ML-OSELM has better prediction accuracy and generalization performance.

高铁硅含量是衡量高炉运行平稳性的重要指标。但是，不能直接在线检测铁水中的硅含量。因此，本文提出了一种基于改进的多层在线极限学习机（ML-OSELM）的铁水硅含量预测模型。改进的ML-OSLEM算法基于ML-OSELM，变量遗忘因子（VFF）和集成模型。引入VFF是为了使新的即将到来的数据得到更多的重视。集成模型可以克服ML-OSELM的过拟合问题。这种改进的算法称为EVFF-ML-OSELM。高炉实际生产数据用于验证基于EVFF-ML-OSELM建立的预测模型。与基于其他算法的铁水硅含量预测模型相比，