Improved multi-layer online sequential extreme learning machine and its application for hot metal silicon content

摘要

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. (C) 2020 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.