Prediction of Hot Metal Silicon Content for Blast Furnace Based on Multi-Layer Online Sequential Extreme Learning Machine
Hot metal silicon content is an important indicator of measuring the smooth operation of the blast furnace. However, the hot metal silicon content cannot be directly detected online. Hence, this paper establishes a prediction model of the hot metal silicon content based on multi-layer online sequential extreme learning machine (ML-OSELM). ML-OSELM is an online sequential version of multi-layer extreme learning machine (ML-ELM), and it can learn data one-by-one or chunk-by-chunk. It uses online sequential extreme learning machine auto-encoder (OS-ELM-AE) to perform layer-by-layer unsupervised sequential learning. The prediction model of the hot metal silicon content based on ML-OSELM is verified by using the production data of the blast furnace. Simulation results demonstrate that this prediction model can accurately and quickly predict the hot metal silicon content. Moreover, this prediction model can provide efficient decision for subsequent operation of the blast furnace.