Prediction of Permeability Index of Blast Furnace Based on Online Sequential Extreme Learning Machine

Permeability index of the blast furnace is one of the vital monitoring parameters to reflect the operation status of the blast furnace. At present, there are few prediction models for the permeability index at home and abroad. Therefore, this paper proposes to establish a prediction model of the permeability index by using online sequential extreme learning machine (OS-ELM) combined with wavelet analysis, and this paper compares it with the prediction models established by extreme learning machine (ELM), support vector machine (SVM) and BP neural network algorithm. The simulation results show that the prediction model based on OS-ELM has better accuracy than others.