Dynamic Prediction of the Silicon Content in the Blast Furnace using LSTM-RNN-Based Models

In blast furnace (BF) ironmaking process, the silicon content of hot metal is regarded as an important index to evaluate the stability of blast furnace and the quality of pig iron. In reality, the reactions inside BF with the features of dynamic, nonlinear and hysteretic impede accurate mathematical modeling. Data-driven models predict silicon content variation tendency through the analysis of historical data, which have achieved beneficial results. However, existing data-driven models are inefficient to handle time series specifically. Therefore, this paper proposes a recurrent neural network (RNN) which is apt at processing sequential data for silicon content prediction. A Long short-term memory (LSTM) unit is introduced to solve long-term dependency problems in conventional recurrent neural networks. Experiment results indicate that LSTM-RNN shows stronger adaptability and higher prediction accuracy at 86%-94% than that of other data-driven models, which is of great significance for controling the silicon content of hot metal.