The ironmaking in blast furnace

with large delay and complex conditions

is a dynamic process. The traditional methods for prediction of silicon content in hot metal are mostly based on the statistics or the simple neural networks

leading to lower accuracy. However

a model based on the long short-term memory-recurrent neural network (LSTM-RNN) is proposed to exploit the characteristics of the mutual information before and after the time series in this paper. The independent variables are selected according to the time series trend and the correlation coefficient. After that

the silicon content is predicted according to the input variables by optimizing the parameters automatically. In order to verify the constructed model

the extremely complex production data is used to compare the LSTM-RNN and simple RNN models. Remarkably

the result shows that the prediction error of LSTM-RNN model is stable and the prediction accuracy is high.