A Hybrid Modeling Method Based on Expert Control and Deep Neural Network for Temperature Prediction of Molten Steel in LF

The temperature control of molten steel in ladle furnace (LF) has a critical impact on steelmaking production. In this work, production data were collected from a steelmaking plant and a hybrid model based on expert control and deep neural network (DNN) was established to predict the molten steel temperature in LF. In order to obtain the optimal DNN model, the trial and error method was used to determine the hyperparameters. And the optimal architecture of DNN model corresponds to the hidden layers of 4, hidden layer neurons of 35, iterations of 3 000, and learning rate of 0.2. Compared with the multiple linear regression model and the shallow neural network model, the DNN model exhibits stronger generalisation performance and higher accuracy. The coefficient of determination (R-2), correlation coefficient (r), mean square error (MSE), and root-mean-square error (RMSE) of the optimal DNN model reached 0.897, 0.947, 2.924, 1.710, respectively. Meanwhile, in the error scope of temperature from - 5 to 5 degrees C, the hit ratio of the hybrid model acquired 99.4%. The results demonstrate that the proposed model is effective to predict temperature of molten steel in LF.