Convolutional Neural Network‐Based Method for Predicting Oxygen Content at the End Point of Converter

Abstract

The main factor affecting the cleanliness of steel is inclusions, most of which are deoxidation products. The greater the oxygen content fluctuation at the converter's end point, the more unstable the inclusion control. A large amount of charging, operation, and sampling data is collected during the converter smelting, but it is not fully utilized, and the deoxidizing alloy addition still relies on manual experience. Herein, the representative convolutional neural network (CNN) of deep learning is adopted, and the number of convolutional layers, convolutional kernel size, and the number of convolutional kernels are optimized to establish the best prediction model of the converter end point. Its root mean square error, mean absolute error, and mean absolute percentage error are 35.29%, 25.59%, and 7.30%, respectively, which are superior to the back propagation neural network. This CNN prediction model uses 1300 sets of preprocessed data containing 23 indicator variables, of which 1200 sets of data are used for training and 100 sets for model validation. Within the ±50 ppm error scope, the model prediction hit rate can reach 87%, and when within the ±70 ppm error scope, the hit rate is 93%.