Prediction of carbon content in the metal of final blow period in BOF using neural network

Abstract

Prediction and control of the carbon content after the end of oxygen blow in BOF converter are key points of steel production efficiency. One of the most accurate methods is the dynamic predicting method based on the use of intermediate sublance measurement (TSC probe) when about 85 – 90 % of total oxygen is consumed and on the final period model. Models of the final period are traditionally based on exponential or cubic functions, currently there are developments based on neural network technologies. We investigated the possibility of using a neural network to predict the final carbon content using the results of intermediate sublance measurement (TSO probe) when about 95 % of total oxygen is consumed. As a model of the final period, a two-layer neural network with one hidden layer and an activation function of the Softplus type for all neurons was implemented in software. The input vectors contain initial carbon content and oxygen consumption for the second blow values. The output vector contains the predicted final carbon content, the output training vector - actual final carbon content values. The network was trained on 700 heats data of the training set. The model trained in this way was tested on 232 heats data of the testing set. The prediction errors distribution and values of the mean absolute error and root mean square error for the training and testing sets are correspondingly close. They are also comparable with similar indicators of the heats, the final period of which was carried out without oxygen blow (only flux additions and/or nitrogen blow), and this indicates a high accuracy of the prediction.