A Deep Learning Method for the Endpoint Carbon Prediction in BOF Steelmaking Process

In many basic oxygen furnace (BOF) steelmaking processes, if the furnace endpoint carbon can be monitored in real time, it is a breakthrough for BOF steelmaking intelligence. This paper presents a deep learning model used to predict the endpoint carbon content in BOF steelmaking process. A convolution long short-term memory network based on attention mechanism (CNN-LSTM-AM) model is proposed for time-series data in BOF process to extract spatial-temporal characteristics of time sequence features and a back propagation (BP) model is proposed for pre-furnace data to auxiliary increase the accuracy of the model. The BOF steelmaking data from an actual process were used for the testing, the result shown that 84.34% of prediction result were within the ±0.02 range, which is better than use those two types of data and model individually.