Metals 2023, 13, 2. https://doi.org/10.3390/met13010002

Basic Oxygen Furnace (BOF) steelmaking is an important way for steel production. Correctly recognizing different blowing periods and abnormal refining states is significant to ensure the normal production process, while accurately predicting the end-point time helps to increase the first-time qualification rate of molten steel. Since the decarburization products CO and CO2 are the main compositions of off-gas, information of off-gas is explored for BOF steelmaking control. However, the problem is that most of the existing research directly gave the proportions of CO and CO2 as model input but barely considered the variation information of off-gas to describe the production state. Therefore, this work proposes a new method, defined as FDA-PP, to describe and predict the BOF steelmaking process from the metallurgical dynamics viewpoint. The FDA-PP method extracts the total proportion of CO and CO2 and its first-order derivative as dynamics features of steelmaking process via functional data analysis (FDA), which indicate the reaction velocity and acceleration of decarburization reaction. Experiments on a real production dataset demonstrate that the FDA-PP method has higher production state recognition accuracy than classical methods, achieving up to 90.94% for splashing anomaly.