Development and application of abnormal state prediction model for smelting process based on flue gas analysis

In order to reduce the adverse impacts of abnormal conditions that occur during the converter smelting process on the stability of steel production, efficiency, and cost control. This paper, through a deep analysis of the changing relationship of the volume fraction curves of CO, CO₂ and O₂ in the flue gas during actual production, has developed and integrated a prediction model for abnormal states in the smelting process by combining Python language and PLC programming. The model mainly encompasses the following components: a prediction model for failure to ignite at the start of blowing, a prediction model for the molten pool temperature in the early stage of smelting, a prediction model for re-drying and splashing during the blowing process, and a prediction model for the double-slag operation during the smelting process. After the implementation of the model, it provides strong guidance for stabilizing the process operation. It effectively eliminates abnormal interruptions during the blowing process. Specifically, the proportion of re-drying in the process is reduced by 10%, the splashing rate is controlled to less than 0.5%, the amount of iron entrained in the slag during the double-slag operation is decreased by 50%, and the dephosphorization rate in the early stage is stabilized at approximately 80%. The stabilization of the smelting process has significantly enhanced technical and economic indicators. The converter smelting cycle is maintained at around 28 min. The qualified rate of the final carbon content for low-alloy steel has increased to around 88%. The proportion of first-time carbon pulling and steel tapping reaches over 95%. The mass fraction of oxygen at the end of the low-alloy steel smelting is stably maintained at 370×10^-6-380×10^-6, and the consumption of steel and iron materials in the converter is stabilized within the range of 1 030-1 033 kg/t.