BOF Spectral Measurement System Design

Oxygen Furnace Steelmaking automatic endpoint prediction and control is the key step in steelmaking process automation and is difficult to realize. Its mainly purpose is controlling the content of carbon(C), phosphorus(P), sulfur(S), manganese(Mn), silicon(Si) and the temperature of the molten steel. The thesis mainly attempted to real-time detect the temperature of the molten steel in the process, providing the effective and auxiliary information to the Oxygen Furnace Steelmaking automatic endpoint prediction system. Firstly, in the thesis, a set of system for the spectral capture, spectral transmission, and temperature measurement was designed to adapt for the complex and severe environment of steel mill. A dynamic model correction was used to improve the traditional dual-wavelength temperature measurement. A SVM model was made for fitting the mapping model between the temperature of the molten steel and the temperature of the furnace’s flame. Then, the noise of the system was discussed, and a wavelet de-nosing was used to denoise the spectrum of the flame, which further enhanced the reliability and accuracy of the system. Finally, the temperature measurement experiment was done at a domestic mills. The experiment data showed that the spectrum of the furnace’s flame carried a lot of important informations related to the molten steel, and the method was feasible. The result obtained in this study will be provided the reliable and useful reference to the BOF automatic endpoint prediction.