Research On Modeling And Prediction Of BOF End-point Based On The Furnace Mouth Radiation Information

The Basic Oxygen Furnace (BOF) steelmaking end-point's accurate control online is a problem to be solved urgently in the world smelting industry. The BOF steelmaking is the most important and the most efficient steelmaking method in the world. The accurate control of the steelmaking end-point has important significance for the improvement of the steel quality, energy-saving and emission-reduction, the reduction of production cost and the improvement of the blowing automation level. However, it is difficult to control the end-point carbon and temperature accurately because of many reasons in the blowing process, such as the instable raw materials, the complex chemical reactions, and the strict steel grade scope and so on.To judge the steelmaking end-point accurately, a series of research have been done as below:A BOF mouth radiation multi-frequency information acquisition system was designed, which mainly included the optical fiber optical system and the mouth flame image capturing system. The optical fiber spectrum was divided into the mouth radiation acquisition subsystem, fiber spectrum division multiplexing subsystem and the multi spectrum light intensity detection subsystem. On the one hand, the visible band radiation information was collected completely and divided into the same spectrum energy using the fiber spectrum division multiplexing technology when they arrived at various channel detectors. The spectrum radiation information was processed by the electro-optical transformation and then transmits to the computer for subsequent judgment through the serial port. On the other hand, the flame image information was captured, and then the image characteristic information is extracted by the color space conversion method. Comprehensive consideration on the spectrum light and image characteristic curve, the latent optical radiation blowing law was obtained, which could reflect the steelmaking blowing process. The law could divide into three stages, and the curve change of different stage coincided with the carbon-oxygen reaction in the same stage. The end-point regress prediction model and the end-point neural network prediction model were established based on the respective training sample data and the parameters selected from the optical radiation blowing law. The non-type data space was forecasted by the models and the anticipated effect is achieved. The regress model prediction accuracy is 90.4% when the measurement erro is less than 4s. The neural network model prediction accuracy is 76.9% when the measurement erro is less than 5s, and the model response time is 1.688s. The results show that the anticipated prediction accuracy and the requirements of online end-point judgment are achieved.The method and technology in this study have important reference value for the development of the BOF steelmaking end-point control technology. It will have good effect on promoting the traditional industry technology level and the advancement craft equipment modernization.