Multi-model coupling-based dynamic control system of ladle slag in argon blowing refining process

Since the current slagging of argon blowing refining process is relatively fixed,which cannot adapt to the fluctuation of converter smelting process,it poses the problems of poor metallurgical property of refining slag and a large amount of molten heel.An optimization system coupled with multiple models was proposed to dynamic control the ladle slagging in the argon blowing refining process.It can compile the optimal dynamic slagging scheme in real time under the guarantee of deoxidation performance and reasonable fluidity.The argon blowing refining slag composition range of CaO/Al_(2)O_(3)=1.3-1.7,CaO/SiO_(2)=6-12,w(MgO)=2%-6% was determined based on FeO activity and liquidus temperature by equilibrium thermodynamic calculation.In addition,it demonstrated better performance in the viscosity prediction task of the presented Visual Geometry Group 16-like one-dimensional convolutional neural network deep learning algorithm versus the Random Forest ensemble learning algorithm,as the adjusted coefficients of determination were 0.9712 and 0.9637,respectively.After the system was applied in operation,the argon blowing refining process was stable,and the steel yield was enhanced,which promoted the intelligent steelmaking level while achieving the cost reduction and efficiency improvement.