Prediction of evolution behavior of slag eye in steel ladle based on BP neural network
The behavior of slag eye during the refining process of bottom blown steel ladle plays a crucial role in regulating the steel composition and the quantity of inclusions.Due to the complex working condition,high temperature,and high costs at the production site,on-site testing poses certain difficulties and dangers,and the accuracy of the test results cannot be guaranteed.In order to accurately investigate the effects of the position of the bottom blown hole,the gas flow rate of a single hole,and the thickness of the slag layer on the slag eye area in the steel ladle refining process,using a 1∶5 similarity ratio,a water model was established using a 150 t actual industrial ladle as a prototype.The BP neural network algorithm was used to fit experimental data and generate a model to predict the evolution behavior of slag holes during the refining process.The analysis shows that when the number of neurons in the hidden layer is 16 and the number of iterations Epoch is 60 000,the model loss function Error value reaches its minimum,and the determination coefficient R2 is 93.439％,the model demonstrates excellent performance,and its prediction accuracy meets industrial requirements,effectively guiding industrial production.
BP neural network150 t industrial ladleslag eyewater modelsingle hole bottom blowing
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NSTL
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