Prediction of converter steelmaking end point based on Bayesian optimization GBDT
In order to improve the prediction accuracy of end point carbon content and temperature in converter steelmaking,a prediction model of end point carbon content and temperature in converter steelmaking based on Bayesian optimized gradient lifting decision tree(BOA_GBDT)was proposed.It is compared and analyzed with the end-point carbon temperature prediction models of the base model radial basis function(RBF),support vector machine(SVM),gradient boosting decision tree(GBDT)and Bayesian algorithm optimized radial basis function(BOA_RBF),support vector machine(BOA_SVM)end point carbon temperature prediction model.The experimental results show that BOA_GBDT has the smallest error index and the highest hit rate.The hit rate of carbon content at the end point is 96.2%within the error interval of±0.01%;the hit rate of the end temperature is 92.1%within the error interval of±10℃.Bayesian optimization algorithm can significantly improve the performance of the model,more accurately judge the end-point carbon content and temperature of converter steelmaking,and provide a more reliable basis for converting qualified molten steel.
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