Prediction of end-point temperature in electric arc furnace based on e-FCNN
The development of the EAF steelmaking short process is an important strategic way to realize the green development of the iron and steel industry.The end-point control of EAF steelmaking determines the quality of tap-ping and smelting efficiency,especially the end-point temperature control.The establishment of the prediction model to achieve the EAF end-point temperature prediction in advance helps to adjust the smelting process in time and real-ize the fast and efficient tapping operation.The EAF end-point temperature prediction model is mainly divided into the mechanism model and the data-driven model.Data-driven modeling is the main research direction at present,but the modeling process usually relies on a large amount of historical data,and it is difficult to achieve accurate end-point temperature prediction under small sample data conditions.Therefore,tightly combined with the metallurgical mechanism,with artificial intelligence algorithms as the core,established a highly adaptive EAF end-point tempera-ture prediction model.The input parameters of the model were obtained by metallurgical mechanism and Pearson da-ta correlation analysis.Based on the FCNN algorithm,the early stopping strategy was introduced,the e-FCNN al-gorithm was proposed to prevent the overfitting phenomenon of the FCNN algorithm,and the end-point temperature prediction model of the EAF was established based on the e-FCNN algorithm.Simulation results show that the e-FCNN model end-point temperature prediction error is within±5 ℃ with a hit rate of 93.33％.In addition,CART,RF,e-SVR,and v-SVR models were developed using hyperparametric random grid search under the condi-tion of small-sample historical data,and the results show that the accuracy of the e-FCNN model is significantly bet-ter than others.Using the e-FCNN model to continuously track the actual production of 30 heats,the hit rate rea-ches 96.7％when the prediction error is within±6 ℃,which can effectively guide the production.In the future,further improvement of the combination of mechanism and data-driven is the development direction of EAF end-point temperature prediction models.
electric arc furnaceend-point predictionend-point temperaturemachine learningsmall sample
NETL
NSTL
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