A new electric arc furnace （EAF） steelmaking process with increasing hot metal charging ratio and improving slagging regime simultaneously was developed and applied in a 50 t electric arc furnace for more than a yea...A new electric arc furnace （EAF） steelmaking process with increasing hot metal charging ratio and improving slagging regime simultaneously was developed and applied in a 50 t electric arc furnace for more than a year at No. 1 Steelmaking Plant of Shanxi Taigang Stainless Corporation Limited. The essential fact of the new EAF steelmaking process was to charge hot metal in two portions or steps： firstly, 35wt%-40wt% hot metal was pretreated by blowing oxygen in a specially designed reactor for decar burization and improving hot metal temperature and melting premelted slag; secondly, 30wt% hot metal was charged into EAF with high basicity refining slags from ladle furnace （LF）-vacuum degassing furnace （VD） refining process. The results show that the hot metal charging ratio can reach to about 65wt%-70wt% for the new EAF steelrnaking process; meanwhile, the tap-to-tap time of a 50 t EAF can shorten by 5-10 min, the electricity consumption can decrease by 35-50 kW·h/t, the lime consumption can reduce by 10.5 kg/t of molten steel, and the content of harmful heavy metals in molten steel can be easily controlled to less than the upper limits of aimed steel specification or grade compared with the traditional EAF steelmaking process. In addition, the dephosphorization ability shows a slight strengthening, however, a small degree of lessening for desulphurization ability is observed for the new EAF steelmaking process, but the weakness of desulphurization ability cannot become an obstacle to its further application since a stronger desulphurization ability can be achieved during secondary refining of LF coupled with VD after EAF steelmaking process.展开更多
In the present study,a novel approach based on an evolutionary wavelet neural network(EWNN)is proposed to estimate the slag quality in an electric arc furnace(EAF)employing power quality indices.In the EWNN,an evoluti...In the present study,a novel approach based on an evolutionary wavelet neural network(EWNN)is proposed to estimate the slag quality in an electric arc furnace(EAF)employing power quality indices.In the EWNN,an evolutionary method is applied to train the parameters for a combination of neural networks and wavelets.I For this purpose,all of the electrical parameters for six melting processes are measured with a power quality analyzer,attached to the secondary component of an EAF transformer at a Saba steel complex,to estimate the foaming slag quality.Experimental results on various combinations of measured electrical parameters,applying the designed EWNN estimator,demonstrate that utilizing five leading indicators leads to the highest precision.The obtained 99%accuracy for estimating the foaming slag quality by EWNN compared to the other methods illustrates the proposed method's efficiency.展开更多
文摘A new electric arc furnace （EAF） steelmaking process with increasing hot metal charging ratio and improving slagging regime simultaneously was developed and applied in a 50 t electric arc furnace for more than a year at No. 1 Steelmaking Plant of Shanxi Taigang Stainless Corporation Limited. The essential fact of the new EAF steelmaking process was to charge hot metal in two portions or steps： firstly, 35wt%-40wt% hot metal was pretreated by blowing oxygen in a specially designed reactor for decar burization and improving hot metal temperature and melting premelted slag; secondly, 30wt% hot metal was charged into EAF with high basicity refining slags from ladle furnace （LF）-vacuum degassing furnace （VD） refining process. The results show that the hot metal charging ratio can reach to about 65wt%-70wt% for the new EAF steelrnaking process; meanwhile, the tap-to-tap time of a 50 t EAF can shorten by 5-10 min, the electricity consumption can decrease by 35-50 kW·h/t, the lime consumption can reduce by 10.5 kg/t of molten steel, and the content of harmful heavy metals in molten steel can be easily controlled to less than the upper limits of aimed steel specification or grade compared with the traditional EAF steelmaking process. In addition, the dephosphorization ability shows a slight strengthening, however, a small degree of lessening for desulphurization ability is observed for the new EAF steelmaking process, but the weakness of desulphurization ability cannot become an obstacle to its further application since a stronger desulphurization ability can be achieved during secondary refining of LF coupled with VD after EAF steelmaking process.
文摘In the present study,a novel approach based on an evolutionary wavelet neural network(EWNN)is proposed to estimate the slag quality in an electric arc furnace(EAF)employing power quality indices.In the EWNN,an evolutionary method is applied to train the parameters for a combination of neural networks and wavelets.I For this purpose,all of the electrical parameters for six melting processes are measured with a power quality analyzer,attached to the secondary component of an EAF transformer at a Saba steel complex,to estimate the foaming slag quality.Experimental results on various combinations of measured electrical parameters,applying the designed EWNN estimator,demonstrate that utilizing five leading indicators leads to the highest precision.The obtained 99%accuracy for estimating the foaming slag quality by EWNN compared to the other methods illustrates the proposed method's efficiency.
