The process model for BOF process can be applied to predict the liquid steel composition and bath temperature during the whole steelmaking process. On the basis of the traditional three-stage decarburization theory, t...The process model for BOF process can be applied to predict the liquid steel composition and bath temperature during the whole steelmaking process. On the basis of the traditional three-stage decarburization theory, the concept of mixing degree was put forward, which was used to indicate the effect of oxygen jet on decarburization. Furthermore, a more practical process model for BOF steelmaking was developed by analyzing the effect of silicon, manganese, oxygen injection rate, oxygen lance height, and bath temperature on decarburization. Process verification and end-point verification for the process model have been carried out, and the verification results show that the predic- tion accuracy of carbon content reaches 82.6% （the range of carbon content at the end-point is less than 0. 1wt%） and 85.7% （the range of carbon content at end-point is 0. 1wt% -0.7wt%） when the absolute error is less than 0.02wt% and 0.05wt%, respectively.展开更多
Artificial intelligence techniques have been used to predict basic oxygen furnace(BOF) end-points. However,the main challenge is to effectively reduce the input nodes as too many input nodes in neural network increase...Artificial intelligence techniques have been used to predict basic oxygen furnace(BOF) end-points. However,the main challenge is to effectively reduce the input nodes as too many input nodes in neural network increase complexity,decrease accuracy and slow down the training speed of the network.Simply picking-up variables as input usually influence validity of model.It is quite necessary to develop an effective method to reduce the number of input nodes whereby to simplify the network and improve model performance.In this study,a variable-filtrating technique combining both metallurgical mechanism model and partial least-squares(PLS ) regression method has been proposed by taking the advantages of both of them,i.e.qualitive and quantative relationships between variables respectively.Accordingly,a fuzzy-reasoning neural network(FNN) prediction model for basic oxygen furnace(BOF) end-point carbon content based on this technique has been developed.The prediction results showed that this model can effectively improve the hit rate of end-point carbon content and increase network training speed.The successful hit rate of the model can reach up to 94.12%with about 0.02% error range.展开更多
A 1∶8 physical water model was constructed to investigate the fluid flow and mixing phenomena in the basic oxygen furnace(BOF)converter.The particle image velocimetry was employed to measure the velocity distribution...A 1∶8 physical water model was constructed to investigate the fluid flow and mixing phenomena in the basic oxygen furnace(BOF)converter.The particle image velocimetry was employed to measure the velocity distribution of the bath and the high-speed camera was applied to capture the cavity shape in the combined blowing BOF converter.The mixing time for varied operating conditions was measured by the stimulus-response approach.The cavity depth increased with the decrease in the lance height and the increase in the top gas flow rate while the bottom blowing gas had little influence on the cavity depth.The minimum cavity depth was obtained under the condition of a 69.8 m^(3)/h top gas flow rate,a 287.5 mm lance height and a 0.93 m^(3)/h bottom blowing gas flow rate,which was 161.2 mm.The mixing time decreased as the lance height decreased and the top blowing gas flow rate increased.The mixing time was first decreased and then increased with the increase in the bottom gas flow rate.With the condition of 69.8 m^(3)/h gas flow rate of top blowing,the 287.5 mm lance height and the 0.93 m^(3)/h gas flow rate of bottom blowing,the mixing time in the converter was 48.65 s.The empirical formula between the stirring power and the mixing time in the converter was calculated.展开更多
基金supported by the New Century Excellent Talents Program of the Ministry of Education of China (No.NCET 07-0067)the National Natural Science Foundation of China (No.50874014)
文摘The process model for BOF process can be applied to predict the liquid steel composition and bath temperature during the whole steelmaking process. On the basis of the traditional three-stage decarburization theory, the concept of mixing degree was put forward, which was used to indicate the effect of oxygen jet on decarburization. Furthermore, a more practical process model for BOF steelmaking was developed by analyzing the effect of silicon, manganese, oxygen injection rate, oxygen lance height, and bath temperature on decarburization. Process verification and end-point verification for the process model have been carried out, and the verification results show that the predic- tion accuracy of carbon content reaches 82.6% （the range of carbon content at the end-point is less than 0. 1wt%） and 85.7% （the range of carbon content at end-point is 0. 1wt% -0.7wt%） when the absolute error is less than 0.02wt% and 0.05wt%, respectively.
文摘Artificial intelligence techniques have been used to predict basic oxygen furnace(BOF) end-points. However,the main challenge is to effectively reduce the input nodes as too many input nodes in neural network increase complexity,decrease accuracy and slow down the training speed of the network.Simply picking-up variables as input usually influence validity of model.It is quite necessary to develop an effective method to reduce the number of input nodes whereby to simplify the network and improve model performance.In this study,a variable-filtrating technique combining both metallurgical mechanism model and partial least-squares(PLS ) regression method has been proposed by taking the advantages of both of them,i.e.qualitive and quantative relationships between variables respectively.Accordingly,a fuzzy-reasoning neural network(FNN) prediction model for basic oxygen furnace(BOF) end-point carbon content based on this technique has been developed.The prediction results showed that this model can effectively improve the hit rate of end-point carbon content and increase network training speed.The successful hit rate of the model can reach up to 94.12%with about 0.02% error range.
基金support from the National Natural Science Foundation of China(U22A20171)the High Steel Center(HSC)at North China University of Technology and University of Science and Technology Beijing,China.
文摘A 1∶8 physical water model was constructed to investigate the fluid flow and mixing phenomena in the basic oxygen furnace(BOF)converter.The particle image velocimetry was employed to measure the velocity distribution of the bath and the high-speed camera was applied to capture the cavity shape in the combined blowing BOF converter.The mixing time for varied operating conditions was measured by the stimulus-response approach.The cavity depth increased with the decrease in the lance height and the increase in the top gas flow rate while the bottom blowing gas had little influence on the cavity depth.The minimum cavity depth was obtained under the condition of a 69.8 m^(3)/h top gas flow rate,a 287.5 mm lance height and a 0.93 m^(3)/h bottom blowing gas flow rate,which was 161.2 mm.The mixing time decreased as the lance height decreased and the top blowing gas flow rate increased.The mixing time was first decreased and then increased with the increase in the bottom gas flow rate.With the condition of 69.8 m^(3)/h gas flow rate of top blowing,the 287.5 mm lance height and the 0.93 m^(3)/h gas flow rate of bottom blowing,the mixing time in the converter was 48.65 s.The empirical formula between the stirring power and the mixing time in the converter was calculated.
