The amount of oxygen blown into the converter is one of the key parameters for the control of the converter blowing process,which directly affects the tap-to-tap time of converter. In this study, a hybrid model based ...The amount of oxygen blown into the converter is one of the key parameters for the control of the converter blowing process,which directly affects the tap-to-tap time of converter. In this study, a hybrid model based on oxygen balance mechanism (OBM) and deep neural network (DNN) was established for predicting oxygen blowing time in converter. A three-step method was utilized in the hybrid model. First, the oxygen consumption volume was predicted by the OBM model and DNN model, respectively. Second, a more accurate oxygen consumption volume was obtained by integrating the OBM model and DNN model. Finally, the converter oxygen blowing time was calculated according to the oxygen consumption volume and the oxygen supply intensity of each heat. The proposed hybrid model was verified using the actual data collected from an integrated steel plant in China, and compared with multiple linear regression model, OBM model, and neural network model including extreme learning machine, back propagation neural network, and DNN. The test results indicate that the hybrid model with a network structure of 3 hidden layer layers, 32-16-8 neurons per hidden layer, and 0.1 learning rate has the best prediction accuracy and stronger generalization ability compared with other models. The predicted hit ratio of oxygen consumption volume within the error±300 m^(3)is 96.67%;determination coefficient (R^(2)) and root mean square error (RMSE) are0.6984 and 150.03 m^(3), respectively. The oxygen blow time prediction hit ratio within the error±0.6 min is 89.50%;R2and RMSE are0.9486 and 0.3592 min, respectively. As a result, the proposed model can effectively predict the oxygen consumption volume and oxygen blowing time in the converter.展开更多
This article discusses and analyzes the law of nitrogen increase in liquid steel and the main factors affect-ing the nitrogen increasing of molten steel,through the way of adding nitrogen to molten steel by bottom blo...This article discusses and analyzes the law of nitrogen increase in liquid steel and the main factors affect-ing the nitrogen increasing of molten steel,through the way of adding nitrogen to molten steel by bottom blowing nitrogen gas in LF refining process.It is considered that the main factors affecting the nitrogen increasing instability of molten steel are the initial temperature of LF refining,nitrogen relative element,surface active elements[O]and[S]of steel liquid,and bottom blowing rate of ladle.The large-scale production practice shows that T[O]not more than 50×10-6 and[S]is not more than 0.020 in LF refining at the initial temperature of not less than 1570.The liquid steel nitrogen enrichment test is carried out by ladle bottom blowing nitrogen gas after 20 min of refining,the flow rate is set as(6.0~7.0)NL/min per ton,and it is turned to 2 NL/min at 6 min before the end of refining,the nitrogen increasing rate of liquid steel is basically stable at(5~6)×10-6 per minute.展开更多
本文讨论非线性Klein-Gordon 方程的混合问题{u(■)—△u+u=F(u,Du,D_xDu) (t,x)∈(0,T)×Ωu(0,x)=h(x) u_t(0,x)=g(x),x∈Ω■u/■v=0■在F(u,Du,D_xDu)≥p sum from i=1 to n u_(X_i)~2+qu_t^2+u 这里(p>0,q>0) 及■_■■^...本文讨论非线性Klein-Gordon 方程的混合问题{u(■)—△u+u=F(u,Du,D_xDu) (t,x)∈(0,T)×Ωu(0,x)=h(x) u_t(0,x)=g(x),x∈Ω■u/■v=0■在F(u,Du,D_xDu)≥p sum from i=1 to n u_(X_i)~2+qu_t^2+u 这里(p>0,q>0) 及■_■■^(ph)(x)×g(x)dx>0时,得到该问题的解在有限时间内爆破.展开更多
基金financially supported by the National Natural Science Foundation of China (Nos.51974023 and52374321)the funding of State Key Laboratory of Advanced Metallurgy,University of Science and Technology Beijing,China (No.41620007)。
文摘The amount of oxygen blown into the converter is one of the key parameters for the control of the converter blowing process,which directly affects the tap-to-tap time of converter. In this study, a hybrid model based on oxygen balance mechanism (OBM) and deep neural network (DNN) was established for predicting oxygen blowing time in converter. A three-step method was utilized in the hybrid model. First, the oxygen consumption volume was predicted by the OBM model and DNN model, respectively. Second, a more accurate oxygen consumption volume was obtained by integrating the OBM model and DNN model. Finally, the converter oxygen blowing time was calculated according to the oxygen consumption volume and the oxygen supply intensity of each heat. The proposed hybrid model was verified using the actual data collected from an integrated steel plant in China, and compared with multiple linear regression model, OBM model, and neural network model including extreme learning machine, back propagation neural network, and DNN. The test results indicate that the hybrid model with a network structure of 3 hidden layer layers, 32-16-8 neurons per hidden layer, and 0.1 learning rate has the best prediction accuracy and stronger generalization ability compared with other models. The predicted hit ratio of oxygen consumption volume within the error±300 m^(3)is 96.67%;determination coefficient (R^(2)) and root mean square error (RMSE) are0.6984 and 150.03 m^(3), respectively. The oxygen blow time prediction hit ratio within the error±0.6 min is 89.50%;R2and RMSE are0.9486 and 0.3592 min, respectively. As a result, the proposed model can effectively predict the oxygen consumption volume and oxygen blowing time in the converter.
文摘This article discusses and analyzes the law of nitrogen increase in liquid steel and the main factors affect-ing the nitrogen increasing of molten steel,through the way of adding nitrogen to molten steel by bottom blowing nitrogen gas in LF refining process.It is considered that the main factors affecting the nitrogen increasing instability of molten steel are the initial temperature of LF refining,nitrogen relative element,surface active elements[O]and[S]of steel liquid,and bottom blowing rate of ladle.The large-scale production practice shows that T[O]not more than 50×10-6 and[S]is not more than 0.020 in LF refining at the initial temperature of not less than 1570.The liquid steel nitrogen enrichment test is carried out by ladle bottom blowing nitrogen gas after 20 min of refining,the flow rate is set as(6.0~7.0)NL/min per ton,and it is turned to 2 NL/min at 6 min before the end of refining,the nitrogen increasing rate of liquid steel is basically stable at(5~6)×10-6 per minute.
文摘本文讨论非线性Klein-Gordon 方程的混合问题{u(■)—△u+u=F(u,Du,D_xDu) (t,x)∈(0,T)×Ωu(0,x)=h(x) u_t(0,x)=g(x),x∈Ω■u/■v=0■在F(u,Du,D_xDu)≥p sum from i=1 to n u_(X_i)~2+qu_t^2+u 这里(p>0,q>0) 及■_■■^(ph)(x)×g(x)dx>0时,得到该问题的解在有限时间内爆破.
