JIANG Li-hong
,
WANG Ai-guo
,
TIAN Nai-yuan
,
ZHANG Wei-cun4
,
FAN Qiao-li4
钢铁研究学报(英文版)
The continuous casting technological parameters have a great influence on the secondary dendrite arm spacing of the slab, which determines the segregation behavior of materials. Therefore, the identification of technological parameters of continuous casting process directly impacts the property of slab. The relationships between continuous casting technological parameters and cooling rate of slab for spring steel were built using BP neural network model, based on which, the relevant secondary dendrite arm spacing was calculated. The simulation calculation was also carried out using the industrial data. The simulation results show that compared with that of the traditional method, the absolute error of calculation result obtained with BP neural network model reduced from 0.015 to 0.0005, and the relative error reduced from 6.76% to 0.22%. BP neural network model had a more precise accuracy in the optimization of continuous casting technological parameters.
关键词:
continuous casting
,
technological parameter
,
secondary dendrite arm spacing
,
BP neural network
XU An-jun
,
ZHANG Hui-ning
,
YANG Yi
,
CUI Jian
,
HE Dong-feng
,
TIAN Nai-yuan
钢铁研究学报(英文版)
Abstract： In order to study calcium leaching behavior for the steelmaking slag, factors that influence the leaching yield have been optimized. The results show that granularity of the slag, liquid to solid ratio (in short for L/S), temperature and reaction time have a significant effect on the leaching yield. The optimal conditions for leaching are determined as follows: 1) the granularity at 75 μm, L/S at 100, temperature at 60 ℃; 2) the granularity at 75 μm, L/S at 50, temperature at 40 ℃. Finally, the optimal leaching yield under these conditions is about 15%.
关键词:
Key words: steelmaking slag
,
leaching yield
,
calcium content
WANG Hong-bing
,
XU An-jun
,
AI Li-xiang
,
TIAN Nai-yuan
钢铁研究学报(英文版)
The hybrid method composed of clustering and predicting stages is proposed to predict the endpoint phosphorus content of molten steel in BOF (Basic Oxygen Furnace). At the clustering stage, the weighted K-means is performed to generate some clusters with homogeneous data. The weights of factors influencing the target are calculated using EWM (Entropy Weight Method). At the predicting stage, one GMDH (Group Method of Data Handling) polynomial neural network is built for each cluster. And the predictive results from all the GMDH polynomial neural networks are integrated into a whole to be the result for the hybrid method. The hybrid method, GMDH polynomial neural network and BP neural network are employed for a comparison. The results show that the proposed hybrid method is effective in predicting the endpoint phosphorus content of molten steel in BOF. Furthermore, the hybrid method outperforms BP neural network and GMDH polynomial neural network.
关键词:
basic oxygen furnace
,
endpoint phosphorus content
,
K-means
,
neural network
,
GMDH
ZHANG Hui-ning
,
XU An-jun
,
CUI Jian
,
HE Dong-feng
,
TIAN Nai-yuan
钢铁研究学报(英文版)
In order to improve the accuracy of model for terminative temperature in steelmaking, it is necessary to predict and control before decarburization. Thus, an optimization neural network model of terminative temperature in the process of dephosphorization by laying correlative degree weights to all input factors related was used. Then simulation experiment of model newly established is conducted utilizing 210 data from a domestic steel plant. The results show that hit rate arrives at 5645% when error is within plus or minus 5%, and the value is 100% when within ±10%. Comparing to the traditional neural network prediction model, the accuracy almost increases by 6839%.Thus, the simulation prediction fits the real perfectly, which accounts for that neural network model for terminative temperature based on grey theory can reflect accurately the practice in dephosphorization. Naturally, this method is effective and practicable.
关键词:
grey theory
,
correlation degree
,
dephosphorization
,
terminative temperature
,
neural network model
WU Peng-fei
,
XU An-jun
,
TIAN Nai-yuan
,
HE Dong-feng
钢铁研究学报(英文版)
Combined with the parameters of the production process of a steel factory, numerical simulations for a new ladle from preheating to turnover are conducted using the finite element analysis system software (ANSYS). The measured data proved that the simulated results are reliable. The effects of preheating time, thermal cycling times, and empty package time on steel temperature are calculated, an ideal preheating time is provided, besides, based on the analysis of a single factor and use the nonlinear analysis method, a steel temperature compensating model with diversified coupling factors is proposed, with the largest error of the present coupling model at 1462 ℃, and the errors between actual and target steel temperature in tundish after the model is applied to practical production are basically controlled within ±6 ℃, which can meet the accuracy of the manufacturer and has a practical guiding significance for the production in steelmaking workshops.
关键词:
ladle
,
thermal state
,
multiple-factor coupling
,
numerical simulation
,
compensation model
