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
F.T.Cheng
材料科学技术（英文）
The present communication addresses an interesting problem related to the indeterminacy in hardness of superelastic NiTi reported by Xu et al. The origin of the indeterminacy is attributed to the inadequacy of the conventional Vickers hardness testing measurement which does not record elastic deformation, and thus the indeterminacy may be removed with suitable techniques. Concepts of hardness in relation to deformation are clarified. Recommendations for measuring the hardness of NiTi and other elastic-plastic materials are suggested, together with comments on the advantages and disadvantages of each of these methods.
关键词:
Hardness
,
null
,
null
,
null
,
null
,
null
Jingpei XIE
,
Yaomin ZHU
,
Xiaoping WANG
材料科学技术（英文）
The M-s temperature, Delta G(gamma-->alpha), Delta G(gamma-->M) and mechanical energy under a non-severe impact loading in a medium manganese steel (Fe-7Mn-1.2C) have been calculated by means of Xu's Fe-X-C model. The relation between the yield strength of austenite and the driving force for martensite transformation has been established. It is proved that the martensite transformation can take place in a medium manganese steel (Fe-7Mn-1.2C alloy) under a non-severe impact loading.
关键词:
李莉
,
李庆芬
,
郑磊
,
徐庭栋
,
杜善义
钢铁研究学报
以工业用12Cr1MoV钢为研究对象,通过俄歇电子能谱分析方法(AES),对磷在恒温过程中的非平衡晶界偏聚浓度进行了测定.获得的磷在钢中的非平衡晶界偏聚动力学曲线直接验证了非平衡晶界偏聚动力学理论中的Xu-Song模型.
关键词:
晶界偏聚
,
临界时间
,
扩散
,
钢
陆樟献
,
龚雁
,
王渊明
,
王正才
,
陈善飞
功能材料与器件学报
doi:10.3969/j.issn.1007-4252.2012.01.005
基于Xu等人的经典振荡磁偶极子模型和Matsumoto等人给出的双折射驰豫理论,研究了磁致双折射和二向色性对磁性液体薄片的光透射率弛豫特性的影响.理论推导了磁性液体的双折射和二向色性函数的偏振光透射率具体表达式,并进行了数值模拟计算.该问题的研究对磁性液体光学各向异性的深入认识以及相关磁性液体光学器件的应用具有一定的指导意义.
关键词:
物理光学
,
双折射驰豫
,
二向色性驰豫
,
磁性液体
,
磁偶极子
