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
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
