ZHANG Jun
,
GUO Xing-min
,
HUANG Xue-jun
钢铁研究学报(英文版)
The relationship of time to minerals composition in sinters is investigated by mineragraphy are claritied observation and component analysis, and the effects of temperature and atmosphere on mineralization process. Results are obtained as follows. The initial melt forms below the eutectic temperature of CaO·Fe2O3 and CaO·2Fe2O3, which is complex substance containing Ca, Fe, Si and Al, rather than the binary calcium ferrite melt. Minerals composition of binding phase is related to local content of silica in melt, which is influenced by temperature. Appearance of the melt promotes the transition from hematite to magnetite, which then alters the mechanism of calcium ferrite formation. Before the formation of magnetite, the contents of Fe and Ca within the multiple calcium ferrite decrease with temperature, but in the case of magnetite presence, the content of Fe increases solely with increase of temperature and decrease of oxygen potential. Temperature and atmosphere determine minerals composition together, and bring influence on sintering process in different ways. It can be deduced that temperature affects kinetics of the mineralization process, but atmosphere just plays a role in thermodynamics.
关键词:
temperature
,
atmosphere
,
iron ore sintering
,
mineralization process
任彦军
,
，王家伟，张晓兵，赵浩文
钢铁
通过研究高炉-转炉界面铁水运输过程温度的主要影响因素，确定了影响高炉-转炉界面铁水运输过程温度的参数，建立了基于Levenberg-Marquardt(LM)算法BP神经网络的高炉-转炉界面铁水温度及铁水过程温降的预报模型。用沙钢100包铁水数据进行模型训练，50包铁水数据进行现场预报，结果表明：在高炉-转炉界面“一包到底”模式下，当绝对误差│X│≤20℃时，铁水温度命中率为94%，铁水温降命中率为78%；当绝对误差│X│≤40℃时，铁水温度命中率为100%，铁水温降命中率为92%，该预报模型能够满足现场实际生产需求，对炼钢生产有很好的指导意义。
关键词:
温度
,
BP neural network
,
LM algorithm
,
predictive model
