针对高炉初渣、中间渣组分多变特别是FeO含量高等问题,系统研究了CaO-SiO2-Al2O3-FeO-MgO五元渣系的黏度及组分对黏度的影响规律,并建立了基于WEB的神经网络-遗传算法（ANN-GA）系统的高炉渣黏度预报模型。结果表明,该模型对高FeO渣系的黏度预报值与试验结果吻合较好,误差在20%以内。通过模型预报获得的各因素对渣黏度影响的规律与文献及试验结果一致。
The primary slag and the intermediate slag of the blast furnace had complex compositions and were especially rich in FeO.Aiming at these problems,the viscosity of the CaO-SiO2-Al2O3-FeO-MgO melt was investigated as well as the influencing laws of each component to viscosity.Then a viscosity prediction model was established on the basis of WEB-based neural network-genetic algorithm（ANN-GA） system.Verification result of this model indicates that the error of this model is basically within 20% which is acceptable.The univariate analyses attained by the ANN-GA system are well consistent with literature results.
参考文献
| [1] | 王筱留.钢铁冶金学:炼铁部分[M].北京:冶金工业出版社,2007 |
| [2] | 沈峰满.高Al2O3含量渣系高炉冶炼工艺探讨[J].鞍钢技术,2005(06):1-4. |
| [3] | 马丽,竺维春,王冬青,苏展.首钢高炉合理渣系的研究[J].鞍钢技术,2007(06):21-24. |
| [4] | Forsbacka Lasse;Holappa Lauri;Iida Takamichi et al.Ex- perimental Study of Viscosities of Selected CaO-MgO-AlzOz- SiOz Slags and Application of the Iida Model[J].Scandinavi- an Journal of Metallurgy,2003,32:273. |
| [5] | 李金锡,张鉴.CaO-MgO-MnO-FeO-CaF2-Al2O3-SiO2渣系粘度的计算模型[J].北京科技大学学报,2000(05):438-441. |
| [6] | 梁子福 .高炉渣黏度实验研究及预报模型[D].北京:北京科技大学,2007. |
| [7] | 刘国华 .基于WEB的神经网络一遗传算法系统及其在骨组织工程支架材料研制中的应用[D].北d京:北京科技大学,2002. |
| [8] | LEE Young Seok;MIN Dong Joon;JUNG Sung Mo et al.Influence of Basicity and FeO Content on Viscosity o Blast Furnace Type Slags Containing FeO[J].ISIJ International,2004,44(08):1283. |
| [9] | Verein Deutscher Eisenhuttenleute(VDEh).Slag Atlas[M].Germany Verlag-Stahleisen GrnbH,1995 |
| [10] | 何环宇,王庆祥,曾小宁.MgO含量对高炉炉渣粘度的影响[J].钢铁研究学报,2006(06):11-13,21. |
| [11] | 孟宪民 .首钢高炉合理渣系及其冶金性能的研究[D].北京:北京科技大学,2005. |
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