期刊文献+
共找到1篇文章
< 1 >
每页显示 20 50 100
Coupling effect and characterization modeling of iron ore fines mixing and granulating at 0-1 mm
1
作者 Dai-fei Liu Xian-ju Shi +2 位作者 Chao-jun Tang Hai-peng Cao Jun Li 《Journal of Iron and Steel Research International》 SCIE EI CAS CSCD 2019年第11期1154-1161,共8页
Characteristic of iron ore is the essential factor of granulating.Three ores,namely specularite,magnetite concentrate and limonite,were selected as adhesion powder to investigate granulating behavior and evolution pro... Characteristic of iron ore is the essential factor of granulating.Three ores,namely specularite,magnetite concentrate and limonite,were selected as adhesion powder to investigate granulating behavior and evolution process of agglomeration.Experiments and modeling were performed to represent granulating behavior on the basis of selectivity,ballability and adhesion rate.The mass fraction of water and particles size of adhesion and nucleation were set at(11±1)%,0-1 mm and 3-5 mm,respectively.Experimental results show that selectivity and ballability promote the evolution of granulation.The water absorption rate of specularite and the ballability of limonite are better.The coupling effects exist in two ores mixing and present positive effect when the proportion of magnetite concentrate is greater than that of specularite or specularite and limonite blend.During three ores mixing,the coupling effect presents a complex superposition state.A characterization model of adhesion rate of mixing granulation was established by random forest algorithms.Its output is adhesion rate,and its inputs include water absorption rate,balling index and mixing proportion.The model parameters are 957 trees and four branches,and the training and prediction errors of the model are 2.3%and 3.7%,respectively.Modeling indicates that the random forest model can be used to represent coupling effects of mixing granulation. 展开更多
关键词 Iron ore Granulating Coupling effect MODELING Random forest algorithm
原文传递
上一页 1 下一页 到第
使用帮助 返回顶部