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发表于 2009-7-21 15:24:58
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来自： 中国河南郑州
有色金属(冶炼部分) 2008 年2 期
' m5 I9 {; X: x6 Q) @汪金良1 ,卢宏2 ,汪仁良3 ,曾青云1
) ?9 C, U' l7 Q) ?(11 江西理工大学材料与化学工程学院,赣州341000 ;21 江西理工大学信息工程学院,赣州341000 ;
' K) N5 H0 K* e3 V31 贵溪冶炼厂,江西贵溪335424)
3 ^8 f4 y- c4 \: [9 O摘要:基于已建立的神经网络模型,研究了富化率、吨矿氧量、熔剂率以及铜精矿主要成分对铜闪速熔炼
0 }4 @2 I# ^3 m5 }过程的影响。结果表明:富化率的增大会使铜锍品位降低、铜锍温度升高,而对渣含Fe/ SiO2 影响不大;/ I4 {3 `( O0 X7 ]% A6 M: m& w" F `
吨矿氧量的增加会使铜锍品位、铜锍温度及渣含Fe/ SiO2 都升高;熔剂率的增加会使渣含Fe/ SiO2 明显; F$ j& r" w; \( a
下降;精矿中Cu 含量的增大会使铜锍品位升高,铜锍温度稍微降低;而Fe 的影响与Cu 相反;S/ Cu 一般& T3 G3 U4 O& Z) l# v: L& @+ [
控制在110 ±012 ,自热熔炼应控制在1134 以上。: F- P7 b, f" L2 ^1 E8 k% v
关键词:神经网络;闪速熔炼;铜;因素$ y+ O, t/ n, T
中图分类号: TF811 文献标识码:A 文章编号:1007 - 7545 (2008) 02 - 0002 - 04' S; s- P$ p" q6 }. ]) m
Analysis of the Effect Factors of Copper Flash Smelting( b* i/ }2 q. E' Y9 G1 ? D% o$ o
Based on Neural Network: F& E4 e% Y ]
WAN GJ in2liang1 , LU Hong2 , WAN G Ren2liang3 , ZEN G Qing2yun1
* C m5 Z( h% p" T(11 Faculty of Material and Chemist ry Engineering , Jiangxi University of Science and Technology ,
/ i) m! D- _8 a# BGanzhou 341000 , China ; 21 Faculty of Information Engineering , Jiangxi University of Science and Technology ,8 W3 Q' f. i6 H: m
Ganzhou 341000 , China ; 31 Guixi Smelter , Guixi 335424 , China)% Q* H+ D4 _: I5 B0 g4 c C
Abstract :The effect s of t he oxygen grade , t he oxygen volume per ton concent rate , t he flux rate and t he el2
/ K4 w5 f& y1 P* Z8 w9 O4 F- wement s content in copper concent rate on the copper flash smelting process are st udied based on the built
! ?1 g' E3 p; Y( z8 B- p3 f. I0 d7 Gneural network model1 Result s show t hat the mat te grade reduces , t he matte temperat ure increases but t he
# K0 j( y( _3 N' e L/ OFe/ SiO2 in slag changes lit tle when t he oxygen grade increases ; the mat te grade , the mat te temperat ure' F; M0 N- w) X# h' _
and t he Fe/ SiO2 in slag increase all when t he oxygen volume per ton concent rate increases ; t he Fe/ SiO2 in5 [: C% n* C ^
slag drop s clearly when t he flux ratio increases ; t he increment of t he Cu content in concent rate makes t he5 v! w5 V- \# Q! P1 h
mat te grade increase but t he mat te temperature drop lit tle ; t he effect of the Fe content in concent rate is op22 |$ t# _$ Z( j1 J
posite to t he Cu content ; S/ Cu should be cont rolled f rom 018 to 112 generally , but more than 1134 for* M) e1 p7 |; z, A$ j2 p, x
self2heat smelting1
9 t) x. p1 _2 K8 VKeywords :Neural network ; Flash Smelting ; Copper ; Factors |
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发表于 2009-7-20 17:43:03
