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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
& P6 s# d. ]7 ~9 ]0 G9 W汪金良1 ,卢宏2 ,汪仁良3 ,曾青云1' W: X6 Z- S; ]: ]6 V' {' k" ?
(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 ;1 ?$ U* C' s& @+ s+ _+ S4 a
31 贵溪冶炼厂,江西贵溪335424); Z/ A1 E" ?' a i' d u) D
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
% {" c" C* {* e3 R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;
3 |9 j2 u( U. F7 `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
, d; X1 V* X6 D- R' B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- C- @! `( {) M( G
控制在110 ±012 ,自热熔炼应控制在1134 以上。) J% @0 d6 G+ |! l5 M
关键词:神经网络;闪速熔炼;铜;因素
7 ^0 _2 K6 i5 r3 E中图分类号: TF811 文献标识码:A 文章编号:1007 - 7545 (2008) 02 - 0002 - 04! e5 w- G5 D" r+ h' e, N3 H2 _6 w
Analysis of the Effect Factors of Copper Flash Smelting
g2 Q( r& t$ ], HBased on Neural Network
: o% [; i c n, o+ C* v4 U/ c% }# YWAN GJ in2liang1 , LU Hong2 , WAN G Ren2liang3 , ZEN G Qing2yun1
5 @$ p, L+ g5 |# G% D9 W! `$ x(11 Faculty of Material and Chemist ry Engineering , Jiangxi University of Science and Technology ,
4 _* l) L( a% W4 H2 oGanzhou 341000 , China ; 21 Faculty of Information Engineering , Jiangxi University of Science and Technology ,0 U* r; \2 W$ A
Ganzhou 341000 , China ; 31 Guixi Smelter , Guixi 335424 , China)/ s) ]3 i _2 H, K0 C4 d, `
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' Y% @% p* G; A/ X+ F1 ]
ement s content in copper concent rate on the copper flash smelting process are st udied based on the built9 f1 ?* x" W5 H- }$ |
neural network model1 Result s show t hat the mat te grade reduces , t he matte temperat ure increases but t he
1 {5 b: y3 Y% \/ }' m9 p( K: k/ kFe/ SiO2 in slag changes lit tle when t he oxygen grade increases ; the mat te grade , the mat te temperat ure
' v' ?/ W1 R; x: iand t he Fe/ SiO2 in slag increase all when t he oxygen volume per ton concent rate increases ; t he Fe/ SiO2 in; @( M; H2 V4 |6 @6 N u
slag drop s clearly when t he flux ratio increases ; t he increment of t he Cu content in concent rate makes t he
3 K% t: T w) k: s' o% Z# c; Cmat te grade increase but t he mat te temperature drop lit tle ; t he effect of the Fe content in concent rate is op2' Z K. D% u' \0 F) C
posite to t he Cu content ; S/ Cu should be cont rolled f rom 018 to 112 generally , but more than 1134 for( J( j6 p' k. f" `
self2heat smelting1/ Q$ n5 h' S" [# t
Keywords :Neural network ; Flash Smelting ; Copper ; Factors |
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发表于 2009-7-20 17:43:03
