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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
: F u" o# b- K% M: j汪金良1 ,卢宏2 ,汪仁良3 ,曾青云1
' W; X/ h$ L2 @4 V; A4 o3 j9 V(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 ;( X H( K" u7 M9 @, v0 B' k& C
31 贵溪冶炼厂,江西贵溪335424) ~ H4 r% U4 w2 H% E, ?- j- u
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( r; F0 A: \4 Q
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;. t& V N c2 L1 s
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
$ K( X: m# Y( K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2 ], Y% O% t1 n' B. _
控制在110 ±012 ,自热熔炼应控制在1134 以上。
! T8 j6 _) R/ [; t( R' B: L关键词:神经网络;闪速熔炼;铜;因素
/ \6 x0 ^4 J$ {7 l, h, J3 }% ?. M; |& h中图分类号: TF811 文献标识码:A 文章编号:1007 - 7545 (2008) 02 - 0002 - 04
/ J$ F/ W5 F" f2 HAnalysis of the Effect Factors of Copper Flash Smelting) W4 p9 t$ _+ K0 P. k5 ^
Based on Neural Network
1 |& Y- y# p. v' f" b1 d2 \! OWAN GJ in2liang1 , LU Hong2 , WAN G Ren2liang3 , ZEN G Qing2yun1
% _, X1 ]1 G' K: s; W7 ]% J(11 Faculty of Material and Chemist ry Engineering , Jiangxi University of Science and Technology ,# i7 f- r. e. Q1 V) Q6 W3 z
Ganzhou 341000 , China ; 21 Faculty of Information Engineering , Jiangxi University of Science and Technology ,
" M' I4 p# ?6 ?3 d6 C" T/ y, H0 NGanzhou 341000 , China ; 31 Guixi Smelter , Guixi 335424 , China)
! X g6 G- n0 B, ?, EAbstract :The effect s of t he oxygen grade , t he oxygen volume per ton concent rate , t he flux rate and t he el2, Q- j# Y% f. D' A9 J# [3 z* C
ement s content in copper concent rate on the copper flash smelting process are st udied based on the built3 Y C5 L! F1 a6 x$ \& A O
neural network model1 Result s show t hat the mat te grade reduces , t he matte temperat ure increases but t he* Y; {+ V9 L7 f, C9 ?
Fe/ SiO2 in slag changes lit tle when t he oxygen grade increases ; the mat te grade , the mat te temperat ure5 P1 q. _$ t$ j* e* K! A
and t he Fe/ SiO2 in slag increase all when t he oxygen volume per ton concent rate increases ; t he Fe/ SiO2 in
# L$ d3 `0 s+ M0 u+ s# ^9 ~slag drop s clearly when t he flux ratio increases ; t he increment of t he Cu content in concent rate makes t he
" W" Q' T% z1 @/ b8 T, Xmat te grade increase but t he mat te temperature drop lit tle ; t he effect of the Fe content in concent rate is op2( \2 h( b1 K- V* Y- c; i
posite to t he Cu content ; S/ Cu should be cont rolled f rom 018 to 112 generally , but more than 1134 for. [; u) L: B; w3 x: T1 q) B }
self2heat smelting1
/ J- a# J, R ]& Z7 z: wKeywords :Neural network ; Flash Smelting ; Copper ; Factors |
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发表于 2009-7-20 17:43:03
