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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; R0 k8 [, _& q1 {) F
汪金良1 ,卢宏2 ,汪仁良3 ,曾青云1" d9 p, J8 z5 ?: ?! ?, l" l
(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 ;" L- n/ e( \( b0 [) }
31 贵溪冶炼厂,江西贵溪335424). g: l/ X3 J7 Z
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 t L. c& _$ [' o5 F
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;; ~7 E# C; t. ` h7 m1 B5 ^
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
j, B* ~# r- a3 S& `! \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
( ?% x! Y1 ]4 b) O: `/ u控制在110 ±012 ,自热熔炼应控制在1134 以上。9 j9 ?3 T# u5 h- ` b a
关键词:神经网络;闪速熔炼;铜;因素( Y! e, D3 b+ t4 N4 s' X9 y
中图分类号: TF811 文献标识码:A 文章编号:1007 - 7545 (2008) 02 - 0002 - 04
9 l/ l" }" h9 Q6 L1 s# P0 EAnalysis of the Effect Factors of Copper Flash Smelting# `: K+ ~: ?, O/ w
Based on Neural Network
7 y6 a% H7 z; h! ]+ nWAN GJ in2liang1 , LU Hong2 , WAN G Ren2liang3 , ZEN G Qing2yun1
: t3 y. C( v* k% o% k(11 Faculty of Material and Chemist ry Engineering , Jiangxi University of Science and Technology ,3 F/ [; _7 Z6 e4 [: P- k& y5 a7 P6 f
Ganzhou 341000 , China ; 21 Faculty of Information Engineering , Jiangxi University of Science and Technology ,
. V- C$ d) g0 i. @6 i9 hGanzhou 341000 , China ; 31 Guixi Smelter , Guixi 335424 , China)4 b. W# U% \! G" z/ }1 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
[# K+ b3 M: Hement s content in copper concent rate on the copper flash smelting process are st udied based on the built) ?) _+ s" _; T: s6 q$ {" q: V, ^
neural network model1 Result s show t hat the mat te grade reduces , t he matte temperat ure increases but t he! ]4 O' J3 H% y: |/ m5 r5 R
Fe/ SiO2 in slag changes lit tle when t he oxygen grade increases ; the mat te grade , the mat te temperat ure
$ }- A* I! { A# I Kand t he Fe/ SiO2 in slag increase all when t he oxygen volume per ton concent rate increases ; t he Fe/ SiO2 in" J5 n2 a( `8 q& m2 K" b3 ~
slag drop s clearly when t he flux ratio increases ; t he increment of t he Cu content in concent rate makes t he; o1 m. K5 y5 s! ~5 ]
mat te grade increase but t he mat te temperature drop lit tle ; t he effect of the Fe content in concent rate is op2
/ J4 [ X, k; R: @posite to t he Cu content ; S/ Cu should be cont rolled f rom 018 to 112 generally , but more than 1134 for
3 q4 H! R# k/ [! iself2heat smelting1
3 ^4 I9 B" k8 Q. TKeywords :Neural network ; Flash Smelting ; Copper ; Factors |
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发表于 2009-7-20 17:43:03
