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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
: p/ L* t% Y6 n; \3 v$ W汪金良1 ,卢宏2 ,汪仁良3 ,曾青云1
, X& L5 R4 N4 O# ]. x- x$ u8 G* |' `(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 ;8 {7 y: N) z$ s
31 贵溪冶炼厂,江西贵溪335424)& \$ ~3 {+ \* v1 _( a, 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
8 G( x: k+ ]: F* \" l, B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;5 p# d# Q+ o4 e) t
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
% C( s1 B! Y# q+ 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) U; M0 u9 X) o% B
控制在110 ±012 ,自热熔炼应控制在1134 以上。
V4 U( L, q4 A. H: }0 A关键词:神经网络;闪速熔炼;铜;因素
: m! Q; V; G0 V" ?9 D中图分类号: TF811 文献标识码:A 文章编号:1007 - 7545 (2008) 02 - 0002 - 04; M6 Y, M# w$ u. t
Analysis of the Effect Factors of Copper Flash Smelting
& f' t; n" Y/ ?% r4 D4 [6 G& l# rBased on Neural Network) q, G% \1 n% W6 L
WAN GJ in2liang1 , LU Hong2 , WAN G Ren2liang3 , ZEN G Qing2yun1% u! H, Q9 y7 i$ d
(11 Faculty of Material and Chemist ry Engineering , Jiangxi University of Science and Technology ,
7 _- k* K5 c7 z& d2 l9 aGanzhou 341000 , China ; 21 Faculty of Information Engineering , Jiangxi University of Science and Technology ,
1 D9 t, M8 }" ]8 V$ `Ganzhou 341000 , China ; 31 Guixi Smelter , Guixi 335424 , China)+ p6 ^/ w y! r) m2 \3 I0 o5 H
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- n$ G! N3 T; e O
ement s content in copper concent rate on the copper flash smelting process are st udied based on the built
; {$ e! n' W& c; ], F' Tneural network model1 Result s show t hat the mat te grade reduces , t he matte temperat ure increases but t he
9 x Z! G2 E( e/ W( N5 G. lFe/ SiO2 in slag changes lit tle when t he oxygen grade increases ; the mat te grade , the mat te temperat ure# n8 X+ [8 u8 J8 W! H' o
and t he Fe/ SiO2 in slag increase all when t he oxygen volume per ton concent rate increases ; t he Fe/ SiO2 in% X6 s j" S# {9 w7 _
slag drop s clearly when t he flux ratio increases ; t he increment of t he Cu content in concent rate makes t he
/ {+ _4 o/ g3 Omat te grade increase but t he mat te temperature drop lit tle ; t he effect of the Fe content in concent rate is op2
$ C: T( m5 {3 Bposite to t he Cu content ; S/ Cu should be cont rolled f rom 018 to 112 generally , but more than 1134 for: T4 T: x4 s/ T
self2heat smelting11 q8 f, O" C$ @& P0 [! ~' g$ C
Keywords :Neural network ; Flash Smelting ; Copper ; Factors |
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发表于 2009-7-20 17:43:03
