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Article(id=1241081027662246857, tenantId=1146029695717560320, journalId=1235980550691926019, issueId=1241081025531540408, articleNumber=null, orderNo=null, doi=10.3969/j.issn.0253-6099.2024.04.033, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1708185600000, receivedDateStr=2024-02-18, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773826375165, onlineDateStr=2026-03-18, pubDate=1722441600000, pubDateStr=2024-08-01, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773826375165, onlineIssueDateStr=2026-03-18, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773826375165, creator=13701087609, updateTime=1773826375165, updator=13701087609, issue=Issue{id=1241081025531540408, tenantId=1146029695717560320, journalId=1235980550691926019, year='2024', volume='44', issue='4', pageStart='1', pageEnd='258', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773826374657, creator=13701087609, updateTime=1773827517159, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1241085817590960730, tenantId=1146029695717560320, journalId=1235980550691926019, issueId=1241081025531540408, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1241085817590960731, tenantId=1146029695717560320, journalId=1235980550691926019, issueId=1241081025531540408, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=175, endPage=180, ext={EN=ArticleExt(id=1241081027943265236, articleId=1241081027662246857, tenantId=1146029695717560320, journalId=1235980550691926019, language=EN, title=Optimization of Low-Grade Cassiterite Flotation by Response Surface Methodology, columnId=1236276106932842717, journalTitle=Mining and Metallurgical Engineering, columnName=MINERAL PROCESSING, runingTitle=null, highlight=null, articleAbstract=

A flotation experiment was conducted for a low-grade fine-grained cassiterite from Yunnan. The median dosage of reagent was determined by a single-factor test, and then the reagent dosage for rougher flotation process of cassiterite was optimized by response surface methodology as follows: 285 g/t of activator (KT-51), 13 g/t of inhibitor (OL-1C) and 873 g/t of compound collector (YK-Sn+SN-705). A verification test on flotation with the addition of those agents yielded a tin rougher concentrate grading 1.460% Sn at 74.44% recovery, which was close to the results predicted by response surface methodology. In order to further explore the effect, a closed-circuit test was conducted, resulting in the tin concentrate grading 5.45% Sn at 66.70% recovery. It is shown that efficient recovery of low-grade cassiterite resources can be actualized.

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对云南某低品位微细粒锡石进行了浮选试验研究。通过单因素实验确定药剂用量的中值水平，并采用响应曲面法对锡粗选药剂用量进行优化，得出最佳的药剂条件为：活化剂（KT-51）用量285 g/t、抑制剂（OL-1C）用量13 g/t、复合捕收剂（YK-Sn+SN-705）用量873 g/t，在该药剂条件下进行验证试验，锡粗精矿中Sn品位和回收率平均值分别为1.460%和74.44%，与响应曲面法预测结果接近。为进一步探索试验效果，进行了闭路试验，获得了Sn品位5.45%、Sn回收率66.70%的锡精矿，有效实现了低品位锡石资源的高效回收。

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欧乐明（1964—），男，湖南宁乡人，博士，教授，主要研究方向为矿物加工浮选药剂、工艺。E-mail：

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欧乐明（1964—），男，湖南宁乡人，博士，教授，主要研究方向为矿物加工浮选药剂、工艺。E-mail：

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欧乐明（1964—），男，湖南宁乡人，博士，教授，主要研究方向为矿物加工浮选药剂、工艺。E-mail：

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（a）KT-51与OL-1C的影响；（b）KT-51与锡捕收剂的影响；（c）锡捕收剂与OL-1C的影响

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（a）KT-51与OL-1C的影响；（b）KT-51与锡捕收剂的影响；（c）锡捕收剂与OL-1C的影响

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SiO2CaOFe2O3Al2O3MgOK2OSO3SnO2其他
27.3820.3715.6412.6010.603.050.330.179.86
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原矿化学成分分析结果（质量分数）

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SiO2CaOFe2O3Al2O3MgOK2OSO3SnO2其他
27.3820.3715.6412.6010.603.050.330.179.86
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水平因素
A：KT-51用量B：OC-1C用量C：锡复合捕收剂用量
-11005600
025015900
+1400251 200
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浮选药剂设计因素及水平

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水平因素
A：KT-51用量B：OC-1C用量C：锡复合捕收剂用量
-11005600
025015900
+1400251 200
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试验编号因素响应值
ABCSn品位/%Sn回收率/%
1400151 2001.02175.22
2100156001.47464.13
310059001.32471.49
4250159001.48173.78
5250256001.57565.53
6250159001.40274.60
7100151 2001.09773.54
825051 2001.28272.25
9100259001.32573.63
10400156001.47868.99
11250159001.47574.18
1240059001.36074.12
13250159001.45374.16
14400259001.25175.68
1525056001.61566.48
16250159001.45773.82
17250251 2001.26973.32
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浮选药剂条件优化Box-Behnken试验设计及响应结果

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试验编号因素响应值
ABCSn品位/%Sn回收率/%
1400151 2001.02175.22
2100156001.47464.13
310059001.32471.49
4250159001.48173.78
5250256001.57565.53
6250159001.40274.60
7100151 2001.09773.54
825051 2001.28272.25
9100259001.32573.63
10400156001.47868.99
11250159001.47574.18
1240059001.36074.12
13250159001.45374.16
14400259001.25175.68
1525056001.61566.48
16250159001.45773.82
17250251 2001.26973.32
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来源平方和自由度均方差F值P值
模型0.386 790.386 732.840 0<0.000 1
A0.001 510.001 51.160 00.318 0
B0.003 210.003 22.480 00.159 6
C0.271 210.271 2207.280 0<0.000 1
AB0.003 010.003 02.310 00.172 2
AC0.001 610.001 61.220 00.305 4
BC0.000 210.000 20.139 30.720 0
A20.098 810.098 875.500 0<0.000 1
B20.000 910.000 90.683 60.435 6
C20.004 610.004 63.490 00.104 0
残差0.009 270.001 3//
失拟0.005 330.001 81.810 00.038 0
纯误差0.003 940.001 0//
总离差0.395 916///
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Sn品位响应面试验结果方差分析结果

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来源平方和自由度均方差F值P值
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A0.001 510.001 51.160 00.318 0
B0.003 210.003 22.480 00.159 6
C0.271 210.271 2207.280 0<0.000 1
AB0.003 010.003 02.310 00.172 2
AC0.001 610.001 61.220 00.305 4
BC0.000 210.000 20.139 30.720 0
A20.098 810.098 875.500 0<0.000 1
B20.000 910.000 90.683 60.435 6
C20.004 610.004 63.490 00.104 0
残差0.009 270.001 3//
失拟0.005 330.001 81.810 00.038 0
纯误差0.003 940.001 0//
总离差0.395 916///
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来源平方和自由度均方差F值P值
模型198.400922.0651.10<0.000 1
A15.740115.7436.500.000 5
B1.82011.804.230.078 6
C106.5801106.58247.39<0.000 1
AB0.08410.080.190.671 9
AC2.53012.535.870.045 9
BC1.02011.022.370.167 8
A20.51010.511.190.312 0
B22.22012.225.150.057 4
C266.910166.91155.32<0.000 1
残差3.02070.43//
失拟2.58030.867.800.038 0
纯误差0.44040.11//
总离差201.44016///
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Sn回收率响应面试验结果方差分析结果

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来源平方和自由度均方差F值P值
模型198.400922.0651.10<0.000 1
A15.740115.7436.500.000 5
B1.82011.804.230.078 6
C106.5801106.58247.39<0.000 1
AB0.08410.080.190.671 9
AC2.53012.535.870.045 9
BC1.02011.022.370.167 8
A20.51010.511.190.312 0
B22.22012.225.150.057 4
C266.910166.91155.32<0.000 1
残差3.02070.43//
失拟2.58030.867.800.038 0
纯误差0.44040.11//
总离差201.44016///
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试验编号Sn品位/%Sn回收率/%
11.47273.46
21.42575.03
31.48274.82
平均1.46074.44
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验证试验结果

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试验编号Sn品位/%Sn回收率/%
11.47273.46
21.42575.03
31.48274.82
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产品名称产率/%Sn品位/%Sn回收率/%
锡精矿2.025.45066.70
尾矿97.980.05633.30
原矿100.000.165100.00
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闭路试验结果

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锡精矿2.025.45066.70
尾矿97.980.05633.30
原矿100.000.165100.00
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响应曲面法优化某低品位锡石浮选试验研究
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欧乐明 1 , 邓颖 1 , 符海桃 2 , 吴维明 1 , 戴建豪 1 , 杨浩 1
矿冶工程杂志 | 选矿 2024,44(4): 175-180
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矿冶工程杂志 | 选矿 2024, 44(4): 175-180
响应曲面法优化某低品位锡石浮选试验研究
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欧乐明1 , 邓颖1, 符海桃2, 吴维明1, 戴建豪1, 杨浩1
作者信息
  • 1.中南大学 资源加工与生物工程学院，湖南 长沙 410083
  • 2.云南华联锌铟股份有限公司，云南 文山 663701

    • 欧乐明（1964—），男，湖南宁乡人，博士，教授，主要研究方向为矿物加工浮选药剂、工艺。E-mail：

Optimization of Low-Grade Cassiterite Flotation by Response Surface Methodology
Leming OU1 , Ying DENG1, Haitao FU2, Weiming WU1, Jianhao DAI1, Hao YANG1
Affiliations
  • 1.School of Resource Processing and Bioengineering, Central South University, Changsha 410083, Hunan, China
  • 2.Yunnan Hualian Zinc and Indium Co., Ltd., Wenshan 663701, Yunnan, China
出版时间: 2024-08-01 doi: 10.3969/j.issn.0253-6099.2024.04.033
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摘要
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对云南某低品位微细粒锡石进行了浮选试验研究。通过单因素实验确定药剂用量的中值水平，并采用响应曲面法对锡粗选药剂用量进行优化，得出最佳的药剂条件为：活化剂（KT-51）用量285 g/t、抑制剂（OL-1C）用量13 g/t、复合捕收剂（YK-Sn+SN-705）用量873 g/t，在该药剂条件下进行验证试验，锡粗精矿中Sn品位和回收率平均值分别为1.460%和74.44%，与响应曲面法预测结果接近。为进一步探索试验效果，进行了闭路试验，获得了Sn品位5.45%、Sn回收率66.70%的锡精矿，有效实现了低品位锡石资源的高效回收。

响应曲面法  /  锡石  /  浮选  /  优化  /  捕收剂  /  活化剂  /  抑制剂

A flotation experiment was conducted for a low-grade fine-grained cassiterite from Yunnan. The median dosage of reagent was determined by a single-factor test, and then the reagent dosage for rougher flotation process of cassiterite was optimized by response surface methodology as follows: 285 g/t of activator (KT-51), 13 g/t of inhibitor (OL-1C) and 873 g/t of compound collector (YK-Sn+SN-705). A verification test on flotation with the addition of those agents yielded a tin rougher concentrate grading 1.460% Sn at 74.44% recovery, which was close to the results predicted by response surface methodology. In order to further explore the effect, a closed-circuit test was conducted, resulting in the tin concentrate grading 5.45% Sn at 66.70% recovery. It is shown that efficient recovery of low-grade cassiterite resources can be actualized.

response surface methodology  /  cassiterite  /  flotation  /  optimization  /  collector  /  activator  /  inhibitor
欧乐明, 邓颖, 符海桃, 吴维明, 戴建豪, 杨浩. 响应曲面法优化某低品位锡石浮选试验研究. 矿冶工程杂志, 2024 , 44 (4) : 175 -180 . DOI: 10.3969/j.issn.0253-6099.2024.04.033
Leming OU, Ying DENG, Haitao FU, Weiming WU, Jianhao DAI, Hao YANG. Optimization of Low-Grade Cassiterite Flotation by Response Surface Methodology[J]. Mining and Metallurgical Engineering, 2024 , 44 (4) : 175 -180 . DOI: 10.3969/j.issn.0253-6099.2024.04.033
正文
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锡是人类最早发现并广泛使用的金属之一，具有熔点低、耐腐蚀、抗疲劳等优点，被广泛应用于机械制造业、生物医药等领域。锡石是主要的含锡矿物，由于锡石密度大（6.8～7.1 g/cm3），大部分锡石通过重选方法回收[1-3]。我国的锡石主要以原生锡矿为主，原生锡矿共伴生组分多且复杂，矿物嵌布粒度细，同时锡石性脆且易碎，极易造成过粉碎，形成大量微细粒矿物。多采用浮选法回收微细粒锡石[4-6]。
锡石浮选是一个多因素交互影响的过程，仅通过单因素条件试验难以确定较佳的浮选药剂条件。响应曲面法是运用广泛的试验优化方法之一，它利用数学统计方法设计试验，利用试验因子与响应结果拟合函数方程，建立连续的曲面模型，并对试验因子及因子间的交互作用进行分析，通过模型优化试验条件，进而得出较佳的浮选药剂条件[7-12]。
云南某多金属选矿厂选锌尾矿中含大量微细粒锡石，选矿厂不断探究浮选回收微细粒锡石的可行性。本文以选矿厂锌硫浮选尾矿经分级脱泥、脱硫处理后的选锌尾矿为研究对象，通过单因素实验探究捕收剂、活化剂、抑制剂用量对锡石浮选的影响并确定较佳的水平范围，利用响应曲面法对浮选条件进行优化，参照优化条件进行闭路试验，考察最终的浮选效果，对选矿厂优化锡石浮选药剂用量条件、改善锡石浮选指标具有一定借鉴作用。
1 试验矿样及方法
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1.1 试验矿样
试验矿样为某选矿厂锌硫浮选尾矿经分级脱泥、脱硫处理后的选锌尾矿（以下称原矿），样品经烘干、混匀、缩分，分别采用XRD和XRF分析其矿物组成和化学成分，结果如图1和表1所示。由图1和表1可知，矿样中SnO2质量分数为0.17%，属于低品位锡石；脉石主成分为SiO2、CaO、Fe2O3等，脉石矿物为绿泥石、石英、方解石、白云石、金云母和角闪石等。
采用激光粒度分析仪分析了原矿粒度组成，结果如图2所示。由图2可知，该样品的特征粒度d95、d90、d50分别为75 μm、62 μm、27 μm，样品中67%矿物颗粒小于37 μm，属于微细粒锡石。
1.2 试验方法
低品位微细粒锡石回收难度大，选择合适的药剂、制定试验流程至关重要。试验所用锡石捕收剂为Yk-Sn和SN-705按一定比例混合的复合捕收剂，它对锡石浮选效果好；为使其充分溶解，配制时通常添加少量碳酸钠。OL-1C是一种多羟基糖类大分子高效脉石抑制剂，为中南大学自主研发的药剂。其他试验药剂包括辅助捕收剂P86、锡石高效活化剂KT-51以及起泡剂2#油等，均为选矿厂现场所用药剂。
试验设备包括XFD-1.5L、XFD-0.75L和XFD-0.5L浮选机、MP2002电子天平、DHG-9345A电热恒温鼓风干燥箱、真空过滤机等。
通过前期探索试验并结合现场实际，试验每次取样500 g，固定碳酸钠用量200 g/t、P86用量30 g/t、2#油用量30 g/t，流程如图3所示。先进行单因素实验，后续使用响应曲面法优化药剂用量，选取优化的浮选试验条件进行闭路试验，验证试验结果。
2 结果与讨论
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2.1 单因素实验
采用单因素实验探究活化剂、抑制剂及锡捕收剂对锡石浮选的影响效果。
不添加活化剂、抑制剂，按照图3所示流程和药剂制度进行了锡复合捕收剂用量试验，结果如图4所示。由图4可知，随着锡复合捕收剂用量增加，Sn品位下降，回收率不断升高，锡复合捕收剂用量从900 g/t增至1 500 g/t，Sn品位迅速降至0.77%，Sn回收率升至70%左右，可能是捕收剂用量过高导致药剂选择性下降，脉石矿物颗粒被浮出。综合考虑Sn品位和回收率，选取锡复合捕收剂用量900 g/t作为响应曲面法的中水平（0水平）。
不添加抑制剂，锡复合捕收剂用量900 g/t，按图3所示流程和药剂制度进行了活化剂KT-51用量试验，结果如图5所示。由图5可知，随着活化剂KT-51用量增加，Sn品位先升高后下降，回收率缓慢升高。KT-51用量超过400 g/t后，Sn回收率上升有限，而Sn品位明显下降。综合考虑，选取活化剂KT-51用量250 g/t作为响应曲面法的中水平。
不添加活化剂，锡复合捕收剂用量900 g/t，按照图3所示流程和药剂制度进行了抑制剂OL-1C用量试验，结果如图6所示。由图6可知，随着抑制剂OL-1C用量增加，Sn品位、回收率均先升高后下降，OL-1C用量超过20 g/t后，Sn回收率下降明显，Sn品位上升有限。综合考虑，选取抑制剂OL-1C用量15 g/t作为响应曲面法的中水平。
2.2 响应曲面法优化锡石浮选
通过Design-Expert 13.0软件对试验因素及响应值进行优化，并利用Box-Behnken试验设计方法将试验所得响应值进行拟合得到合适的数学模型，分析浮选药剂间的交互作用，并利用该模型优化锡石浮选药剂用量条件，进而改善锡石浮选指标。
2.2.1 响应曲面模型的建立
根据单因素实验结果，采用Box-Behnken试验设计方法，设计三因素三水平试验，三因素分别为A：KT-51用量（g/t）、B：OL-1C用量（g/t）和C：锡复合捕收剂用量（g/t），响应值为Sn品位和Sn回收率，响应曲面法优化试验的因子编码及水平如表2所示，Design-Expert 13.0软件拟定的试验条件及实际试验结果见表3。
2.2.2 回归方程方差分析
通过Design-Expert 13.0软件对试验数据进行统计分析，量化各参数的影响及交互作用，根据试验所选取的参数及水平，采用二阶多项式数学模型进行条件优化，并由此推出参数与响应之间的关系。
对Sn品位和Sn回收率的响应模型进行方差分析，结果见表4和表5。由表4～5可知，Sn品位和回收率的回归模型对应的F值分别为32.84和51.10，对应的P<0.000 01，表明模型具有极高的显著性。活化剂KT-51、抑制剂OL-1C对Sn回收率有显著影响，锡复合捕收剂用量对Sn品位和回收率均有显著影响。Sn品位模型的确定系数R2为0.976 9，调整确定系数为0.947 1，变异系数值为2.63%（小于10%），信噪比为21.506（大于4）；Sn回收率模型的确定系数为0.985 0，调整确定系数为0.965 8，变异系数值为0.91%（小于10%），信噪比为21.538（大于4）。表明样本数据均拟合效果好、可靠性高、变异概率低。
Sn品位和Sn回收率模型的因子编码方程式分别为：
2.2.3 响应曲面模型分析
图7为锡品位和回收率实际值与预测值的对比图，图中每个点均代表一个试验值。由图7可见，试验值在斜线附近的分布很集中，且各点均匀分布在两侧，说明模型拟合程度较好，准确性较高。
使用Design-expert 13.0软件模拟给出的二次方程模型，利用origin软件绘制了各参数之间的三维响应曲面和等高线图，如图8～9所示。响应曲面上的点高度越高，说明响应值越大；变化越快，坡度越陡峭，则影响效果越显著。等高线图可清晰反映各因素对响应值的影响，等高线的形状可反映因素交互效应的强弱，其形状越趋于椭圆形，说明因素的交互作用越强。
由图8（a）可知，活化剂KT-51用量增加，Sn品位先上升后下降，Sn品位峰值出现在KT-51用量中水平处；在锡石浮选中，KT-51通过吸附在锡石表面为锡石提供更多的活性位点促进捕收剂在锡石表面的吸附；抑制剂OL-1C用量增加，Sn品位略微下降，总体变化不大。由图8（b）可知，KT-51用量一定时，锡复合捕收剂用量增加，Sn品位下降，Sn品位峰值出现在锡复合捕收剂用量低水平处；可能是增大捕收剂用量导致其选择性下降，脉石矿物被浮出；锡复合捕收剂用量一定时，随着KT-51用量增加，Sn品位先升高后下降，Sn品位峰值出现在KT-51用量中水平处。由图8（c）可知，从响应面坡度来看，锡复合捕收剂用量对Sn品位的影响显著，OL-1C用量一定时，Sn品位随着锡复合捕收剂用量增加逐渐降低；锡复合捕收剂用量一定时，OL-1C用量对Sn品位的影响不大；Sn品位峰值出现在OL-1C和锡复合捕收剂用量均为低水平处。
由图9（a）可知，KT-51用量增加，Sn回收率升高；KT-51用量在低水平时，增加OL-1C用量，Sn回收率升高，OL-1C对脉石矿物产生有效抑制，有利于提升锡石浮选回收率；OL-1C用量处在中、高水平时，其用量变化对Sn回收率没有显著影响，此时KT-51用量对Sn回收率起主要影响作用。由图9（b）可知，从响应面坡度来看，锡复合捕收剂用量及KT-51用量对Sn回收率的影响显著，两者交互作用明显；KT-51用量一定时，锡复合捕收剂用量增加，Sn回收率先升高后下降；锡复合捕收剂用量一定时，随着KT-51用量增加，Sn回收率升高；Sn回收率峰值出现在两者用量均为高水平处。由图9（c）可知，从响应面坡度来看，锡复合捕收剂用量对Sn回收率的影响显著；OL-1C用量一定时，Sn回收率随着锡复合捕收剂用量增加先升高后降低；锡复合捕收剂用量一定时，Sn回收率随着OL-1C用量增加逐渐降低，且OL-1C与锡捕收剂交互作用明显；Sn回收率峰值出现在锡复合捕收剂用量中高水平、OL-1C用量高水平处。
由图8～9可知：KT-51用量与锡复合捕收剂用量对Sn品位、回收率交互作用显著，可能是药剂间的协同强化作用，KT-51活化剂作用在锡石表面，改变其表面电性和增加活性位点，强化锡复合捕收剂的作用效果；OL-1C与锡复合捕收剂对Sn回收率有较强的交互作用，可能是锡石表面受脉石矿物罩盖，而OL-1C能有效抑制脉石矿物，改善矿浆环境，增强捕收剂的吸附作用，优化锡石的浮选回收效果。
2.2.4 优化试验
采用Design-expert 13.0软件对锡石浮选药剂用量进行优化拟合，Sn品位和Sn回收率选取最大值进行预测，拟合得出活化剂KT-51用量为284.45 g/t，抑制剂OL-1C用量为12.547 g/t，锡复合捕收剂用量为872.6 g/t。在该药剂制度下，模拟得到的Sn品位和Sn回收率分别为1.467%和73.96%。在活化剂KT-51用量285 g/t、抑制剂OL-1C用量13 g/t、锡复合捕收剂用量873 g/t条件下进行了多组验证试验，结果如表6所示。验证试验获得了锡粗精矿Sn品位和Sn回收率平均值分别为1.460%和74.44%，与响应曲面优化预测结果基本一致，说明响应曲面法优化药剂最佳用量较为准确可靠。
2.3 闭路试验
在单因素实验及响应曲面法优化试验基础上进行了闭路试验，采用一次粗选、两次精选、两次扫选的浮选试验流程，其中精选1尾矿与扫选1精矿返回粗选，精选2尾矿顺序返回精选1，扫选2精矿顺序返回扫选1，试验流程如图10所示，试验结果见表7。闭路试验最终获得Sn品位5.45%、Sn回收率66.7%的锡精矿，该流程较好实现了低品位微细粒锡石资源的回收。
3 结论
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1）在化学元素分析及激光粒度分析基础上，确定某锡石浮选回收的工艺条件，通过单因素实验确定浮选药剂用量中值水平分别为：KT-51用量250 g/t，OL-1C用量15 g/t，锡复合捕收剂用量900 g/t。
2）在单因素实验基础上，采用Box-Behnken试验设计方法设计三因素三水平试验，依照设计方案进行试验，试验结果采用软件建立模型，所得模型P值均小于0.000 1，且软件预测结果与试验实际值接近，说明模型拟合程度较好、可信度高。
3）通过响应曲面法优化拟合得到的最佳浮选药剂条件为：活化剂KT-51用量285 g/t，抑制剂OL-1C用量13 g/t，锡复合捕收剂用量873 g/t，在该条件下进行浮选试验，得到Sn品位和回收率平均值分别为1.460%和74.44%。试验结果与响应曲面法优化结果基本一致。闭路试验最终获得Sn品位5.45%、Sn回收率66.7%的锡精矿。
4）利用响应曲面法对锡石浮选药剂条件进行优化，并探究药剂间交互作用对锡石浮选的影响，较好地实现了低品位微细粒锡石资源的回收，可为处理细粒锡石资源提供一定借鉴作用。
基金
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  • 国家自然科学基金(51674291)
文献
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参考文献 引证文献
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2024年第44卷第4期
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doi: 10.3969/j.issn.0253-6099.2024.04.033
  • 接收时间：2024-02-18
  • 首发时间：2026-03-18
  • 出版时间：2024-08-01
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  • 收稿日期：2024-02-18
基金
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    1.中南大学 资源加工与生物工程学院，湖南 长沙 410083
    2.云南华联锌铟股份有限公司，云南 文山 663701
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