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Article(id=1241321699447197928, tenantId=1146029695717560320, journalId=1235980550691926019, issueId=1241321691524158287, articleNumber=null, orderNo=null, doi=10.3969/j.issn.0253-6099.2025.02.018, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1729353600000, receivedDateStr=2024-10-20, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773883755789, onlineDateStr=2026-03-19, pubDate=1743436800000, pubDateStr=2025-04-01, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773883755789, onlineIssueDateStr=2026-03-19, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773883755789, creator=13701087609, updateTime=1773883755789, updator=13701087609, issue=Issue{id=1241321691524158287, tenantId=1146029695717560320, journalId=1235980550691926019, year='2025', volume='45', issue='2', pageStart='1', pageEnd='204', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773883753901, creator=13701087609, updateTime=1773884632018, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1241325374676726363, tenantId=1146029695717560320, journalId=1235980550691926019, issueId=1241321691524158287, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1241325374676726364, tenantId=1146029695717560320, journalId=1235980550691926019, issueId=1241321691524158287, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=102, endPage=107, ext={EN=ArticleExt(id=1241321699724022019, articleId=1241321699447197928, tenantId=1146029695717560320, journalId=1235980550691926019, language=EN, title=Comparison of Flotation Schemes for Low-Grade Spodumene Ore with Complex Mineral Dissemination, columnId=1236276106932842717, journalTitle=Mining and Metallurgical Engineering, columnName=MINERAL PROCESSING, runingTitle=null, highlight=null, articleAbstract=

Based on the optimization of flotation parameters of a low-grade spodumene ore from Sichuan, processing tests were conducted by adopting four flotation schemes to investigate their advantages and disadvantages. It is found that both the process of flotation with full-size range of ores followed by middling regrinding and the process of desliming followed by flotation and sequential returning of middlings are more advantageous than the conventional flowsheet of flotation with full size-range of ores followed by just sequential returning of middlings. It is shown that Li2O recoveries by two processes can be increased correspondingly by 1.08 and 1.53 percentage points, and the second processing technique can bring a higher Li2O recovery at 86.7%. However, the first processing technique can produce a higher quality lithium concentrate with a Li2O grade of 5.76%.

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以四川某低品位锂辉石矿为研究对象，探讨了浮选优化条件，在此基础上进行了4种浮选工艺流程试验，并对比分析了各流程的优劣。结果表明：相对于常规全粒级浮选-中矿顺序返回工艺，全粒级浮选-中矿再磨工艺和脱泥-浮选-中矿顺序返回工艺更具优势，所得锂精矿Li2O回收率分别提高了1.08百分点和1.53百分点；其中脱泥-浮选-中矿顺序返回流程Li2O回收率更高，为86.7%；全粒级浮选-中矿再磨工艺所得锂精矿Li2O品位更高，为5.76%。

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康金星（1988—），男，湖南衡阳人，博士，高级工程师，主要从事低品位金属资源选冶加工技术研究。E-mail：

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康金星（1988—），男，湖南衡阳人，博士，高级工程师，主要从事低品位金属资源选冶加工技术研究。E-mail：

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康金星（1988—），男，湖南衡阳人，博士，高级工程师，主要从事低品位金属资源选冶加工技术研究。E-mail：

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articleId=1241321699447197928, language=EN, label=Fig.6, caption=Flowchart of scheme 4, figureFileSmall=XAP69nG0yd68ldg7u/WSvQ==, figureFileBig=9q75HlAFR1tchfoCa+WahQ==, tableContent=null), ArticleFig(id=1241327687852151339, tenantId=1146029695717560320, journalId=1235980550691926019, articleId=1241321699447197928, language=CN, label=图6, caption=方案4流程, figureFileSmall=XAP69nG0yd68ldg7u/WSvQ==, figureFileBig=9q75HlAFR1tchfoCa+WahQ==, tableContent=null), ArticleFig(id=1241327687931843120, tenantId=1146029695717560320, journalId=1235980550691926019, articleId=1241321699447197928, language=EN, label=Table 1, caption=

Main chemical composition analysis of sample

, figureFileSmall=null, figureFileBig=null, tableContent=
Li2OAl2O3SiO2CaOK2ONa2OCs2ORb2OMgO
1.2615.6372.550.422.733.660.007 30.0800.12
Ta2O5Nb2O5BeOTFeMnTiO2SnSP2O5
0.005 80.006 50.0501.030.100.0470.0150.0130.27
), ArticleFig(id=1241327688066060857, tenantId=1146029695717560320, journalId=1235980550691926019, articleId=1241321699447197928, language=CN, label=表1, caption=

试样主要化学成分分析结果（质量分数）

, figureFileSmall=null, figureFileBig=null, tableContent=
Li2OAl2O3SiO2CaOK2ONa2OCs2ORb2OMgO
1.2615.6372.550.422.733.660.007 30.0800.12
Ta2O5Nb2O5BeOTFeMnTiO2SnSP2O5
0.005 80.006 50.0501.030.100.0470.0150.0130.27
), ArticleFig(id=1241327688183501377, tenantId=1146029695717560320, journalId=1235980550691926019, articleId=1241321699447197928, language=EN, label=Table 2, caption=

Mineral composition and contents

, figureFileSmall=null, figureFileBig=null, tableContent=
锂辉石锂绿泥石磷锂铝石绿柱石褐铁矿石英
15.121.020.210.300.6030.20
钠长石钾长石白云母黑云母其他合计
27.3010.2010.402.602.05100.00
), ArticleFig(id=1241327688279970374, tenantId=1146029695717560320, journalId=1235980550691926019, articleId=1241321699447197928, language=CN, label=表2, caption=

矿物组成及含量（质量分数）

, figureFileSmall=null, figureFileBig=null, tableContent=
锂辉石锂绿泥石磷锂铝石绿柱石褐铁矿石英
15.121.020.210.300.6030.20
钠长石钾长石白云母黑云母其他合计
27.3010.2010.402.602.05100.00
), ArticleFig(id=1241327688409993804, tenantId=1146029695717560320, journalId=1235980550691926019, articleId=1241321699447197928, language=EN, label=Table 3, caption=

Distribution of Li2O in lithium containing minerals

, figureFileSmall=null, figureFileBig=null, tableContent=
赋存方式Li2O含量（质量分数）%Li2O分布率/%
锂辉石中Li2O1.20295.11
锂绿泥石中Li2O0.0312.46
磷锂铝石中Li2O0.0201.61
白云母中Li2O0.0100.82
), ArticleFig(id=1241327688544211539, tenantId=1146029695717560320, journalId=1235980550691926019, articleId=1241321699447197928, language=CN, label=表3, caption=

Li2O在含锂矿物中的分布情况

, figureFileSmall=null, figureFileBig=null, tableContent=
赋存方式Li2O含量（质量分数）%Li2O分布率/%
锂辉石中Li2O1.20295.11
锂绿泥石中Li2O0.0312.46
磷锂铝石中Li2O0.0201.61
白云母中Li2O0.0100.82
), ArticleFig(id=1241327688640680537, tenantId=1146029695717560320, journalId=1235980550691926019, articleId=1241321699447197928, language=EN, label=Table 4, caption=

Effects of collector types on lithium recovery

, figureFileSmall=null, figureFileBig=null, tableContent=
捕收剂种类捕收剂用量/（g·t-1）粗精矿Li2O品位/%Li2O回收率/%
7312 0003.6579.84
油酸4003.1785.40
731+SDS1 500+5003.3583.44
731+苯乙烯膦酸1 500+5004.0282.17
731+烷基羟肟酸1 500+5003.6082.78
731+苄基砷酸钠1 500+5004.2383.48
EF11172 0004.1785.33
), ArticleFig(id=1241327688745538142, tenantId=1146029695717560320, journalId=1235980550691926019, articleId=1241321699447197928, language=CN, label=表4, caption=

捕收剂种类对锂回收的影响

, figureFileSmall=null, figureFileBig=null, tableContent=
捕收剂种类捕收剂用量/（g·t-1）粗精矿Li2O品位/%Li2O回收率/%
7312 0003.6579.84
油酸4003.1785.40
731+SDS1 500+5003.3583.44
731+苯乙烯膦酸1 500+5004.0282.17
731+烷基羟肟酸1 500+5003.6082.78
731+苄基砷酸钠1 500+5004.2383.48
EF11172 0004.1785.33
), ArticleFig(id=1241327688871367268, tenantId=1146029695717560320, journalId=1235980550691926019, articleId=1241321699447197928, language=EN, label=Table 5, caption=

Effect of EF1117 dosage on lithium recovery

, figureFileSmall=null, figureFileBig=null, tableContent=
EF1117用量/（g·t-1）粗精矿Li2O品位/%粗精矿Li2O回收率/%3次精选锂精矿Li2O品位/%
1 2504.2480.246.03
1 5004.2183.255.99
1 7504.2186.265.97
2 0004.3086.995.91
2 5004.3388.165.60
), ArticleFig(id=1241327689047528039, tenantId=1146029695717560320, journalId=1235980550691926019, articleId=1241321699447197928, language=CN, label=表5, caption=

捕收剂EF1117用量对锂回收的影响

, figureFileSmall=null, figureFileBig=null, tableContent=
EF1117用量/（g·t-1）粗精矿Li2O品位/%粗精矿Li2O回收率/%3次精选锂精矿Li2O品位/%
1 2504.2480.246.03
1 5004.2183.255.99
1 7504.2186.265.97
2 0004.3086.995.91
2 5004.3388.165.60
), ArticleFig(id=1241327689148191340, tenantId=1146029695717560320, journalId=1235980550691926019, articleId=1241321699447197928, language=EN, label=Table 6, caption=

Closed-circuit test results

, figureFileSmall=null, figureFileBig=null, tableContent=
方案产品名称产率/%Li2O品位/%Li2O回收率/%
1锂精矿19.285.5685.22
尾矿80.720.2314.78
原矿100.001.26100.00
2锂精矿18.685.7686.30
尾矿81.320.2113.70
原矿100.001.25100.00
3细泥6.010.813.85
锂精矿19.815.5386.75
尾矿74.180.169.40
原矿100.001.26100.00
4细泥5.940.843.94
锂精矿20.015.3884.96
尾矿74.050.1911.10
原矿100.001.27100.00
), ArticleFig(id=1241327689240466033, tenantId=1146029695717560320, journalId=1235980550691926019, articleId=1241321699447197928, language=CN, label=表6, caption=

闭路试验结果

, figureFileSmall=null, figureFileBig=null, tableContent=
方案产品名称产率/%Li2O品位/%Li2O回收率/%
1锂精矿19.285.5685.22
尾矿80.720.2314.78
原矿100.001.26100.00
2锂精矿18.685.7686.30
尾矿81.320.2113.70
原矿100.001.25100.00
3细泥6.010.813.85
锂精矿19.815.5386.75
尾矿74.180.169.40
原矿100.001.26100.00
4细泥5.940.843.94
锂精矿20.015.3884.96
尾矿74.050.1911.10
原矿100.001.27100.00
), ArticleFig(id=1241327689328546421, tenantId=1146029695717560320, journalId=1235980550691926019, articleId=1241321699447197928, language=EN, label=Table 7, caption=

Comparison of advantages and disadvantages of flotation processes

, figureFileSmall=null, figureFileBig=null, tableContent=
方案流程内容优点缺点
1全粒级浮选-中矿顺序返回1）流程相对简单，便于现场管理与维护
2）投资费用相对较低		1）磨浮系统处理量大
2）药剂消耗量大
2全粒级浮选-中矿再磨1）优先浮出部分高品质易浮锂辉石，以相对慢浮含锂矿物的中矿再磨再选，对矿物有效解离及精细化调控浮选有利
2）快浮出部分粗精矿产品，中矿经再磨再选后尾矿再循环返回至粗选作业，可缓解粗选作业的处理压力		1）流程相对复杂，需中矿再磨，及多矿浆流分支分导系统
2）投资费用相对较高
3）浮选剂消耗量较大
3脱泥-浮选-中矿顺序返回1）流程相对简单，现场管理与维护方便
2）减少细泥影响，药剂用量相对较低，锂回收指标相对较好
3）投资费用相对较低		脱泥效果的好坏对浮选指标及系统稳定影响较大
4脱泥-浮选-中矿再磨1）药剂用量相对较低
2）可快速浮出部分粗精矿，缓解粗选作业压力		1）流程复杂，需脱泥并配备中矿再磨系统
2）脱泥后中矿再磨难控制，次生细泥易影响浮选效果
), ArticleFig(id=1241327689429209719, tenantId=1146029695717560320, journalId=1235980550691926019, articleId=1241321699447197928, language=CN, label=表7, caption=

各浮选流程方案优缺点对比

, figureFileSmall=null, figureFileBig=null, tableContent=
方案流程内容优点缺点
1全粒级浮选-中矿顺序返回1）流程相对简单，便于现场管理与维护
2）投资费用相对较低		1）磨浮系统处理量大
2）药剂消耗量大
2全粒级浮选-中矿再磨1）优先浮出部分高品质易浮锂辉石，以相对慢浮含锂矿物的中矿再磨再选，对矿物有效解离及精细化调控浮选有利
2）快浮出部分粗精矿产品，中矿经再磨再选后尾矿再循环返回至粗选作业，可缓解粗选作业的处理压力		1）流程相对复杂，需中矿再磨，及多矿浆流分支分导系统
2）投资费用相对较高
3）浮选剂消耗量较大
3脱泥-浮选-中矿顺序返回1）流程相对简单，现场管理与维护方便
2）减少细泥影响，药剂用量相对较低，锂回收指标相对较好
3）投资费用相对较低		脱泥效果的好坏对浮选指标及系统稳定影响较大
4脱泥-浮选-中矿再磨1）药剂用量相对较低
2）可快速浮出部分粗精矿，缓解粗选作业压力		1）流程复杂，需脱泥并配备中矿再磨系统
2）脱泥后中矿再磨难控制，次生细泥易影响浮选效果
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复杂嵌布低品位锂辉石矿浮选工艺的方案比较
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康金星 1, 2 , 王鑫 1, 2 , 姜志学 1, 2 , 刘志国 1, 2 , 王亚运 1, 2
矿冶工程杂志 | 选矿 2025,45(2): 102-107
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矿冶工程杂志 | 选矿 2025, 45(2): 102-107
复杂嵌布低品位锂辉石矿浮选工艺的方案比较
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康金星1, 2 , 王鑫1, 2, 姜志学1, 2, 刘志国1, 2, 王亚运1, 2
作者信息
  • 1.中国恩菲工程技术有限公司，北京　100038
  • 2.中国冶金科工集团有限公司，北京　100028

    • 康金星（1988—），男，湖南衡阳人，博士，高级工程师，主要从事低品位金属资源选冶加工技术研究。E-mail：

Comparison of Flotation Schemes for Low-Grade Spodumene Ore with Complex Mineral Dissemination
Jinxing KANG1, 2 , Xin WANG1, 2, Zhixue JIANG1, 2, Zhiguo LIU1, 2, Yayun WANG1, 2
Affiliations
  • 1.China ENFI Engineering Corporation, Beijing 100038, China
  • 2.China Metallurgical Group Corporation, Beijing 100028, China
出版时间: 2025-04-01 doi: 10.3969/j.issn.0253-6099.2025.02.018
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摘要
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以四川某低品位锂辉石矿为研究对象，探讨了浮选优化条件，在此基础上进行了4种浮选工艺流程试验，并对比分析了各流程的优劣。结果表明：相对于常规全粒级浮选-中矿顺序返回工艺，全粒级浮选-中矿再磨工艺和脱泥-浮选-中矿顺序返回工艺更具优势，所得锂精矿Li2O回收率分别提高了1.08百分点和1.53百分点；其中脱泥-浮选-中矿顺序返回流程Li2O回收率更高，为86.7%；全粒级浮选-中矿再磨工艺所得锂精矿Li2O品位更高，为5.76%。

锂辉石  /  矿物嵌布特征  /  浮选  /  脱泥浮选  /  锂精矿  /  浮选方案  /  方案优选  /  中矿再磨

Based on the optimization of flotation parameters of a low-grade spodumene ore from Sichuan, processing tests were conducted by adopting four flotation schemes to investigate their advantages and disadvantages. It is found that both the process of flotation with full-size range of ores followed by middling regrinding and the process of desliming followed by flotation and sequential returning of middlings are more advantageous than the conventional flowsheet of flotation with full size-range of ores followed by just sequential returning of middlings. It is shown that Li2O recoveries by two processes can be increased correspondingly by 1.08 and 1.53 percentage points, and the second processing technique can bring a higher Li2O recovery at 86.7%. However, the first processing technique can produce a higher quality lithium concentrate with a Li2O grade of 5.76%.

spodumene  /  mineral dissemination characteristics  /  flotation  /  desliming and flotation  /  lithium concentrate  /  flotation scheme  /  configuration optimization  /  middling regrinding
康金星, 王鑫, 姜志学, 刘志国, 王亚运. 复杂嵌布低品位锂辉石矿浮选工艺的方案比较. 矿冶工程杂志, 2025 , 45 (2) : 102 -107 . DOI: 10.3969/j.issn.0253-6099.2025.02.018
Jinxing KANG, Xin WANG, Zhixue JIANG, Zhiguo LIU, Yayun WANG. Comparison of Flotation Schemes for Low-Grade Spodumene Ore with Complex Mineral Dissemination[J]. Mining and Metallurgical Engineering, 2025 , 45 (2) : 102 -107 . DOI: 10.3969/j.issn.0253-6099.2025.02.018
正文
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锂作为高电化学活性的轻金属元素，已在新能源电池、原子能、航空航天等领域广泛应用[1]。近年来，锂成为主要经济体竞相争夺的战略金属，也是保障我国产业升级和战略转型的关键金属之一[2-4]。自然界中锂仅呈化合矿物或离子盐形式赋存。据统计，我国锂消耗量巨大，且需大量进口[5]。锂的安全供应是我国急需解决的卡脖子重点问题之一。
目前虽已发现150余种含锂矿物，但相对具有工业开发价值的仍局限于锂辉石、锂云母、透锂长石、磷锂铝石等[6]。我国锂矿物以锂辉石矿和锂云母矿为主，但原矿Li2O品位低[7]。国内锂云母矿经济开发难度大，因此锂辉石矿的高效开发利用尤为重要。尽管锂辉石选矿技术在不断更新，但浮选法仍是主导工艺[8-9]。现有锂辉石浮选技术研究多集中于浮选药剂的研发，而对不同流程方案对比分析研究相对较少[10-11]。
本文以四川某复杂嵌布低品位锂辉石矿为研究对象，基于矿石性质，通过浮选药剂与工艺的协同优化，比较不同浮选方案的优缺点，从而找到具有优势的工艺流程，为该类型锂辉石矿的矿物浮选加工提供参考。
1　矿石性质及试验方法
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1.1　矿石性质
试样取自四川某稀有金属矿床的伟晶岩型锂辉石矿，原矿化学多元素分析结果见表1，矿物组成及含量如表2所示，Li2O在含锂矿物中的分布情况见表3。从表1可知，试样中Li2O品位1.26%，伴生金属铍钽铌锡等含量不高，回收的目的金属主要是锂。由表2～3可知，锂主要以锂辉石形式存在；矿石中主要伴生金属矿物为绿柱石、褐铁矿等，脉石矿物主要为石英、长石类和云母类等。锂辉石矿物结构特征表明，锂辉石矿物大部分呈粗粒的板状、柱状，与钠长石、白云母和石英等脉石矿物紧密共生，有部分脉石矿物呈微细粒充填在锂辉石矿物颗粒内；另有少量锂辉石以微细粒不规则状嵌布于脉石矿物中。
1.2　试验方法
将原矿破碎至-2 mm，混匀后，采用球磨机磨矿，每次磨矿试样500 g，磨矿浓度（质量分数）66%；浮选在XFD系列浮选机中进行。浮选产品经烘干、称重、制样后分析Li2O品位，计算浮选回收率。试验用调整剂氢氧化钠、碳酸钠、硫化钙均为分析纯，其他药剂为工业级药剂；EF1117为中国恩菲工程技术有限公司自主研发的药剂，它由多种不同链长脂肪酸及其盐为主要组分、辅以少量油-水乳化剂复合构效配制而成。试验设备包括XMQ系列球磨机、XFD系列浮选机等。
2　浮选条件试验
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2.1　捕收剂种类及用量试验
锂辉石矿物的选择性浮选主要通过调整剂和捕收剂实现。采用碳酸钠+氢氧化钠+氯化钙组合调整剂，在磨矿细度-0.074 mm粒级占70%条件下，依序进行了各调整剂的单因素条件试验，经优化得到了碳酸钠用量2 500 g/t（加入磨机中）、氢氧化钠用量400 g/t、氯化钙用量100 g/t的药剂制度。而后，在此调整剂药剂制度条件下，考察了含羧基（氧化石蜡皂731、油酸）、磺酸基（十二烷基磺酸钠SDS）、砷酸基（苄基砷酸钠）、羟肟基（烷基羟肟酸）、膦酸基（苯乙烯膦酸）等活性基团的不同捕收剂对该锂矿石浮选的影响，试验流程见图1，捕收剂种类对锂回收的影响如表4所示。由表4可知，捕收剂EF1117对锂辉石矿物表现出良好的捕收性和选择性，经一次粗选即可获得Li2O品位4.17%、回收率85.33%的粗精矿。
相同条件下，考察了捕收剂EF1117用量对锂回收的影响，结果见表5。从表5可以看出，随着EF1117用量增加，粗精矿Li2O回收率增加，但3次精选后的锂精矿Li2O品位呈逐渐降低的趋势。确定适宜的捕收剂EF1117用量为2 000 g/t。
2.2　磨矿细度试验
矿物解离状态与磨矿细度相关，尤其是粗选作业。在调整剂碳酸钠用量2 500 g/t（加入磨机中）、氢氧化钠用量400 g/t、氯化钙用量100 g/t，捕收剂EF1117用量2 000 g/t，考察了粗选磨矿细度对锂回收的影响，结果见图2。由图2可知，粗选磨矿细度由-0.074 mm粒级占60%增加至占80%，锂回收率、锂品位均先增加后降低。这可能是磨矿细度不够时，相对粗粒的目的矿物吸附捕收剂的量不足，或因粗粒矿物表面吸附药剂的矿化程度不高，泡沫负载粗粒矿物能力不强，造成部分粗粒矿物未进入泡沫产品而损失；颗粒偏细时，矿石中含有较多的磨矿易泥化矿物，随着粗选磨矿细度增加，产出的细泥量增加，对锂矿物浮选产生负作用。适宜的粗选磨矿细度为-0.074 mm粒级占70%，此时对应的锂辉石矿物单体解离度为92.6%。
2.3　浮选综合试验
经单因素实验得到全粒级浮选的优化条件为：粗选磨矿细度-0.074 mm粒级占70%，调整剂碳酸钠用量2 500 g/t（加入磨机中）、氢氧化钠用量400 g/t、氯化钙用量100 g/t，捕收剂EF1117用量2 000 g/t。再经浮选浓度、浮选时间及精扫选等试验，论证了粗精矿采用3次精选（在精选1、精选2、精选3时分别添加500、500、300 g/t碳酸钠）、2次扫选的浮选流程。同时，采用上述浮选剂体系，对该试验矿石进行了脱泥-浮选条件优化试验，得到脱泥-浮选的优化条件为：粗选磨矿细度-0.074 mm粒级占70%，调整剂碳酸钠用量2 500 g/t（加入磨机中），脱泥后调整剂碳酸钠用量200 g/t、氢氧化钠用量200 g/t、氯化钙用量100 g/t，捕收剂EF1117用量1 500 g/t，精、扫选流程与全粒级浮选流程一致。
3　不同流程方案闭路对比试验
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在实验室条件下，进行了4种不同流程方案的闭路浮选对比试验，对应的试验流程分别如图3～6所示，试验结果见表6。从表6可知，采用方案1～3都能得到Li2O品位高于5.5%、Li2O回收率大于85%的锂精矿，方案4分选指标相对较差。与方案1（常规工艺流程）相比，方案2和方案3获得的锂精矿Li2O回收率分别提高了1.08和1.53百分点；方案3的Li2O回收率最高，为86.75%，但对应的精矿Li2O品位比方案2相对低0.23百分点，方案2获得的锂精矿Li2O品位达到了5.76%。方案4可能受限于实验室试验条件，脱泥后所得浮选中矿再磨产生次生细泥而影响了锂的分选指标。
各工艺流程方案各有优、缺点，不同流程方案的优缺点对比见表7。对比分析可见，方案1的优势在于：流程结构简单，便于现场管理与维护，设备投资费用相对较低；但该方案存在对矿物细泥的走向控制不佳、易造成磨浮粗选回路处理量大、且浮选捕收剂消耗大等缺陷。方案2的优势主要有：快速浮出部分易浮锂辉石矿物颗粒，以相对慢浮含锂矿物所得中矿泡沫产品再磨再选，有利于提高复杂嵌布矿物的解离效率，同时慢浮中矿经再磨再选后尾矿再循环返回粗选，还能有效缓解粗选不当积压中矿的现象，继而增强目的矿物分选的选择性，提高粗选分选效率；该方案的不足在于，流程结构复杂，需配置中矿再磨系统，并配备多段矿浆流的分支分导管路系统，生产设备的投资成本相对更高。方案3的优势在于：预先脱泥后再浮选，流程结构简单，现场管理与维护方便，通过物理方法预先脱除部分细泥，减少细泥对锂浮选分离系统的不当影响，其药剂消耗量低，相对于全粒级浮选流程捕收剂用量降低约30%；其不足在于锂浮选分离指标的好坏取决于脱泥效果的好坏，且脱泥过多时细泥损失锂较多。
综上所述，对于从复杂矿物嵌布的低品位锂辉石矿中浮选回收锂辉石矿物，经全粒级浮选与脱泥-浮选工艺方案的对比研究发现，除考虑提高Li2O回收率指标外，从矿物有效解离控制、磨浮系统适应性的角度出发，推荐采用方案2（两粗两扫三精全粒级浮选-中矿再磨），该工艺闭路浮选获得了Li2O品位5.76%、Li2O回收率86.30%的锂精矿；从减少浮选药剂消耗、规避细泥影响角度出发，推荐方案3（脱泥-一粗二扫三精浮选-中矿顺序返回），该方案闭路浮选获得了Li2O品位5.53%、Li2O回收率86.75%的锂精矿。
4　结论
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1）试验用锂原矿Li2O品位为1.26%，为低品位锂辉石矿；矿石中含锂矿物绝大多数为锂辉石矿物，其与脉石矿物呈多相矿物集合体，有部分锂矿物与脉石矿物互含并呈微细粒被包裹结构，致使含锂矿物磨矿高效解离与选择性浮游分离难度大。
2）3种浮选流程可从该锂矿石中浮选获得Li2O品位高于5.5%、Li2O回收率高于85%的锂精矿；相对于常规的全粒级浮选-中矿顺序返回浮选工艺流程（方案1），采用全粒级浮选-中矿再磨浮选工艺流程（方案2）和脱泥-浮选-中矿顺序返回工艺流程（方案3）所得锂精矿中Li2O回收率分别提高了1.08和1.53百分点，其中，又以脱泥-浮选-中矿顺序返回工艺流程（方案3）获得的Li2O回收率最高（86.75%），以全粒级浮选-中矿再磨浮选工艺流程（方案2）获得的锂精矿Li2O品位最高（5.76%）。
3）除考虑Li2O回收率指标外，从矿物有效解离控制、磨浮系统适应性的角度出发，推荐采用两粗两扫三精全粒级浮选-中矿再磨的浮选工艺流程（闭路试验获得了Li2O品位5.76%、Li2O回收率86.30%的锂精矿）；从减少浮选药剂消耗、规避细泥影响角度出发，推荐采用脱泥-一粗二扫三精浮选-中矿顺序返回的浮选工艺流程（闭路试验获得了Li2O品位5.53%、Li2O回收率86.75%的锂精矿）。
基金
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  • 中冶集团“181计划”重大研发项目(YG2474)
文献
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2025年第45卷第2期
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doi: 10.3969/j.issn.0253-6099.2025.02.018
  • 接收时间：2024-10-20
  • 首发时间：2026-03-19
  • 出版时间：2025-04-01
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  • 收稿日期：2024-10-20
基金
中冶集团“181计划”重大研发项目(YG2474)
作者信息
    1.中国恩菲工程技术有限公司，北京　100038
    2.中国冶金科工集团有限公司，北京　100028
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https://castjournals.cast.org.cn/joweb/kygczz/CN/10.3969/j.issn.0253-6099.2025.02.018
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2种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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