Article(id=1226462298496221230, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1226462293408531329, articleNumber=null, orderNo=null, doi=null, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1744732800000, receivedDateStr=2025-04-16, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1770340998529, onlineDateStr=2026-02-06, pubDate=1761321600000, pubDateStr=2025-10-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1770340998529, onlineIssueDateStr=2026-02-06, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1770340998529, creator=13701087609, updateTime=1770340998529, updator=13701087609, issue=Issue{id=1226462293408531329, tenantId=1146029695717560320, journalId=1225396423026438145, year='2025', volume='45', issue='10', pageStart='1', pageEnd='288', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1770340997315, creator=13701087609, updateTime=1770341205851, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1226463168155792201, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1226462293408531329, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1226463168155792202, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1226462293408531329, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=102, endPage=111, ext={EN=ArticleExt(id=1226462298810794049, articleId=1226462298496221230, tenantId=1146029695717560320, journalId=1225396423026438145, language=EN, title=Study on the Relationship Between Physical Properties of Tailings and Solid Flux, columnId=null, journalTitle=Mining Research and Development, columnName=null, runingTitle=null, highlight=null, articleAbstract=

In order to explore the influence of physical properties of tailings on solid flux and optimize the thickening process parameters, the total tailings samples of 10 typical metal mines were selected, and the quantitative relationships between solid flux and tailings particle size or density was systematically studied. Combined with static flocculation sedimentation and dynamic thickening test data, a solid flux prediction model based on particle size-density composite parameters was established. The results show that under the condition of static flocculation sedimentation, the type and unit consumption of flocculant significantly affect the sedimentation rate and underflow concentration, and rational regulation of flocculation conditions can effectively improve sedimentation efficiency. The solid flux is significantly positively correlated with the square root of the median particle size and the density correction value of the tailings (R2≥0.94). The particle size-density composite parameters prediction model established based on nonlinear regression can accurately characterize the quantitative relationship between the physical properties of tailings and solid flux. Under dynamic thickening conditions, the feed rate of tailings slurry is linearly positively correlated with the solid flux, and the solid content of the overflow water forms a dual constraint mechanism on the flux threshold. The comparative test shows that the dynamic thickening process can increase the underflow concentration by 10%−15% compared with the static sedimentation, which fully verifies the technical advantages of the deep cone thickener in the preparation of high concentration slurry. The research results can provide theoretical basis and technical support for efficient thickening and intelligent filling of mine tailings.

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为探究尾砂物理性质对固体通量的影响规律并优化浓密工艺参数,选取10座典型金属矿山全尾砂样本,系统研究了尾砂粒度、密度等参数与固体通量的定量关系。结合静态絮凝沉降与动态浓密试验数据,建立了基于粒径-密度复合参数的固体通量预测模型。结果表明:在静态絮凝沉降条件下,絮凝剂类型及单耗显著影响沉降速率与底流浓度,合理调控絮凝条件可有效提升沉降效率;固体通量与尾砂中值粒径平方根及密度修正值呈成著正相关(R2≥0.94),基于非线性回归建立的粒径-密度复合参数预测模型,可精准表征尾砂物理性质与固体通量的定量关联;在动态浓密条件下,尾砂浆给料速度与固体通量成线性正相关,且溢流水含固量对通量阈值形成双重约束机制。对比试验表明,动态浓密工艺较静态沉降可使底流质量浓度提升10%~15%,充分验证了深锥浓密机在高浓度料浆制备中的技术优势。研究成果为矿山尾砂高效浓密与智能化充填提供了理论依据和技术支撑。

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喻海根(1999一),男,江西南昌人,硕士,工程师,主要从事采矿与充填技术等方面的研究工作。E-mail:
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刘奇(1987一),男,江西赣州人,博士,高级工程师,主要从事采矿与充填技术等方面的研究工作。E-mail:

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刘奇(1987一),男,江西赣州人,博士,高级工程师,主要从事采矿与充填技术等方面的研究工作。E-mail:

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tableContent=null), ArticleFig(id=1226462307803382584, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1226462298496221230, language=EN, label=Fig.4, caption=Linear relationship between variations in tailings density, particle size, concentration and solid flux, figureFileSmall=VlIpXQcXP1katu90F5Tdhg==, figureFileBig=zFz0ZQ/rsubO5KpXH5h/NQ==, tableContent=null), ArticleFig(id=1226462307904045886, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1226462298496221230, language=CN, label=图4, caption=尾砂密度、粒径、浓度改变量与固体通量的线性关系, figureFileSmall=VlIpXQcXP1katu90F5Tdhg==, figureFileBig=zFz0ZQ/rsubO5KpXH5h/NQ==, tableContent=null), ArticleFig(id=1226462308210230081, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1226462298496221230, language=EN, label=Fig.5, caption=Nonlinear relationship between tailings particle size and mass concentration variation, figureFileSmall=XVLZvdq1bykiGSXkwJYCUA==, figureFileBig=XODKNJa+2n6l8FH19AJ5Nw==, tableContent=null), ArticleFig(id=1226462308361225032, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1226462298496221230, language=CN, label=图5, caption=尾砂粒径与质量浓度变化量的非线性关系, figureFileSmall=XVLZvdq1bykiGSXkwJYCUA==, figureFileBig=XODKNJa+2n6l8FH19AJ5Nw==, tableContent=null), ArticleFig(id=1226462308461888331, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1226462298496221230, language=EN, label=Fig.6, caption=Schematic diagram of dynamic thickening test apparatus, figureFileSmall=MFrv7YzMcFSRWnHnPlrNxg==, figureFileBig=C9xemLWuhIsDqrshAoTK/g==, tableContent=null), ArticleFig(id=1226462308608688977, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1226462298496221230, language=CN, label=图6, caption=动态浓密试验装置示意, figureFileSmall=MFrv7YzMcFSRWnHnPlrNxg==, figureFileBig=C9xemLWuhIsDqrshAoTK/g==, tableContent=null), ArticleFig(id=1226462308696769367, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1226462298496221230, language=EN, label=Fig.7, caption=Relationship between the feeding rate of tailings slurry and underflow flux for mine 9, figureFileSmall=PiyAEnItttE6MEtlqAgGfg==, figureFileBig=9TZqGicsz6dErhajQCfubg==, tableContent=null), ArticleFig(id=1226462308797432672, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1226462298496221230, language=CN, label=图7, caption=矿山9料浆给料速度与底流通量的关系, figureFileSmall=PiyAEnItttE6MEtlqAgGfg==, figureFileBig=9TZqGicsz6dErhajQCfubg==, tableContent=null), ArticleFig(id=1226462308919067493, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1226462298496221230, language=EN, label=Fig.8, caption=Relationship between the feeding rate of tailings slurry and the solid content in overflow water for mine 9, figureFileSmall=lxEdvfM2J5kCPAnDZb363g==, figureFileBig=srDTIX/XFd684cT4879i6A==, tableContent=null), ArticleFig(id=1226462309011342184, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1226462298496221230, language=CN, label=图8, caption=矿山9料浆给料速度与溢流水含固量关系图, figureFileSmall=lxEdvfM2J5kCPAnDZb363g==, figureFileBig=srDTIX/XFd684cT4879i6A==, tableContent=null), ArticleFig(id=1226462309116199788, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1226462298496221230, language=EN, label=Fig.9, caption=Nonlinear relationship between solid flux and underflow flux, figureFileSmall=qjfjqaq6nP0XK/o6H3j2MA==, figureFileBig=+tNLMZe+slgQUq1jUiRTRQ==, tableContent=null), ArticleFig(id=1226462309216863092, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1226462298496221230, language=CN, label=图9, caption=固体通量与底流通量的非线性关系, figureFileSmall=qjfjqaq6nP0XK/o6H3j2MA==, figureFileBig=+tNLMZe+slgQUq1jUiRTRQ==, tableContent=null), ArticleFig(id=1226462309330109311, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1226462298496221230, language=EN, label=Table 1, caption=

Summary of flocculation sedimentation test results from different mines

, figureFileSmall=null, figureFileBig=null, tableContent=
矿山编号密度γ/(g·cm-3)粒径/mm最佳矿浆稀释质量浓度C0/%静态沉降试验底流质量浓度C1/%最佳絮凝剂类型絮凝剂单耗/(g·t-1)固体通量qt/(t·m-2·h-1)
中值粒径d50特征粒径d60
矿山12.1000.027 50.037 716.952.883376202.96
矿山22.2870.022 10.031 511.244.683376303.59
矿山32.5820.032 80.041 514.254.183376204.18
矿山42.9090.046 10.059 013.856.26013S155.67
矿山52.9040.050 10.063 116.759.983 376106.11
矿山62.5600.054 80.072 314.259.4AG9020104.76
矿山72.7520.061 30.081 215.761.383376155.95
矿山83.0090.084 70.105 516.763.8833767.57.27
矿山92.7010.107 70.138 615.267.383376106.69
矿山102.5100.102 10.129 914.165.3AG6025105.54
), ArticleFig(id=1226462309418189702, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1226462298496221230, language=CN, label=表1, caption=

各矿山絮凝沉降试验结果汇总

, figureFileSmall=null, figureFileBig=null, tableContent=
矿山编号密度γ/(g·cm-3)粒径/mm最佳矿浆稀释质量浓度C0/%静态沉降试验底流质量浓度C1/%最佳絮凝剂类型絮凝剂单耗/(g·t-1)固体通量qt/(t·m-2·h-1)
中值粒径d50特征粒径d60
矿山12.1000.027 50.037 716.952.883376202.96
矿山22.2870.022 10.031 511.244.683376303.59
矿山32.5820.032 80.041 514.254.183376204.18
矿山42.9090.046 10.059 013.856.26013S155.67
矿山52.9040.050 10.063 116.759.983 376106.11
矿山62.5600.054 80.072 314.259.4AG9020104.76
矿山72.7520.061 30.081 215.761.383376155.95
矿山83.0090.084 70.105 516.763.8833767.57.27
矿山92.7010.107 70.138 615.267.383376106.69
矿山102.5100.102 10.129 914.165.3AG6025105.54
), ArticleFig(id=1226462309518853001, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1226462298496221230, language=EN, label=Table 2, caption=

Results of dynamic thickening tests

, figureFileSmall=null, figureFileBig=null, tableContent=
矿山编号给料速度v/(L/min)给料质量浓度C0/%底流质量浓度C2/%底流通量qc/(t·m-2·h-1)溢流水含固量f/(mg/L)
矿山10.12916.970.00.18247.98
0.16516.967.90.23377.95
0.19216.967.80.27295.88
0.23816.967.10.338125.91
0.27416.966.90.388155.72
0.30116.966.60.427266.78
0.33016.966.40.468353.01
矿山20.29211.254.50.26791.02
0.40311.254.10.36896.73
0.48411.253.30.442114.85
0.61411.252.80.561194.87
0.72311.252.30.661322.04
矿山30.31114.267.90.36957.95
0.36814.267.80.43793.88
0.44114.267.10.525125.73
0.55614.266.90.660235.89
0.69514.264.40.826373.05
矿山40.50413.872.60.58563.01
0.58813.871.90.682100.88
0.71413.869.30.828186.99
0.74813.867.80.867216.13
0.86413.866.91.002311.82
矿山50.42716.773.00.61274.08
0.51616.772.70.73995.71
0.61316.772.20.878195.92
0.67716.771.40.970325.86
矿山60.43814.272.50.52064.96
0.54714.272.20.650124.01
0.62314.271.90.740194.23
0.66514.271.30.790246.83
0.71514.270.80.850341.68
矿山70.48015.774.90.63956.51
0.62015.773.60.827168.43
0.68015.772.90.907225.86
0.78915.771.41.052353.26
矿山80.45116.778.80.64888.11
0.49316.778.70.70991.00
0.55716.778.60.799106.80
0.66216.777.10.951194.86
0.78216.773.41.123323.76
矿山90.25015.281.70.32164.96
0.31015.281.10.39879.85
0.51515.280.80.662125.73
0.65215.279.40.838155.84
0.77715.278.60.998290.93
0.89815.277.51.153462.12
矿山100.45114.178.10.53171.74
0.59814.177.90.704122.85
0.68914.177.40.812190.03
0.84314.176.80.993282.97
0.97714.176.41.151368.13
), ArticleFig(id=1226462309653070739, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1226462298496221230, language=CN, label=表2, caption=

动态浓密试验结果

, figureFileSmall=null, figureFileBig=null, tableContent=
矿山编号给料速度v/(L/min)给料质量浓度C0/%底流质量浓度C2/%底流通量qc/(t·m-2·h-1)溢流水含固量f/(mg/L)
矿山10.12916.970.00.18247.98
0.16516.967.90.23377.95
0.19216.967.80.27295.88
0.23816.967.10.338125.91
0.27416.966.90.388155.72
0.30116.966.60.427266.78
0.33016.966.40.468353.01
矿山20.29211.254.50.26791.02
0.40311.254.10.36896.73
0.48411.253.30.442114.85
0.61411.252.80.561194.87
0.72311.252.30.661322.04
矿山30.31114.267.90.36957.95
0.36814.267.80.43793.88
0.44114.267.10.525125.73
0.55614.266.90.660235.89
0.69514.264.40.826373.05
矿山40.50413.872.60.58563.01
0.58813.871.90.682100.88
0.71413.869.30.828186.99
0.74813.867.80.867216.13
0.86413.866.91.002311.82
矿山50.42716.773.00.61274.08
0.51616.772.70.73995.71
0.61316.772.20.878195.92
0.67716.771.40.970325.86
矿山60.43814.272.50.52064.96
0.54714.272.20.650124.01
0.62314.271.90.740194.23
0.66514.271.30.790246.83
0.71514.270.80.850341.68
矿山70.48015.774.90.63956.51
0.62015.773.60.827168.43
0.68015.772.90.907225.86
0.78915.771.41.052353.26
矿山80.45116.778.80.64888.11
0.49316.778.70.70991.00
0.55716.778.60.799106.80
0.66216.777.10.951194.86
0.78216.773.41.123323.76
矿山90.25015.281.70.32164.96
0.31015.281.10.39879.85
0.51515.280.80.662125.73
0.65215.279.40.838155.84
0.77715.278.60.998290.93
0.89815.277.51.153462.12
矿山100.45114.178.10.53171.74
0.59814.177.90.704122.85
0.68914.177.40.812190.03
0.84314.176.80.993282.97
0.97714.176.41.151368.13
), ArticleFig(id=1226462309766316953, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1226462298496221230, language=EN, label=Table 3, caption=

Relationship between feeding rate, underflow flux, and solid content in overflow water for different mines

, figureFileSmall=null, figureFileBig=null, tableContent=
矿山编号给料速度v与底流通量qc的关系给料速度v与溢流水含固量f的关系
矿山1qc=1.417 2vf=69 569v3-39 178v2+7 823.3v-457.74
矿山2qc=0.913 6vf=2 077.7v3-1 413.6v2+267.91v+81.732
矿山3qc=1.188 8vf=-1 937.2v3+3 827.4v2-1 494.4v+213.99
矿山4qc=1.159 8vf=-1 825.2v3+4 449.3v2-2 773.1v+564.09
矿山5qc=1.433 4vf=7 688v3-7 703.9v2+2 366.3v-130.21
矿山6qc=1.188 2vf=8 899.3v3-12 457v2+6 314.7v-1 058.8
矿山7qc=1.333 3vf=1 350.8v3-1 589.1v2+1 311.5v-355.99
矿山8qc=1.436 6vf=-4 935v3+11 128v2-7 250v+1 548.5
矿山9qc=1.284 8vf=2 525v3-3 102v2+1 419.7v-135.72
矿山10qc=1.177 6vf=-1 430.6v3+3 350.8v2-1 934.7v+393.05
), ArticleFig(id=1226462309913117596, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1226462298496221230, language=CN, label=表3, caption=

各矿山给料速度与底流通量和溢流水含固量的关系。

, figureFileSmall=null, figureFileBig=null, tableContent=
矿山编号给料速度v与底流通量qc的关系给料速度v与溢流水含固量f的关系
矿山1qc=1.417 2vf=69 569v3-39 178v2+7 823.3v-457.74
矿山2qc=0.913 6vf=2 077.7v3-1 413.6v2+267.91v+81.732
矿山3qc=1.188 8vf=-1 937.2v3+3 827.4v2-1 494.4v+213.99
矿山4qc=1.159 8vf=-1 825.2v3+4 449.3v2-2 773.1v+564.09
矿山5qc=1.433 4vf=7 688v3-7 703.9v2+2 366.3v-130.21
矿山6qc=1.188 2vf=8 899.3v3-12 457v2+6 314.7v-1 058.8
矿山7qc=1.333 3vf=1 350.8v3-1 589.1v2+1 311.5v-355.99
矿山8qc=1.436 6vf=-4 935v3+11 128v2-7 250v+1 548.5
矿山9qc=1.284 8vf=2 525v3-3 102v2+1 419.7v-135.72
矿山10qc=1.177 6vf=-1 430.6v3+3 350.8v2-1 934.7v+393.05
), ArticleFig(id=1226462310097666977, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1226462298496221230, language=EN, label=Table 4, caption=

Relationship between solid flux and feeding rate, solid content in overflow water

, figureFileSmall=null, figureFileBig=null, tableContent=
矿山名称固体通量/(t·m-2·h-1)溢流水含固量200 mg/L溢流水含固量240 mg/L溢流水含固量270 mg/L溢流水含固量300 mg/L
给料速度/(L/min)底流通量/(t·m-2·h-1)给料速度/(L/min)底流通量/(t·m-2·h-1)给料速度/(L/min)底流通量/(t·m-2·h-1)给料速度/(L/min)底流通量/(t·m-2·h-1)
矿山12.960.2850.4040.2990.4240.3090.4370.3170.449
矿山23.590.6200.5660.6600.6030.6850.6260.7080.647
矿山34.180.5210.6190.5630.6690.5930.7050.6230.741
矿山45.670.7290.8460.7780.9020.8130.9430.8490.985
矿山56.110.6150.8820.6380.9140.6530.9350.6660.955
矿山64.760.6290.7480.6590.7830.6780.8060.6950.826
矿山75.950.6540.8720.6940.9250.7210.9610.7470.996
矿山87.270.6680.9600.7061.0140.7331.0530.7601.092
矿山96.690.6950.8930.7410.9520.7690.9880.7941.020
矿山105.540.7160.8430.7810.9200.8270.9740.8641.017
), ArticleFig(id=1226462310198330279, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1226462298496221230, language=CN, label=表4, caption=

固体通量与给料速度、溢流水含固量的关系

, figureFileSmall=null, figureFileBig=null, tableContent=
矿山名称固体通量/(t·m-2·h-1)溢流水含固量200 mg/L溢流水含固量240 mg/L溢流水含固量270 mg/L溢流水含固量300 mg/L
给料速度/(L/min)底流通量/(t·m-2·h-1)给料速度/(L/min)底流通量/(t·m-2·h-1)给料速度/(L/min)底流通量/(t·m-2·h-1)给料速度/(L/min)底流通量/(t·m-2·h-1)
矿山12.960.2850.4040.2990.4240.3090.4370.3170.449
矿山23.590.6200.5660.6600.6030.6850.6260.7080.647
矿山34.180.5210.6190.5630.6690.5930.7050.6230.741
矿山45.670.7290.8460.7780.9020.8130.9430.8490.985
矿山56.110.6150.8820.6380.9140.6530.9350.6660.955
矿山64.760.6290.7480.6590.7830.6780.8060.6950.826
矿山75.950.6540.8720.6940.9250.7210.9610.7470.996
矿山87.270.6680.9600.7061.0140.7331.0530.7601.092
矿山96.690.6950.8930.7410.9520.7690.9880.7941.020
矿山105.540.7160.8430.7810.9200.8270.9740.8641.017
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尾砂物理性质与固体通量的关系研究
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刘奇 1, 2 , 喻海根 1, 2 , 杨宁 1, 2 , 唐鸣东 1, 2 , 李晓辉 1, 2
矿业研究与开发 | 采矿与矿山充填 2025,45(10): 102-111
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矿业研究与开发 | 采矿与矿山充填 2025, 45(10): 102-111
尾砂物理性质与固体通量的关系研究
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刘奇1, 2 , 喻海根1, 2 , 杨宁1, 2, 唐鸣东1, 2, 李晓辉1, 2
作者信息
  • 1.长沙矿山研究院有限责任公司,湖南 长沙 410012
  • 2.国家金属采矿工程技术研究中心,湖南 长沙 410012
  • 刘奇(1987一),男,江西赣州人,博士,高级工程师,主要从事采矿与充填技术等方面的研究工作。E-mail:

通讯作者:

喻海根(1999一),男,江西南昌人,硕士,工程师,主要从事采矿与充填技术等方面的研究工作。E-mail:
Study on the Relationship Between Physical Properties of Tailings and Solid Flux
Qi LIU1, 2 , Haigen YU1, 2 , Ning YANG1, 2, Mingdong TANG1, 2, Xiaohui LI1, 2
Affiliations
  • 1.Changsha Institute of Mining Research Co., Ltd., Changsha, Hunan 410012, China
  • 2.National Engineering Technology Research Center for Metal Mining, Changsha, Hunan 410012, China
出版时间: 2025-10-25
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为探究尾砂物理性质对固体通量的影响规律并优化浓密工艺参数,选取10座典型金属矿山全尾砂样本,系统研究了尾砂粒度、密度等参数与固体通量的定量关系。结合静态絮凝沉降与动态浓密试验数据,建立了基于粒径-密度复合参数的固体通量预测模型。结果表明:在静态絮凝沉降条件下,絮凝剂类型及单耗显著影响沉降速率与底流浓度,合理调控絮凝条件可有效提升沉降效率;固体通量与尾砂中值粒径平方根及密度修正值呈成著正相关(R2≥0.94),基于非线性回归建立的粒径-密度复合参数预测模型,可精准表征尾砂物理性质与固体通量的定量关联;在动态浓密条件下,尾砂浆给料速度与固体通量成线性正相关,且溢流水含固量对通量阈值形成双重约束机制。对比试验表明,动态浓密工艺较静态沉降可使底流质量浓度提升10%~15%,充分验证了深锥浓密机在高浓度料浆制备中的技术优势。研究成果为矿山尾砂高效浓密与智能化充填提供了理论依据和技术支撑。

尾砂粒径  /  絮凝沉降  /  动态沉降  /  固体通量  /  回归分析

In order to explore the influence of physical properties of tailings on solid flux and optimize the thickening process parameters, the total tailings samples of 10 typical metal mines were selected, and the quantitative relationships between solid flux and tailings particle size or density was systematically studied. Combined with static flocculation sedimentation and dynamic thickening test data, a solid flux prediction model based on particle size-density composite parameters was established. The results show that under the condition of static flocculation sedimentation, the type and unit consumption of flocculant significantly affect the sedimentation rate and underflow concentration, and rational regulation of flocculation conditions can effectively improve sedimentation efficiency. The solid flux is significantly positively correlated with the square root of the median particle size and the density correction value of the tailings (R2≥0.94). The particle size-density composite parameters prediction model established based on nonlinear regression can accurately characterize the quantitative relationship between the physical properties of tailings and solid flux. Under dynamic thickening conditions, the feed rate of tailings slurry is linearly positively correlated with the solid flux, and the solid content of the overflow water forms a dual constraint mechanism on the flux threshold. The comparative test shows that the dynamic thickening process can increase the underflow concentration by 10%−15% compared with the static sedimentation, which fully verifies the technical advantages of the deep cone thickener in the preparation of high concentration slurry. The research results can provide theoretical basis and technical support for efficient thickening and intelligent filling of mine tailings.

Tailings particle size  /  Flocculation sedimentation  /  Dynamic sedimentation  /  Solid flux  /  Regression analysis
刘奇, 喻海根, 杨宁, 唐鸣东, 李晓辉. 尾砂物理性质与固体通量的关系研究. 矿业研究与开发, 2025 , 45 (10) : 102 -111 .
Qi LIU, Haigen YU, Ning YANG, Mingdong TANG, Xiaohui LI. Study on the Relationship Between Physical Properties of Tailings and Solid Flux[J]. Mining Research and Development, 2025 , 45 (10) : 102 -111 .
在矿产资源日益紧缺与环保标准持续升级的双重驱动下,充填采矿凭借其资源高效回采率与环境扰动最小化的显著优势,已成为金属矿山绿色开采的重要方法[1-3]。充填料浆的制备是充填采矿法的关键环节,而尾砂因其来源广泛、成本低廉,常被视为充填骨料的首选[4-6]。然而,由于选矿厂排出的尾矿浓度普遍较低,无法满足当前高浓度充填甚至膏体充填的要求,因此必须进行浓缩脱水处理[7-9]。固体通量作为尾砂浓密性能的关键技术参数之一,对料浆的输送可靠性和充填体的强度具有重要影响[10-12]
近年来,众多学者围绕尾砂浓密参数及其规律展开了深入研究。阮竹恩等[13]全面梳理了近20年来我国全尾砂料浆浓密研究的丰硕成果,涵盖了絮凝行为、沉降规律、脱水机理及浓密机设计等多个方面,并展望了基于流变学和人工智能的未来发展方向。YIN S H等[14]通过细致研究细粒级全尾砂的絮凝沉降特性、力学性能及影响因素,揭示了阴离子絮凝剂的优异效果,同时指出沉降参数受多重因素交互作用的复杂影响,并确定了静态沉降效果最佳的工艺条件。诸利一等[15]运用正交试验方法,系统研究了尾砂浓度、絮凝剂类型等因素对絮凝沉降与浓密效果的影响,优化了工艺条件,提升了沉降效率。杨莹等[16]则利用先进的图像处理技术,开发了尾砂絮凝沉降观测系统,实现了沉降过程的智能识别,优化了浓密机参数,提高了试验的精度和效率。JIAO H Z等[17]借助高精度CT扫描和三维重建技术,深入探究了深锥浓密机底流浓度的提升策略,发现适当的剪切速率能显著提升充填料浆质量浓度,优化孔隙结构,促进尾砂料浆的增稠和高效利用。王石等[18]研究了给料质量分数对尾砂浆沉降和水分迁移的影响机制,揭示了低质量分数对提升沉降速度和浓密效果的积极作用。王洪江等[19]则探索了双重絮凝工艺对超细尾砂浓密脱水性能的提升效果,发现“阴离子+阳离子”组合效果最佳,显著提高了底流浓度和沉降速度。温震江等[20]采用响应面法优化了全尾砂浆絮凝沉降参数,为浓密机参数设计提供了坚实的理论依据。陈格仲等[21]分析了絮凝条件对尾砂絮团结构及固液分离效率的影响,通过优化絮凝条件,提高了浓密机沉降区的固液分离效率。彭青松等[22]则研究了超声波对尾矿底流浓密效果及流变性的影响,发现超声波能显著提升底流浓度,改善浓密效果。ZHU L Y等[23]进一步探究了超声波参数对全尾砂底流浓度的影响,揭示了在最佳参数下,超声波能显著提升底流质量浓度至71.75%,且20 kHz超声波通过机械振动和空化作用促进尾砂料浆的致密化,效果显著。
尽管上述研究在尾砂浓密领域已取得显著进展,但多侧重于单一因素或特定条件下的分析,且关于尾砂物理性质(特别是粒径和密度)与固体通量之间的定量关系研究尚显薄弱。鉴于此,本研究选取10座典型金属矿山的全尾砂样本,通过系统的静态沉降与动态浓密试验,深入探究尾砂物理性质对固体通量的影响机制,旨在构建尾砂物理性质与固体通量的定量关系模型,为矿山尾砂浓密处理提供科学的理论依据和技术支撑,以期实现尾砂资源的高效利用和矿山开采的可持续发展。
基于马尔文激光衍射系统(Mastersizer系列),对10座矿山的全尾砂样本进行了粒度分布测试,主要表征参数包括中值粒径(d50)及特征粒径(d60),并结合标准比重瓶法(三次平行试验取均值)测定真密度,获得基础物理参数数据集(见表1)。由表1可知,矿山1至矿山4的尾砂颗粒分布呈超细粉体特征(d50<50 μm),矿山5至矿山8的尾砂样本呈现中等粒径分布(50 μm≤d50≤100 μm),而矿山9至矿山10的尾砂样本则具有典型的粗粒级特性(d50>100 μm)。
以矿山8所产的尾砂为研究对象,开展了沉降试验。在确保料浆质量浓度恒定的条件下,分别向尾砂中添加了用量为20 g/t的83376型、AG9020型及665 V型3种阴离子絮凝剂。通过综合评估沉降速率、上清液透明度及底流质量浓度等多项指标,最终确定83376型絮凝剂为处理矿山8尾砂的最优选择。
为确定矿山8尾砂的最佳絮凝剂(83376型絮凝剂)单耗,选取20.9%、18.3%、16.6%、14.5%这4种不同的料浆质量浓度,并分别按照5.0 g/t、7.5 g/t、10.0 g/t3种絮凝剂添加量进行平行试验,试验结果如图1所示。
图1展示了矿山8尾砂在不同料浆质量浓度下的沉降效果随絮凝剂添加量的变化关系。观察发现,当絮凝剂单耗处于5.0 g/t至10.0 g/t区间内时,尾砂的沉降效果与絮凝剂添加量呈正相关关系,即随着絮凝剂单耗的增加,沉降效果也相应得到提升。当絮凝剂单耗超过7.5 g/t时,沉降效果的改善趋势放缓。因此,确定83376型絮凝剂的最佳单耗为7.5 g/t。
静态沉降试验的固体通量是指:尾砂在量筒中,在自由沉降过程中(沉降曲线直线段)单位时间(1 h)、单位面积(m2)内靠尾砂自重沉降的干尾砂质量,固体通量与尾砂的自然沉降速度线性相关。通过分析固体通量和充填料浆质量浓度的关系曲线(见图2),确定矿山8充填料浆的最佳质量浓度为16.7%,此时固体通量达到最大值7.27 t/(m2·h)。
由此可知,将矿浆质量浓度稀释至16.7%,并选用83376型絮凝剂,且将其单耗控制在7.5 g/t,即可实现矿山8尾砂的最优絮凝沉降效果。
表1全面汇总了10座矿山全尾砂的粒径分布、密度特性,以及各自所适用的最佳絮凝剂类型、絮凝剂单耗、矿浆稀释浓度和固体通量等相关参数数据。
试验数据表明,尾砂的沉降行为与其粒度特征及物质密度具有显著相关性。根据表1的实测结果,较大粒径和高密度颗粒在流体介质中表现出更显著的沉降速度优势。为深入揭示其作用机理,本研究系统考察了粒径-密度复合参数与固体通量的定量关系。
图3(a)所示,直接采用原始中值粒径与密度的乘积参数时,其与固体通量未形成显著的单变量关联规律,即使采用四次多项式模型,决定系数仅为0.841 4。通过引入粒径参数的平方根校正,如图3(b)所示,复合参数的非线性响应特性明显改善,模型拟合优越度提升至0.920 8。
基于斯托克斯沉降理论中浮力作用的力学分析(流体密度取1 g/cm3),对密度参数进行浮力校正处理(γ−1)。如图3(c)所示,校正后的复合参数与固体通量展现出更强的非线性关联,四次多项式模型的决定系数达到0.952 6。值得注意的是,该参数组合同时呈现出良好的线性响应特性,如图3(d)所示,一元线性回归模型的决定系数达到0.940 2,这为工程应用中的简化计算提供了理论依据。
基于同源尾砂的絮凝沉降试验研究表明,在絮凝剂类型与絮凝剂单耗固定的条件下,矿浆初始质量浓度对沉降动力学过程有显著影响。试验数据显示,固体通量与浓度变化值之间通常呈负相关关系,具体表现为底流浓度与初始浓度的偏差值增大时,固体通量相应降低。
图4所示,引入尾砂的中值粒径或特征粒径的开平方值、密度与料浆质量浓度的复合参数时,该参数与固体通量之间呈显著线性关系,其定量关系见式(1)和式(2)。从物理意义上分析:当体系不存在固体颗粒(d50=0)时,式(1)预测的底流质量浓度变化量仅为−2.9%,而式(2)在d60=0条件下得出底流质量浓度变化量为−33.27%。对比可知,式(1)的预测偏差较式(2)降低一个数量级,说明基于中值粒径d50的多元线性回归模型具有更优的工程适用性。
式中:qt为固体通量,t·m−2·h−1γ为尾砂密度,g·cm−3C1为静态絮凝沉降试验料浆底流浓度,%;C0为料浆初始浓度(给料浓度),%;d50为尾砂中值粒径,mm;d60为尾砂特征粒径,mm。
表1数据还表明,尾砂粒径越粗,其底流浓度越高。沉降试验进一步显示,料浆初始浓度越高,底流浓度也增大。因此,需要考虑料浆质量浓度变化量(底流浓度与初始浓度的差值)与尾砂粒径之间的关系。
试验数据表明,粒径分布特征与料浆质量浓度演变显著相关:粗颗粒尾砂具有更高的底流浓度(表1),且该趋势随初始浓度提高更为明显。这表明有必要建立粒径参数(d50d60)与料浆质量浓度变化量之间的定量关系模型。
图5展示了尾砂中值粒径d50和特征粒径d60与浓度变化量之间的函数关系,其拟合程度接近99%。结果表明,仅通过尾砂的中值粒径和特征粒径即可有效预测料浆质量浓度变化量。对应函数关系见式(3)和式(4)。
非线性回归分析进一步证实,中值粒径与特征粒径分别与料浆质量浓度变化量呈强相关性(决定系数达0.99)。
从物理约束条件分析:当系统中不含固体颗粒时,理论上料浆质量浓度变化量应趋近于0。式(3)在d50=0时得出料浆质量浓度变化量为10.49%, 而式(4)对应d60=0时料浆质量浓度变化量为−5.18%, 相较而言,式(4)的预测偏离度降低了82%, 表明特征粒径模型具有更优的物理自洽性。通过联立式(1)与式(4),最终构建了融合粒径、密度与固体通量的综合预测方程,即式(5)。
式中,C1C0为料浆质量浓度改变量,%。
动态浓密装置(见图6)仿照深锥浓密机设计,采用4个蠕动泵分别泵送稀释水、絮凝剂、尾矿矿浆进入给料系统,并从底部泵出底流矿浆。稀释水为自来水,絮凝剂添加量参考静态试验数据,矿浆配成10%~18%的质量浓度后搅拌均匀并泵入管道。通过调整蠕动泵转速,模拟不同工况下的浓密试验结果。泥层高度达到150 mm和240 mm时,分别取样测量溢流水和底流浓度。动态浓密装置试验所得的固体通量值和底流浓度,与深锥浓密机现场工业试验的结果高度一致。
根据全尾砂静态絮凝沉降试验确定的最佳絮凝剂类型、单耗及矿浆稀释浓度,对来自10座不同矿山的尾砂,在不同给料速度下,分别研究了其对溢流水澄清度及动态浓密试验固体通量的影响,试验结果见表2。动态浓密沉降试验中的固体通量,又称底流通量,指动态浓密装置在单位时间(1 h)、单位面积(m2)内处理尾砂的能力,即底部持续排出的高浓度砂浆的干重。该参数与尾砂的自然沉降速度、干涉沉降效应及挤压沉淀速率密切相关。由表2可知,随着给料速度的增加,底流通量呈上升趋势,同时溢流水中的含固量也随之增加。这主要是因为给料速度的提高缩短了尾矿颗粒的絮凝反应时间,导致絮凝过程不够充分,进而使得溢流水中的含固量增加。
根据物质守恒定律,流入动态浓密装置的尾砂总量与排出的尾砂总量相等。根据溢流水排放标准,溢流水中的含固量不能超过300 mg/L,因此溢流水中排出的尾砂质量可以忽略不计。由此可知,给料速度与底流通量成正比。以矿山9为例,该矿山给料速度与底流通量的关系见表3图7。在本次试验中,不同给料速度下试验对应的溢流水含固量范围为50~400 mg/L。
料浆给料速度是影响选厂尾砂输送系统设计和深锥浓密机浓密能力的重要指标。给料速度主要根据浓密效果,尤其是溢流水含固量确定。以矿山9为例,该矿山的尾砂料浆给料速度与溢流水含固量关系见图8。在图8中同时给出了给料速度与溢流水含固量的拟合曲线,以及用三次多项式拟合的曲线方程与拟合度。10座矿山给料速度与溢流水含固量的关系见表3
固体通量是确定浓密机面积的主要参数,当溢流水中含固量分别为200, 240, 270, 300 mg/L时,静态沉降试验装置和动态浓密试验装置处理这10座矿山尾砂的能力(固体通量和底流通量)见表4
结合表1表2表4可知,动态浓密沉降试验底流质量浓度要比静态絮凝沉降试验底流质量浓度高出10%~15%,这说明经过动态浓密装置(深锥浓密机)处理后料浆的底流浓度要比静态沉降装置(立式砂仓)处理后料浆的底流高,低浓度的砂浆经过动态浓密装置处理后可以稳定输出高浓度的砂浆,且溢流水含固量不会超标,这表明深锥浓密机可以更有效地提高底流浓度、有利于提高井下充填体强度、增强料浆高浓度输送的可靠性。
静态絮凝沉降试验与动态浓密试验固体通量的关系以及尾砂粒径、密度与固体通量的关系见图9及式(6)至式(9)。图9中给出了固体通量qt与底流通量qc的拟合曲线及用二次多项式拟合的曲线方程与拟合度。由式(5)至式(9)可知,只要知道某矿山尾砂的粒径和密度,就能预测出静态絮凝沉降试验与动态浓密试验的固体通量,计算出该矿山深锥浓密机的面积大小。
深锥浓密机面积设计与稀释浓度协同控制策略是基于尾砂粒径、密度及料浆质量浓度变化量的综合考量而提出的。该策略的核心在于通过优化深锥浓密机的设计面积和初始料浆的稀释浓度,以实现最佳的浓密效果和经济效益。
(1)首先,根据尾砂的粒径分布和真密度,利用已建立的粒径-密度复合参数预测模型,估算出在不同稀释浓度下可能达到的底流浓度和固体通量。
(2)结合生产实际和浓密机处理能力,选择一个既能保证较高底流浓度,又能避免浓密机过载的初始料浆稀释浓度。
(3)基于预计的固体通量和所需的底流浓度,利用固体通量预测模型反算推出所需的深锥浓密机面积。同时,考虑浓密机的操作弹性和维护便利性,对设计面积进行适当调整。
(4)在实际生产过程中,根据尾砂性质的波动和浓密效果的反馈,适时调整稀释浓度和深锥浓密机的操作参数,以实现协同控制,确保浓密效果的稳定性及浓密作业的高效率。
通过上述协同控制策略的实施,可有效提高尾砂浓密过程的效率和稳定性,降低生产成本,同时满足环保和安全生产的要求。
(1)基于10座典型金属矿山全尾砂样本的粒度分布与真密度参数测定,结合静态-动态联合试验体系,创新构建了尾砂物理性质与固体通量的定量关系模型。该模型表明,固体通量与尾砂中值粒径平方根及密度修正值之间成显著正相关(R2≥0.94),为尾砂浓密过程提供了精准预测工具,显著提升了固体通量预测的科学性与可靠性。
(2)在动态浓密条件下,研究发现尾砂浆给料速度与固体通量成线性正相关,而溢流水含固量对固体通量阈值具有明显约束作用。通过非线性回归分析建立的粒径-密度复合参数预测方程,不仅深化了对尾砂浓密机理的理解,也为深锥浓密机选型设计及工艺参数优化提供了理论依据,实现了理论模型与工程应用的有机结合。
(3)对比试验表明,动态浓密装置可使底流质量浓度提高10%~15%,显著增强了料浆输送的稳定性与充填体强度。基于粒径-密度复合参数与浓度变化量的耦合模型,创新提出了深锥浓密机面积设计与稀释浓度协同控制策略,为尾砂资源高效利用及矿山智能充填提供了关键技术支撑,可为推动矿山的可持续发展提供帮助。
  • 国家重点研发计划项目(2023YFC2907404)
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2025年第45卷第10期
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  • 接收时间:2025-04-16
  • 首发时间:2026-02-06
  • 出版时间:2025-10-25
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  • 收稿日期:2025-04-16
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国家重点研发计划项目(2023YFC2907404)
作者信息
    1.长沙矿山研究院有限责任公司,湖南 长沙 410012
    2.国家金属采矿工程技术研究中心,湖南 长沙 410012

通讯作者:

喻海根(1999一),男,江西南昌人,硕士,工程师,主要从事采矿与充填技术等方面的研究工作。E-mail:
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2种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

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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