Article(id=1314907936082715085, tenantId=1146029695717560320, journalId=1235980550691926019, issueId=1314907935172551117, articleNumber=null, orderNo=null, doi=10.3969/j.issn.0253-6099.2026.03.021, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1764259200000, receivedDateStr=2025-11-28, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1791428081770, onlineDateStr=2026-10-08, pubDate=null, pubDateStr=null, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1791428081770, onlineIssueDateStr=2026-10-08, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1791428081770, creator=13701087609, updateTime=1791428081770, updator=13701087609, issue=Issue{id=1314907935172551117, tenantId=1146029695717560320, journalId=1235980550691926019, year='2026', volume='46', issue='3', pageStart='1', pageEnd='297', issueExtLink='null', onlineDate='null', pubDate='1780243200000', pubDateStr='2026-06-01', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1791428081553, creator='13701087609', updateTime=1791430444568, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1314917846770082179, tenantId=1146029695717560320, journalId=1235980550691926019, issueId=1314907935172551117, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1314917846770082180, tenantId=1146029695717560320, journalId=1235980550691926019, issueId=1314907935172551117, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=156, endPage=159, ext={EN=ArticleExt(id=1314907936971907534, articleId=1314907936082715085, tenantId=1146029695717560320, journalId=1235980550691926019, language=EN, title=Prediction of Key Indices of Ilmenite Separation Based on Dry Magnetic Separator, columnId=1236276106932842717, journalTitle=Mining and Metallurgical Engineering, columnName=MINERAL PROCESSING, runingTitle=null, highlight=null, articleAbstract=

Aiming at the problems of accuracy in prediction of ore grade and recovery in dry magnetic separation process, a prediction model framework integrating XGBoost and Transformer was proposed. In this method, XGBoost is used to extract local features and enhance features via leaf node information, improving the representation ability of input data. Subsequently, Transformer employs a multi-head self-attention mechanism to explore global dependencies among features and strengthen the prediction performance of the model. The prediction results of dry magnetic separation of ilmenite show that the constructed integrated model outperforms SVR, XGBoost, BP neural network and standard Transformer in terms of prediction accuracy and generalization ability.

, authors=Jiahao XIE1, Yang LIU1, 2, Chongzhen MA1, Shanglin ZENG1, Hengfa LIU1, Shengyue QI2, Kanglong HU2, Tao HUANG3, Ling QI2, Wenke ZHANG2, authorsList=Jiahao XIE, Yang LIU, Chongzhen MA, Shanglin ZENG, Hengfa LIU, Shengyue QI, Kanglong HU, Tao HUANG, Ling QI, Wenke ZHANG, authorCompany=null, correspAuthors=Yang LIU, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1314907939442352591, articleId=1314907936082715085, tenantId=1146029695717560320, journalId=1235980550691926019, language=CN, title=基于干式磁选机的钛铁矿分选关键指标预测, columnId=1236276107125780707, journalTitle=矿冶工程杂志, columnName=选矿, runingTitle=null, highlight=null, articleAbstract=

针对干式磁选工艺中矿石品位与回收率预测精度不高的问题,提出一种基于XGBoost与Transformer融合的预测模型框架。该方法通过XGBoost提取局部特征并利用叶节点信息实现特征增强,提升输入数据的表达能力;随后,Transformer利用多头自注意力机制挖掘特征间的全局依赖关系,强化模型的预测性能。针对钛铁矿干式磁选的预测结果表明,融合模型在预测精度与泛化能力方面均优于支持向量回归(SVR)、XGBoost、BP神经网络及标准Transformer。

, authors=谢家豪1, 刘洋1, 2, 马崇振1, 曾尚林1, 刘恒发1, 祁生月2, 胡康龙2, 黄涛3, 祁玲2, 张文科2, authorsList=谢家豪, 刘洋, 马崇振, 曾尚林, 刘恒发, 祁生月, 胡康龙, 黄涛, 祁玲, 张文科, authorCompany=null, correspAuthors=刘洋, authorNote=

谢家豪(2000—),男,湖南衡阳人,硕士,主要研究方向为矿物加工及选冶自动化。E-mail:

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刘洋(1983—),男,湖南岳阳人,博士,正高级工程师,主要研究方向为矿物加工及选冶智能化建设。E-mail:
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谢家豪(2000—),男,湖南衡阳人,硕士,主要研究方向为矿物加工及选冶自动化。E-mail:

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谢家豪(2000—),男,湖南衡阳人,硕士,主要研究方向为矿物加工及选冶自动化。E-mail:

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基于干式磁选机的钛铁矿分选关键指标预测
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谢家豪 1 , 刘洋 1, 2 , 马崇振 1 , 曾尚林 1 , 刘恒发 1 , 祁生月 2 , 胡康龙 2 , 黄涛 3 , 祁玲 2 , 张文科 2
矿冶工程杂志 | 选矿 2026,46(3): 156-159
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矿冶工程杂志 |选矿 2026 , 46 (3) : 156 -159
基于干式磁选机的钛铁矿分选关键指标预测
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谢家豪1 , 刘洋1, 2 , 马崇振1, 曾尚林1, 刘恒发1, 祁生月2, 胡康龙2, 黄涛3, 祁玲2, 张文科2
作者信息
  • 1.长沙矿冶研究院有限责任公司,湖南 长沙 410012
  • 2.青海西矿信息技术有限公司,青海 西宁 810000
  • 3.中铁十九局集团矿业投资有限公司,北京 100161
通讯作者:
刘洋(1983—),男,湖南岳阳人,博士,正高级工程师,主要研究方向为矿物加工及选冶智能化建设。E-mail:
作者简介:

谢家豪(2000—),男,湖南衡阳人,硕士,主要研究方向为矿物加工及选冶自动化。E-mail:

Prediction of Key Indices of Ilmenite Separation Based on Dry Magnetic Separator
Jiahao XIE1 , Yang LIU1, 2 , Chongzhen MA1, Shanglin ZENG1, Hengfa LIU1, Shengyue QI2, Kanglong HU2, Tao HUANG3, Ling QI2, Wenke ZHANG2
Affiliations
  • 1.Changsha Research Institute of Mining and Metallurgy Co., Ltd., Changsha 410012, Hunan, China
  • 2.Qinghai Xikuang Information Technology Co., Ltd., Xining 810000, Qinghai, China
  • 3.China Railway 19th Bureau Group Mining Investment Co., Ltd., Beijing 100161, China
doi: 10.3969/j.issn.0253-6099.2026.03.021
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针对干式磁选工艺中矿石品位与回收率预测精度不高的问题,提出一种基于XGBoost与Transformer融合的预测模型框架。该方法通过XGBoost提取局部特征并利用叶节点信息实现特征增强,提升输入数据的表达能力;随后,Transformer利用多头自注意力机制挖掘特征间的全局依赖关系,强化模型的预测性能。针对钛铁矿干式磁选的预测结果表明,融合模型在预测精度与泛化能力方面均优于支持向量回归(SVR)、XGBoost、BP神经网络及标准Transformer。

干式磁选  /  机器学习  /  预测模型  /  品位预测  /  回收率预测  /  XGBoost  /  Transformer  /  特征增强  /  钛铁矿

Aiming at the problems of accuracy in prediction of ore grade and recovery in dry magnetic separation process, a prediction model framework integrating XGBoost and Transformer was proposed. In this method, XGBoost is used to extract local features and enhance features via leaf node information, improving the representation ability of input data. Subsequently, Transformer employs a multi-head self-attention mechanism to explore global dependencies among features and strengthen the prediction performance of the model. The prediction results of dry magnetic separation of ilmenite show that the constructed integrated model outperforms SVR, XGBoost, BP neural network and standard Transformer in terms of prediction accuracy and generalization ability.

dry magnetic separation  /  machine learning  /  forecasting model  /  grade prediction  /  recovery prediction  /  XGBoost  /  Transformer  /  feature enhancement  /  ilmenite
谢家豪, 刘洋, 马崇振, 曾尚林, 刘恒发, 祁生月, 胡康龙, 黄涛, 祁玲, 张文科. 基于干式磁选机的钛铁矿分选关键指标预测. 矿冶工程杂志, 2026 , 46 (3) : 156 -159 . DOI: 10.3969/j.issn.0253-6099.2026.03.021
Jiahao XIE, Yang LIU, Chongzhen MA, Shanglin ZENG, Hengfa LIU, Shengyue QI, Kanglong HU, Tao HUANG, Ling QI, Wenke ZHANG. Prediction of Key Indices of Ilmenite Separation Based on Dry Magnetic Separator[J]. Mining and Metallurgical Engineering, 2026 , 46 (3) : 156 -159 . DOI: 10.3969/j.issn.0253-6099.2026.03.021
  • 国家重点研发计划(2021YFC2902703)
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2026年第46卷第3期
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doi: 10.3969/j.issn.0253-6099.2026.03.021
  • 接收时间:2025-11-28
  • 首发时间:2026-10-08
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  • 收稿日期:2025-11-28
基金
国家重点研发计划(2021YFC2902703)
作者信息
    1.长沙矿冶研究院有限责任公司,湖南 长沙 410012
    2.青海西矿信息技术有限公司,青海 西宁 810000
    3.中铁十九局集团矿业投资有限公司,北京 100161

通讯作者:

刘洋(1983—),男,湖南岳阳人,博士,正高级工程师,主要研究方向为矿物加工及选冶智能化建设。E-mail:
参考文献
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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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