Article(id=1314907228251972289, tenantId=1146029695717560320, journalId=1235980550691926019, issueId=1314907112854089785, articleNumber=null, orderNo=null, doi=10.3969/j.issn.0253-6099.2025.06.018, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1751212800000, receivedDateStr=2025-06-30, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1791427913010, onlineDateStr=2026-10-08, pubDate=null, pubDateStr=null, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1791427913010, onlineIssueDateStr=2026-10-08, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1791427913010, creator=13701087609, updateTime=1791427913010, updator=13701087609, issue=Issue{id=1314907112854089785, tenantId=1146029695717560320, journalId=1235980550691926019, year='2025', volume='45', issue='6', pageStart='1', pageEnd='228', issueExtLink='null', onlineDate='null', pubDate='1764518400000', pubDateStr='2025-12-01', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1791427885497, creator='13701087609', updateTime=1791430521754, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1314918170201252492, tenantId=1146029695717560320, journalId=1235980550691926019, issueId=1314907112854089785, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1314918170201252493, tenantId=1146029695717560320, journalId=1235980550691926019, issueId=1314907112854089785, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=102, endPage=107, ext={EN=ArticleExt(id=1314907228864340674, articleId=1314907228251972289, tenantId=1146029695717560320, journalId=1235980550691926019, language=EN, title=Optimization of Reagent Regimen for Roughing of Bulk Flotation Using Bayesian Optimization, columnId=1236276106932842717, journalTitle=Mining and Metallurgical Engineering, columnName=MINERAL PROCESSING, runingTitle=null, highlight=null, articleAbstract=

To address problems of cumbersome procedures, time-consuming and high cost for traditional flotation test of molybdenum-bismuth ores, a Bayesian optimization (BO)-based reagent optimization model and a reagent simulation model with combining functions were established for molybdenum-bismuth ore flotation. The simulation functions of flotation reagent were processed with the BO-based model, and it is shown that in a test with 100 sets of computer simulation, the maximum of the function by BO after 25 iterations was 0.93 on average, compared to the optimum value of 0.86 after 25 trials by conventional single-factor test, presenting a 8.14% rise. This verifies the feasible application of BO applied in reagent optimization for molybdenum-bismuth flotation. The laboratory mineral processing data from a concentrator were taken in a study with BO. A model of ore grade and recovery was established via Gaussian process for model fitting. For obtaining the maximum of objective function by multiple iterations, it required an average of 18 iterations by using BO, while 30 trials on average were required by conventional single-factor test. It is concluded that BO can improve the optimization efficiency by 40%.

, authors=Yanglong ZHANG, Wen CHEN, Zhenhong LIAO, Ren LIU, authorsList=Yanglong ZHANG, Wen CHEN, Zhenhong LIAO, Ren LIU, authorCompany=null, correspAuthors=Wen CHEN, 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=1314907229254410947, articleId=1314907228251972289, tenantId=1146029695717560320, journalId=1235980550691926019, language=CN, title=基于贝叶斯优化的混合浮选粗选药剂制度优化, columnId=1236276107125780707, journalTitle=矿冶工程杂志, columnName=选矿, runingTitle=null, highlight=null, articleAbstract=

针对传统钼铋矿石的浮选试验繁琐、周期长、成本高的问题，构建了基于贝叶斯优化算法的钼铋矿物浮选药剂寻优模型和模拟钼铋矿物浮选药剂的组合函数模型。以贝叶斯优化模型处理浮选药剂的模拟函数结果显示，在100组计算机模拟试验下，贝叶斯优化迭代25次的当前最优值的均值为0.93，而正常单因素实验25组试验最优值的均值为0.86，贝叶斯优化同比提高了8.14%，证实了贝叶斯优化的优势；以某选矿厂实验室选矿数据进行贝叶斯优化研究，先经过高斯模型拟合建立关于矿石品位和回收率的模型，经过多次迭代后得到最佳目标值，贝叶斯优化平均需要18次试验，而传统的单因素实验则平均需要30次试验，贝叶斯优化与传统单因素实验相比，优化效率提升了40%。

, authors=张洋龙, 陈雯, 廖振鸿, 刘任, authorsList=张洋龙, 陈雯, 廖振鸿, 刘任, authorCompany=null, correspAuthors=陈雯, authorNote=, correspAuthorsNote=, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=asUL0q5hRX2LpGl8GIy4mg==, magXml=cTGjfKtXJ2qLcnyWg7HlpQ==, pdfUrl=null, pdf=UF+jjp9Vj0rWZxkMwFdYVQ==, pdfFileSize=782630, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=s+voGh9hLAosr47xVmTxMw==, mapNumber=null, fund=null)}, authors=[Author(id=1314907230093271752, tenantId=1146029695717560320, journalId=1235980550691926019, articleId=1314907228251972289, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=18110775915@163.com, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1314907230516896458, tenantId=1146029695717560320, journalId=1235980550691926019, articleId=1314907228251972289, authorId=1314907230093271752, language=EN, stringName=Yanglong ZHANG, firstName=Yanglong, middleName=null, lastName=ZHANG, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=null, address=Changsha Research Institute of Mining and Metallurgy Co., Ltd., Changsha 410012, Hunan, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1314907232219783884, tenantId=1146029695717560320, journalId=1235980550691926019, articleId=1314907228251972289, authorId=1314907230093271752, language=CN, stringName=张洋龙, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=null, address=长沙矿冶研究院有限责任公司，湖南 长沙 410012, bio={"content":"

张洋龙（2001—），男，安徽天长人，硕士研究生，主要研究方向为矿物加工工程和数据科学。E-mail: 18110775915@163.com

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张洋龙（2001—），男，安徽天长人，硕士研究生，主要研究方向为矿物加工工程和数据科学。E-mail: 18110775915@163.com

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