Article(id=1314908286453903922, tenantId=1146029695717560320, journalId=1235980550691926019, issueId=1314908074188562588, articleNumber=null, orderNo=null, doi=10.3969/j.issn.0253-6099.2026.04.032, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1769961600000, receivedDateStr=2026-02-02, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1791428165304, onlineDateStr=2026-10-08, pubDate=null, pubDateStr=null, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1791428165304, onlineIssueDateStr=2026-10-08, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1791428165304, creator=13701087609, updateTime=1791428165304, updator=13701087609, issue=Issue{id=1314908074188562588, tenantId=1146029695717560320, journalId=1235980550691926019, year='2026', volume='46', issue='4', pageStart='1', pageEnd='266', issueExtLink='null', onlineDate='null', pubDate='1785513600000', pubDateStr='2026-08-01', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1791428114697, creator='13701087609', updateTime=1791430419337, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1314917740654191268, tenantId=1146029695717560320, journalId=1235980550691926019, issueId=1314908074188562588, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1314917740654191269, tenantId=1146029695717560320, journalId=1235980550691926019, issueId=1314908074188562588, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=210, endPage=214, ext={EN=ArticleExt(id=1314908286684590643, articleId=1314908286453903922, tenantId=1146029695717560320, journalId=1235980550691926019, language=EN, title=Optimization Model for Roasting Parameters of High-Copper Low-Sulfur Concentrate Based on Response Surface Methodology, columnId=1236276106727321817, journalTitle=Mining and Metallurgical Engineering, columnName=METALLURGY, runingTitle=null, highlight=null, articleAbstract=

By adopting central composite design (CCD) of response surface methodology (RSM), a binary quadratic regression prediction model was established, with roasting temperature and oxygen concentration as influencing factors and copper leaching rate of calcine as the response value. It is shown that the prediction model presents a P value of 0.018 2, adjusted determination coefficient of 0.810 1, and signal-to-noise ratio of 7.323 6, exhibiting favorable reliability and prediction accuracy. Roasting temperature has an extremely significant effect on copper leaching rate, while oxygen concentration brings insignificant effect, and their interaction is weak. The parameters were optimized via Design-Expert software, and the optimal roasting process parameters are obtained as follows:roasting temperature of 750 ℃ and oxygen concentration of 30%. Under such optimized parameters, the predicted copper leaching rate of calcine is 97.47%, while the copper leaching rate of calcine in industrial applications reaches 98.17%.

, authors=Xing LUO^{1, 2}, Fucheng ZHAO^{1, 2}, Kangle JIA^{1, 2}, Haibao FAN^{1, 2}, Jun PU^{1, 2}, authorsList=Xing LUO, Fucheng ZHAO, Kangle JIA, Haibao FAN, Jun PU, authorCompany=null, correspAuthors=null, 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=1314908287099826740, articleId=1314908286453903922, tenantId=1146029695717560320, journalId=1235980550691926019, language=CN, title=基于响应曲面法的高铜低硫精矿焙烧参数优化模型, columnId=1236276107322913000, journalTitle=矿冶工程杂志, columnName=冶金, runingTitle=null, highlight=null, articleAbstract=

采用响应曲面法（RSM）的中心组合设计（CCD），以焙烧温度和氧气浓度为影响因素、焙砂铜浸出率为响应值，构建了二元二次回归预测模型。结果表明，预测模型P值为0.0182、调整决定系数为0.8101、信噪比为7.3236，具备良好的可靠性与预测精度。焙烧温度对铜浸出率影响极显著，氧气浓度影响不显著，二者交互作用较弱。通过Design-Expert软件优化参数，得到优化的焙烧工艺参数为：焙烧温度750 ℃、氧气浓度30%，此条件下，铜浸出率预测值为97.47%。在优化参数指导下，工业应用焙砂铜浸出率为98.17%。

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罗星（1987—），男，江西萍乡人，硕士，高级工程师，主要从事焙烧和制酸生产管理。E-mail: 570492050@qq.com

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罗星（1987—），男，江西萍乡人，硕士，高级工程师，主要从事焙烧和制酸生产管理。E-mail: 570492050@qq.com

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