Article(id=1319243334414095319, tenantId=1146029695717560320, journalId=1185652524569653253, issueId=1319243315246125889, articleNumber=null, orderNo=null, doi=10.13647/j.cnki.btgkj.2021.01.004, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1602604800000, receivedDateStr=2020-10-14, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1792461721280, onlineDateStr=2026-10-20, pubDate=1614182400000, pubDateStr=2021-02-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1792461721280, onlineIssueDateStr=2026-10-20, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1792461721280, creator=13701087609, updateTime=1792461721280, updator=13701087609, issue=Issue{id=1319243315246125889, tenantId=1146029695717560320, journalId=1185652524569653253, year='2021', volume='47', issue='1', pageStart='1', pageEnd='98', issueExtLink='null', onlineDate='null', pubDate='1614182400000', pubDateStr='2021-02-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1792461716710, creator='13701087609', updateTime=1792735308253, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1320390841416397726, tenantId=1146029695717560320, journalId=1185652524569653253, issueId=1319243315246125889, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1320390841416397727, tenantId=1146029695717560320, journalId=1185652524569653253, issueId=1319243315246125889, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=13, endPage=15, ext={EN=ArticleExt(id=1319243336226034648, articleId=1319243334414095319, tenantId=1146029695717560320, journalId=1185652524569653253, language=EN, title=Optimization Method of Coal Blending for Coking Based on Intelligence Algorithm, columnId=1187340472918880861, journalTitle=Science & Technology of Baotou Steel, columnName=Production Practice and Management, runingTitle=null, highlight=null, articleAbstract=

In the production process of coking, blending coal as the main raw material determines not only quality of coke, but also cost and profit of coking enterprises. At present, the adjustment for proportion of single coal with experiences is mainly applied for coal blending in coking enterprises, which lacks scientific basis, so it is difficult to achieve high cost-effective coal blending scheme to optimize the coal blending structure for coking. In this paper, it is introduced the purposes of reducing cost of blending coal, improving coke quality, reducing cost of metallurgical coke and improving benefit of enterprises are achieved by using artificial neural network to predict coke quality and using simulated annealing algorithm to optimize proportion of single coal with coke quality as the constraint condition and cost price of blending coal as the optimization objective.

, authors=Jian-wen Lu¹, Li-jun Fu², Xin Jiang², Xiao-zong Jia³, authorsList=Jian-wen Lu, Li-jun Fu, Xin Jiang, Xiao-zong Jia, 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=1319243336498664409, articleId=1319243334414095319, tenantId=1146029695717560320, journalId=1185652524569653253, language=CN, title=基于智能算法的炼焦配煤优化方法, columnId=1185882039552193444, journalTitle=包钢科技, columnName=生产实践与管理, runingTitle=null, highlight=null, articleAbstract=

炼焦生产过程中，配合煤作为主要原料，不仅决定着焦炭的质量，而且决定着炼焦企业的成本与利润。目前炼焦企业主要采用人工经验调整单种煤配比的方式进行配煤，缺乏科学的依据，难以实现高性价比的配煤方案，以优化炼焦配煤结构。文章以焦炭质量为约束条件，以配合煤成本价格为优化目标，采用人工神经网络预测焦炭质量，模拟退火算法优化单种煤配比。达到了降低配合煤成本、提高焦炭质量、降低冶金焦炭成本、提升企业效益的目的。

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芦建文（1969-），男，山西省朔州市人，硕士，高级工程师，现从事计算机与自动化工作。

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芦建文（1969-），男，山西省朔州市人，硕士，高级工程师，现从事计算机与自动化工作。

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