Article(id=1221425788449116431, tenantId=1146029695717560320, journalId=1210938733613449225, issueId=1221425781750808678, articleNumber=null, orderNo=null, doi=10.19666/j.rlfd.202205088, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1652025600000, receivedDateStr=2022-05-09, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1769140200960, onlineDateStr=2026-01-23, pubDate=1674576000000, pubDateStr=2023-01-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1769140200960, onlineIssueDateStr=2026-01-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1769140200960, creator=13701087609, updateTime=1769140200960, updator=13701087609, issue=Issue{id=1221425781750808678, tenantId=1146029695717560320, journalId=1210938733613449225, year='2023', volume='52', issue='1', pageStart='1', pageEnd='182', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1769140199363, creator=13701087609, updateTime=1769145231708, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1221446888994292213, tenantId=1146029695717560320, journalId=1210938733613449225, issueId=1221425781750808678, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1221446888994292214, tenantId=1146029695717560320, journalId=1210938733613449225, issueId=1221425781750808678, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=74, endPage=82, ext={EN=ArticleExt(id=1221425788801437982, articleId=1221425788449116431, tenantId=1146029695717560320, journalId=1210938733613449225, language=EN, title=Optimization of main steam temperature deviation on both sides of supercritical 600 MW opposed firing boiler based on hydrodynamic model of flow network method, columnId=1211002405299294959, journalTitle=Thermal Power Generation, columnName=Thermal energy science research, runingTitle=null, highlight=null, articleAbstract=

By taking a supercritical 600 MW opposed firing boiler as the research object, the temperature deviation law of main steam on both sides is analyzed and verified through hydrodynamic modeling, and a technical scheme is put forward to optimize the temperature deviation of main steam from the perspective of water side. After adopting this steam temperature optimization scheme, the main steam temperature deviations on both sides of the boiler reduced by 44.6%, 95.8% and 28.0% respectively under 50%, 75% and 100% BRL conditions. This scheme can realize safe and economical operation of the unit, and has good guidance and reference value for the same type of boiler.

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以某超临界600 MW机组对冲燃烧锅炉为研究对象，通过对其进行水动力建模，分析验证了主蒸汽两侧温度偏差规律，并从锅炉水侧提出优化主蒸汽温度偏差的技术方案。根据提出的蒸汽温度优化方案调整后，50%、75%和100% BRL负荷下锅炉出口两侧主蒸汽温度偏差分别缩小了44.6%、95.8%和28.0%。该方案可实现机组的安全、经济运行，并为同类型机组主蒸汽温度偏差治理提供理论依据与改造参考。

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李松山（1983），男，工程师，主要研究方向为锅炉燃烧、主辅设备检修与维护，563525031@qq.com。

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李松山（1983），男，工程师，主要研究方向为锅炉燃烧、主辅设备检修与维护，563525031@qq.com。

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