Article(id=1221467208572653925, tenantId=1146029695717560320, journalId=1210938733613449225, issueId=1221467200582500783, articleNumber=null, orderNo=null, doi=10.19666/j.rlfd.202212181, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1670169600000, receivedDateStr=2022-12-05, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1769150076287, onlineDateStr=2026-01-23, pubDate=1684944000000, pubDateStr=2023-05-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1769150076287, onlineIssueDateStr=2026-01-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1769150076287, creator=13701087609, updateTime=1769150076287, updator=13701087609, issue=Issue{id=1221467200582500783, tenantId=1146029695717560320, journalId=1210938733613449225, year='2023', volume='52', issue='5', pageStart='1', pageEnd='166', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1769150074382, creator=13701087609, updateTime=1769157444393, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1221498112716226774, tenantId=1146029695717560320, journalId=1210938733613449225, issueId=1221467200582500783, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1221498112716226775, tenantId=1146029695717560320, journalId=1210938733613449225, issueId=1221467200582500783, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=127, endPage=135, ext={EN=ArticleExt(id=1221467208862060926, articleId=1221467208572653925, tenantId=1146029695717560320, journalId=1210938733613449225, language=EN, title=Prediction and optimization of back pressure of direct air-cooled unit based on data mining, columnId=1221467202000175547, journalTitle=Thermal Power Generation, columnName=Thermal energy and science research, runingTitle=null, highlight=null, articleAbstract=

In order to realize the cold end optimization of direct air-cooled units, a cold end operation optimization method of air-cooled units is proposed based on the historical operation data of units and combined with data mining and deep learning algorithm. Firstly, the obtained historical operation data are screened in steady state and divided into working conditions. Combined with the Gaussian mixture model algorithm, the back pressure reference interval of the unit under multiple working conditions is determined. Then, the Spearman coefficient method is used to select the characteristic variables, and the back pressure prediction model of the direct air cooling unit is constructed in combination with the gated circulation unit. The back pressure optimization suggestions and early warning information are given by comparing the back pressure reference interval with the back pressure prediction value. Finally, the method is applied to a subcritical 300 MW air-cooled condensing steam unit. The results show that the back pressure optimization method proposed in this paper can give effective back pressure early warning information and realize optimal operation of cold end of the air-cooled unit.

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为实现直接空冷机组冷端优化，以机组历史运行数据为基础结合数据挖掘与深度学习算法，提出了一种直接空冷机组冷端运行优化方法。首先，对获取的历史运行数据进行稳态筛选、工况划分，结合高斯混合模型算法确定机组多元工况下背压基准区间；然后，使用Spearman系数法选取特征变量，结合门控循环单元神经网络构建直接空冷机组背压预测模型，对比背压基准区间与背压预测值给出背压的优化建议和预警信息；最后，将该方法应用于某亚临界300 MW空冷凝汽式机组。研究结果表明：提出的背压优化方法能够给出有效的背压预警信息，实现空冷机组冷端优化运行。

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刘宇航（1998），男，硕士研究生，主要研究方向为火电机组状态监测与故障诊断，liuyuhang0101@163.com。

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刘宇航（1998），男，硕士研究生，主要研究方向为火电机组状态监测与故障诊断，liuyuhang0101@163.com。

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