Article(id=1221497398967325521, tenantId=1146029695717560320, journalId=1210938733613449225, issueId=1221497393514730153, articleNumber=null, orderNo=null, doi=10.19666/j.rlfd.202208168, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1659801600000, receivedDateStr=2022-08-07, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1769157274238, onlineDateStr=2026-01-23, pubDate=1682352000000, pubDateStr=2023-04-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1769157274238, onlineIssueDateStr=2026-01-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1769157274238, creator=13701087609, updateTime=1769157274238, updator=13701087609, issue=Issue{id=1221497393514730153, tenantId=1146029695717560320, journalId=1210938733613449225, year='2023', volume='52', issue='4', 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=1769157272938, creator=13701087609, updateTime=1769157397933, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1221497917878223060, tenantId=1146029695717560320, journalId=1210938733613449225, issueId=1221497393514730153, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1221497917878223061, tenantId=1146029695717560320, journalId=1210938733613449225, issueId=1221497393514730153, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=82, endPage=89, ext={EN=ArticleExt(id=1221497399235760989, articleId=1221497398967325521, tenantId=1146029695717560320, journalId=1210938733613449225, language=EN, title=Performance analysis and working fluid selection of organic Rankine cycle system with variable temperature heat source based on NSGA-II algorithm, columnId=1221467202000175547, journalTitle=Thermal Power Generation, columnName=Thermal energy and science research, runingTitle=null, highlight=null, articleAbstract=

A mathematical model of subcritical organic Rankine cycle (ORC) system is established for the flue gas waste heat of 120~150 ℃. Firstly, the thermal performance and economic performance of the system are analyzed at different heat source temperatures by taking R245fa as an example. Then, a multi-objective optimization study is conducted for six pure working fluids based on NSGA-Ⅱ algorithm. At last, working fluid selection and performance analysis of the ORC system with various heat source temperatures are carried out by TOPSIS method and gray correlation analysis. The results show that, in the temperature range of this study, the increase of superheat degree and evaporator pinch point temperature difference is not conducive to improve the system performance. The optimal working fluids are varied at different heat source temperatures, when the heat source temperature is 120 ℃, R601 has the best thermal performance, R245fa has the best economic performance and R1233zd has the best overall performance. The increase of heat source temperature is beneficial to improve the economic performance of the system. The optimal evaporation temperature of each working fluid increases with the heat source temperature. The gray correlation analysis indicates that the comprehensive performance of all six working fluids improves with the heat source temperature.

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针对120~150 ℃烟气余热，建立亚临界有机朗肯循环系统的数学模型。首先以R245fa为例，在不同热源温度下对系统进行了热力性能和经济性能分析；其次选取6种纯工质，基于NSGA-Ⅱ算法对系统进行了多目标优化研究；最后通过TOPSIS法和灰色关联分析对不同热源温度下的系统进行了工质选择和性能分析。结果表明：在研究温度范围内，过热度和蒸发器夹点温差的升高均不利于提升系统性能；不同热源温度下的最佳工质不同，热源温度为120 ℃时，R601的热力性能最优，R245fa的经济性最优，R1233zd的综合性能最优；热源温度的升高有利于提升系统的经济性能，各工质的最佳蒸发温度随着热源温度的升高而升高。灰色关联分析表明，6种工质的综合性能均随着热源温度升高有所提升。

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马新灵（1977），女，博士，副教授，主要研究方向为低品位热能利用，xinlingma@zzu.edu.cn。

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马新灵（1977），女，博士，副教授，主要研究方向为低品位热能利用，xinlingma@zzu.edu.cn。

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