Article(id=1222488503489913261, tenantId=1146029695717560320, journalId=1210938733613449225, issueId=1222488501866713569, articleNumber=null, orderNo=null, doi=10.19666/j.rlfd.202303042, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1679414400000, receivedDateStr=2023-03-22, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1769393571970, onlineDateStr=2026-01-26, pubDate=1690214400000, pubDateStr=2023-07-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1769393571970, onlineIssueDateStr=2026-01-26, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1769393571970, creator=13701087609, updateTime=1769393571970, updator=13701087609, issue=Issue{id=1222488501866713569, tenantId=1146029695717560320, journalId=1210938733613449225, year='2023', volume='52', issue='7', pageStart='1', pageEnd='199', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1769393571583, creator=13701087609, updateTime=1769393973240, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1222490186634743828, tenantId=1146029695717560320, journalId=1210938733613449225, issueId=1222488501866713569, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1222490186634743829, tenantId=1146029695717560320, journalId=1210938733613449225, issueId=1222488501866713569, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=106, endPage=112, ext={EN=ArticleExt(id=1222488506950214073, articleId=1222488503489913261, tenantId=1146029695717560320, journalId=1210938733613449225, language=EN, title=Data-driven method for predicting the wall temperature of heating surface of supercritical boilers, columnId=1222488504991474098, journalTitle=Thermal Power Generation, columnName=Intelligent management technologies for coal-fired power plants, runingTitle=null, highlight=null, articleAbstract=

The overheating of boiler heating surface seriously affects the safe operation of the power plant. It is of great significance for the safety of boiler to predict the tube temperature of heating surface and to take appropriate preventative measures. A data driven-based model for tube temperature prediction is proposed in this study. Firstly, the key variables affecting the tube temperature are selected by grey correlation analysis that affect the wall temperature of the heating surface, and a wall temperature prediction model based on long short term memory (LSTM) neural network is constructed. Then, the correlation feature coefficients under similar historical operating conditions are defined, and the predicted wall temperature obtained by the LSTM neural network is corrected to improve the model's prediction accuracy. Finally, an on-duty supercritical boiler with 600 MW capacity is taken as the case study. Results showed that the relative error of the proposed prediction model is within (−2.5%, 2.5%). The average relative error is 0.40%, and the average tube temperature prediction error is 2.24 ℃. It indicates that the proposed model is helpful for the tube temperature prediction of the boiler under complex operating conditions.

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锅炉受热面管壁超温严重影响电厂的安全运行，对锅炉受热面壁温进行预测，提前做出针对性的运行调整，避免管壁超温，对锅炉的安全运行具有重要意义。提出一种基于数据驱动的锅炉受热面壁温预测模型。首先，采用灰色关联分析选取影响受热面壁温的关键特征变量，构建基于长短时记忆（LSTM）神经网络的壁温预测模型；然后，定义历史相似工况下的关联特征系数，对由LSTM神经网络得到的预测壁温进行修正，提高模型预测精度；最后，以某在役超临界600 MW直流锅炉为研究对象进行分析，结果表明，所提出的锅炉受热面壁温预测模型的相对误差在（−2.5%，2.5%），平均相对误差为0.40%，平均壁温预测误差2.24 ℃。可见该预测模型可实现复杂工况下锅炉受热面壁温的准确预测。

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魏小兵（1975），男，高级工程师，主要研究方向为电厂热工自动控制，LLDLWXB@126.com。

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魏小兵（1975），男，高级工程师，主要研究方向为电厂热工自动控制，LLDLWXB@126.com。

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