Article(id=1308544103042872304, tenantId=1146029695717560320, journalId=1268266715432370238, issueId=1308544094092222643, articleNumber=null, orderNo=null, doi=10.3969/j.issn.1006-4613.2026.04.001, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=1783267200000, revisedDateStr=2026-07-06, acceptedDate=null, acceptedDateStr=null, onlineDate=1789910825739, onlineDateStr=2026-09-20, pubDate=null, pubDateStr=null, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1789910825739, onlineIssueDateStr=2026-09-20, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1789910825739, creator=13701087609, updateTime=1789910825739, updator=13701087609, issue=Issue{id=1308544094092222643, tenantId=1146029695717560320, journalId=1268266715432370238, year='2026', volume='', issue='4', pageStart='1', pageEnd='72', issueExtLink='null', onlineDate='null', pubDate='1786291200000', pubDateStr='2026-08-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1789910823605, creator='13701087609', updateTime=1789955282254, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1308730567286415933, tenantId=1146029695717560320, journalId=1268266715432370238, issueId=1308544094092222643, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1308730567286415934, tenantId=1146029695717560320, journalId=1268266715432370238, issueId=1308544094092222643, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=1, endPage=7, ext={EN=ArticleExt(id=1308544103260976113, articleId=1308544103042872304, tenantId=1146029695717560320, journalId=1268266715432370238, language=EN, title=Data Governance and Prediction of Blast Furnace Gas Generation, columnId=1268934126095729605, journalTitle=Angang Technology, columnName=Expert Tribune, runingTitle=null, highlight=null, articleAbstract=

Accurate prediction of blast furnace gas (BFG) generation is crucial for achieving rational utilization of gas and intelligent control of gas systems. Due to measurement instrument errors and production fluctuations, measured values of BFG generation are prone to anomalies. The current approach focuses on the self-identification and self-correction of BFG generation data, without leveraging the correlations among process parameters to conduct data governance on BFG generation. To address the above issues, a blast furnace was selected as the research object to construct a data governance model, which identified and corrected abnormal data from BFG generation measurements by utilizing the correlation between BFG generation and blast volume. Kalman filtering was applied to eliminate noise, and backpropagation neural network (BPNN), long short-term memory network (LSTM) and extreme gradient boosting (XGBoost) prediction models were built for result analysis. The results showed that the prediction accuracy of all models was significantly improved after data governance. The BPNN model demonstrated the optimal prediction performance with a mean absolute error (MAE) of 90.21 m³/min and a mean absolute percentage error (MAPE) of 2.48%, while LSTM and XGBoost models achieved MAE values of 133.92 m³/min and 125.19 m³/min, with corresponding MAPE values of 3.71% and 3.45%, respectively.

, authors=Wenqiang SUN^{1, 2}, Shuhan LIU¹, Zhu YE¹, authorsList=Wenqiang SUN, Shuhan LIU, Zhu YE, 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=1308544103651046388, articleId=1308544103042872304, tenantId=1146029695717560320, journalId=1268266715432370238, language=CN, title=高炉煤气数据治理与产生量预测, columnId=1268934126271890375, journalTitle=鞍钢技术, columnName=专家论坛, runingTitle=null, highlight=null, articleAbstract=

精准预测高炉煤气产生量是实现煤气合理利用和煤气系统智能管控的关键。受测量仪表和生产波动的共同影响，高炉煤气产生量的测量值易出现异常。当前方法聚焦于高炉煤气产生量数据的自辨识与自修正，未利用工艺参数间的关联性对高炉煤气产生量进行数据治理。为了解决上述问题，以高炉为研究对象，构建了数据治理模型，利用高炉煤气产生量和鼓风量间的关联性辨识并修正高炉煤气产生量测量值中的异常数据，利用卡尔曼滤波模型对噪声进行消除，构建反向传播神经网络（BPNN）模型、长短期记忆网络（LSTM）模型和极端梯度提升（XGBoost）模型对预测结果进行分析。结果表明，数据治理后各预测模型的预测精度显著提升，BPNN模型表现出最佳的预测效果，其平均绝对误差（MAE）和平均绝对百分比误差（MAPE）分别为90.21 m³/min和2.48%，LSTM模型和XGBoost模型的MAE分别为133.92 m³/min和125.19 m³/min，MAPE分别为3.71%和3.45%。

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^2．生态环境部生态工业重点实验室，辽宁 沈阳 110819, bio={"content":"

孙文强，博士，教授，2014年毕业于东北大学热能工程专业。E-mail: sunwq@mail.neu.edu.cn

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孙文强，博士，教授，2014年毕业于东北大学热能工程专业。E-mail: sunwq@mail.neu.edu.cn

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