Article(id=1308544153177391959, tenantId=1146029695717560320, journalId=1268266715432370238, issueId=1308544152535658686, articleNumber=null, orderNo=null, doi=10.3969/j.issn.1006-4613.2026.01.003, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=1750694400000, revisedDateStr=2025-06-24, acceptedDate=null, acceptedDateStr=null, onlineDate=1789910837692, onlineDateStr=2026-09-20, pubDate=null, pubDateStr=null, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1789910837692, onlineIssueDateStr=2026-09-20, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1789910837692, creator=13701087609, updateTime=1789910837692, updator=13701087609, issue=Issue{id=1308544152535658686, tenantId=1146029695717560320, journalId=1268266715432370238, year='2026', volume='', issue='1', pageStart='1', pageEnd='71', issueExtLink='null', onlineDate='null', pubDate='1770652800000', pubDateStr='2026-02-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1789910837537, creator='13701087609', updateTime=1789955219063, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1308730302504198699, tenantId=1146029695717560320, journalId=1268266715432370238, issueId=1308544152535658686, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1308730302508393004, tenantId=1146029695717560320, journalId=1268266715432370238, issueId=1308544152535658686, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=19, endPage=31, ext={EN=ArticleExt(id=1308544155463287640, articleId=1308544153177391959, tenantId=1146029695717560320, journalId=1268266715432370238, language=EN, title=Intelligent Empowerment of Green Steel Manufacturing: Steelmaking Process, columnId=1308544153160609987, journalTitle=Angang Technology, columnName=Green and Low-carbon Column (Ⅰ), runingTitle=null, highlight=null, articleAbstract=

With the rapid development of machine learning, deep learning, and large model technologies, the intelligent empowerment of green steel manufacturing has become a research hot spot. The deep integration of digital and intelligent technologies with steelmaking process industrial scenarios was systematically explored, the current applications in key processes such as converters, electric arc furnaces, refining, and continuous casting were analyzed, the cutting-edge technologies such as multimodal perception and early warning, resource collaborative optimization, digital twins, and full-process causal reasoning were proposed to achieve green and low-carbon production as well as intelligent, efficient collaboration in steelmaking process. Finally, the challenges faced by the integrated and in-depth application of artificial intelligence in steelmaking-continuous casting section, potential strategies for large models to empower green manufacturing, and future prospects were discussed, which provided transformation ideas and theoretical support for intelligent empowerment of green steel manufacturing.

, authors=Nanfu ZONG1, Chenglin ZHAO2, Jixiang JIA3, Hongjin YUE2, Jian GAO2, Lifeng ZHANG2, Xiaolei ZHU2, Tao JING4, authorsList=Nanfu ZONG, Chenglin ZHAO, Jixiang JIA, Hongjin YUE, Jian GAO, Lifeng ZHANG, Xiaolei ZHU, Tao JING, 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=1308544156037907289, articleId=1308544153177391959, tenantId=1146029695717560320, journalId=1268266715432370238, language=CN, title=智能化赋能钢铁绿色制造:炼钢流程, columnId=1308544154938994885, journalTitle=鞍钢技术, columnName=绿色低碳, runingTitle=null, highlight=null, articleAbstract=

随着机器学习、深度学习和大模型技术的快速发展,智能化赋能钢铁绿色制造已成为研究热点。系统探讨借助数智化技术与炼钢流程工业场景深度融合,重点分析其在转炉、电炉、精炼和连铸等核心工序的应用现状,提出采用多模态感知预警,资源协同优化,数字孪生和全流程因果推理等前沿技术,实现炼钢流程的绿色低碳生产和智能高效协同的攻关方向。最后,探讨人工智能在炼钢-连铸区段中集成深度应用面临的挑战、大模型赋能绿色制造的潜在策略和未来展望,为智能化赋能钢铁绿色制造提供转型思路和理论支撑。

, authors=宗男夫1, 赵成林2, 贾吉祥3, 岳洪进2, 高鉴2, 张立锋2, 朱晓雷2, 荆涛4, authorsList=宗男夫, 赵成林, 贾吉祥, 岳洪进, 高鉴, 张立锋, 朱晓雷, 荆涛, authorCompany=null, correspAuthors=null, authorNote=

宗男夫,博士后,高级工程师,2019年毕业于钢铁研究总院钢铁冶金专业。E-mail:

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宗男夫,博士后,高级工程师,2019年毕业于钢铁研究总院钢铁冶金专业。E-mail:

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宗男夫,博士后,高级工程师,2019年毕业于钢铁研究总院钢铁冶金专业。E-mail:

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智能化赋能钢铁绿色制造:炼钢流程
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宗男夫 1 , 赵成林 2 , 贾吉祥 3 , 岳洪进 2 , 高鉴 2 , 张立锋 2 , 朱晓雷 2 , 荆涛 4
鞍钢技术 | 绿色低碳 2026,(1): 19-31
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鞍钢技术 |绿色低碳 2026 , (1) : 19 -31
智能化赋能钢铁绿色制造:炼钢流程
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宗男夫1 , 赵成林2, 贾吉祥3, 岳洪进2, 高鉴2, 张立锋2, 朱晓雷2, 荆涛4
作者信息
  • 1.鞍钢集团本钢技术中心,辽宁 本溪 117000
  • 2.鞍钢集团凌钢技术中心,辽宁 朝阳 122500
  • 3.鞍钢集团钢铁研究院,辽宁 鞍山 114009
  • 4.清华大学材料学院先进成形制造教育部重点实验室,北京 100084
作者简介:

宗男夫,博士后,高级工程师,2019年毕业于钢铁研究总院钢铁冶金专业。E-mail:

Intelligent Empowerment of Green Steel Manufacturing: Steelmaking Process
Nanfu ZONG1 , Chenglin ZHAO2, Jixiang JIA3, Hongjin YUE2, Jian GAO2, Lifeng ZHANG2, Xiaolei ZHU2, Tao JING4
Affiliations
  • 1.Bensteel Technology Center of Ansteel Group Co., Ltd., Benxi 117000, Liaoning, China
  • 2.Lingsteel Technology Center of Ansteel Group Co., Ltd., Chaoyang 122500, Liaoning, China
  • 3.Ansteel Iron & Steel Research Institutes, Anshan 114009, Liaoning, China
  • 4.Key Laboratory for Advanced Materials Processing Technology, Ministry of Education, School of Materials, Tsinghua University, Beijing 100084, China
doi: 10.3969/j.issn.1006-4613.2026.01.003
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随着机器学习、深度学习和大模型技术的快速发展,智能化赋能钢铁绿色制造已成为研究热点。系统探讨借助数智化技术与炼钢流程工业场景深度融合,重点分析其在转炉、电炉、精炼和连铸等核心工序的应用现状,提出采用多模态感知预警,资源协同优化,数字孪生和全流程因果推理等前沿技术,实现炼钢流程的绿色低碳生产和智能高效协同的攻关方向。最后,探讨人工智能在炼钢-连铸区段中集成深度应用面临的挑战、大模型赋能绿色制造的潜在策略和未来展望,为智能化赋能钢铁绿色制造提供转型思路和理论支撑。

绿色制造  /  炼钢流程  /  大模型  /  动态决策  /  数字孪生

With the rapid development of machine learning, deep learning, and large model technologies, the intelligent empowerment of green steel manufacturing has become a research hot spot. The deep integration of digital and intelligent technologies with steelmaking process industrial scenarios was systematically explored, the current applications in key processes such as converters, electric arc furnaces, refining, and continuous casting were analyzed, the cutting-edge technologies such as multimodal perception and early warning, resource collaborative optimization, digital twins, and full-process causal reasoning were proposed to achieve green and low-carbon production as well as intelligent, efficient collaboration in steelmaking process. Finally, the challenges faced by the integrated and in-depth application of artificial intelligence in steelmaking-continuous casting section, potential strategies for large models to empower green manufacturing, and future prospects were discussed, which provided transformation ideas and theoretical support for intelligent empowerment of green steel manufacturing.

green manufacturing  /  steelmaking process  /  large model  /  autonomous decision  /  digital twins
宗男夫, 赵成林, 贾吉祥, 岳洪进, 高鉴, 张立锋, 朱晓雷, 荆涛. 智能化赋能钢铁绿色制造:炼钢流程. 鞍钢技术, 2026 , (1) : 19 -31 . DOI: 10.3969/j.issn.1006-4613.2026.01.003
Nanfu ZONG, Chenglin ZHAO, Jixiang JIA, Hongjin YUE, Jian GAO, Lifeng ZHANG, Xiaolei ZHU, Tao JING. Intelligent Empowerment of Green Steel Manufacturing: Steelmaking Process[J]. Angang Technology, 2026 , (1) : 19 -31 . DOI: 10.3969/j.issn.1006-4613.2026.01.003
  • 国家自然科学基金面上项目(52074162)
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doi: 10.3969/j.issn.1006-4613.2026.01.003
  • 首发时间:2026-09-20
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出版历史
  • 修回日期:2025-06-24
基金
国家自然科学基金面上项目(52074162)
作者信息
    1.鞍钢集团本钢技术中心,辽宁 本溪 117000
    2.鞍钢集团凌钢技术中心,辽宁 朝阳 122500
    3.鞍钢集团钢铁研究院,辽宁 鞍山 114009
    4.清华大学材料学院先进成形制造教育部重点实验室,北京 100084
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https://castjournals.cast.org.cn/joweb/agjs/CN/10.3969/j.issn.1006-4613.2026.01.003
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2种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)
属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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