A Hybrid Model for Billet Tapping Temperature Prediction and Optimization in Reheating Furnace
To predict and optimize the billet heating process in reheating furnace for rolling mills, this paper proposes a hybrid model that combines data-driven model with traditional mechanism knowledge, abbreviated as HMDM. By examining the heat conduction mechanism, a billet temperature distribution equat...
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| Veröffentlicht in: | IEEE transactions on industrial informatics 2023-08, Vol.19 (8), p.1-10 |
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| creator | Yu, Hong Gong, Jiangnan Wang, Guoyin Chen, Xiaofang |
| description | To predict and optimize the billet heating process in reheating furnace for rolling mills, this paper proposes a hybrid model that combines data-driven model with traditional mechanism knowledge, abbreviated as HMDM. By examining the heat conduction mechanism, a billet temperature distribution equation is established. Then, the billet temperature distribution in each heating zone is calculated and spliced with the corresponding process parameters. The Stacked-AutoEncoder is utilized to extract the features of process parameters, and the Long Short Term Memory model is employed to predict the temperature. Finally, using the previous predictions, the parameters of the subsequent heating stage are optimized and adjusted during the heating process. The experimental results on the real steel plant verify the effectiveness of HMDM. For example, the temperature prediction error has been reduced to less than 4^{\circ }C, and the number of billets with abnormal tapping temperature has been decreased by 42.9\%. |
| doi_str_mv | 10.1109/TII.2022.3221219 |
| format | Article |
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By examining the heat conduction mechanism, a billet temperature distribution equation is established. Then, the billet temperature distribution in each heating zone is calculated and spliced with the corresponding process parameters. The Stacked-AutoEncoder is utilized to extract the features of process parameters, and the Long Short Term Memory model is employed to predict the temperature. Finally, using the previous predictions, the parameters of the subsequent heating stage are optimized and adjusted during the heating process. The experimental results on the real steel plant verify the effectiveness of HMDM. For example, the temperature prediction error has been reduced to less than <inline-formula><tex-math notation="LaTeX">4^{\circ }</tex-math></inline-formula>C, and the number of billets with abnormal tapping temperature has been decreased by <inline-formula><tex-math notation="LaTeX">42.9\%</tex-math></inline-formula>.]]></description><identifier>ISSN: 1551-3203</identifier><identifier>EISSN: 1941-0050</identifier><identifier>DOI: 10.1109/TII.2022.3221219</identifier><identifier>CODEN: ITIICH</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>Billets ; Conduction cooling ; Conduction heating ; Conductive heat transfer ; deep learning ; Error reduction ; Furnaces ; Heating ; Heating furnaces ; Heating systems ; Iron and steel plants ; Mathematical models ; multi-stage prediction ; Optimization ; Predictive models ; process industry ; process optimization ; Process parameters ; Reheating furnace ; Rolling mills ; Temperature distribution</subject><ispartof>IEEE transactions on industrial informatics, 2023-08, Vol.19 (8), p.1-10</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2023</rights><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c291t-4df8de84f85911ba1af5b00be322ccbf43ee7a58ef0d1b5d52f4881039385c3</citedby><cites>FETCH-LOGICAL-c291t-4df8de84f85911ba1af5b00be322ccbf43ee7a58ef0d1b5d52f4881039385c3</cites><orcidid>0000-0002-8521-5232 ; 0000-0002-7188-032X ; 0000-0003-0667-8413</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9944864$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,27924,27925,54758</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/9944864$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Yu, Hong</creatorcontrib><creatorcontrib>Gong, Jiangnan</creatorcontrib><creatorcontrib>Wang, Guoyin</creatorcontrib><creatorcontrib>Chen, Xiaofang</creatorcontrib><title>A Hybrid Model for Billet Tapping Temperature Prediction and Optimization in Reheating Furnace</title><title>IEEE transactions on industrial informatics</title><addtitle>TII</addtitle><description><![CDATA[To predict and optimize the billet heating process in reheating furnace for rolling mills, this paper proposes a hybrid model that combines data-driven model with traditional mechanism knowledge, abbreviated as HMDM. By examining the heat conduction mechanism, a billet temperature distribution equation is established. Then, the billet temperature distribution in each heating zone is calculated and spliced with the corresponding process parameters. The Stacked-AutoEncoder is utilized to extract the features of process parameters, and the Long Short Term Memory model is employed to predict the temperature. Finally, using the previous predictions, the parameters of the subsequent heating stage are optimized and adjusted during the heating process. The experimental results on the real steel plant verify the effectiveness of HMDM. For example, the temperature prediction error has been reduced to less than <inline-formula><tex-math notation="LaTeX">4^{\circ }</tex-math></inline-formula>C, and the number of billets with abnormal tapping temperature has been decreased by <inline-formula><tex-math notation="LaTeX">42.9\%</tex-math></inline-formula>.]]></description><subject>Billets</subject><subject>Conduction cooling</subject><subject>Conduction heating</subject><subject>Conductive heat transfer</subject><subject>deep learning</subject><subject>Error reduction</subject><subject>Furnaces</subject><subject>Heating</subject><subject>Heating furnaces</subject><subject>Heating systems</subject><subject>Iron and steel plants</subject><subject>Mathematical models</subject><subject>multi-stage prediction</subject><subject>Optimization</subject><subject>Predictive models</subject><subject>process industry</subject><subject>process optimization</subject><subject>Process parameters</subject><subject>Reheating furnace</subject><subject>Rolling mills</subject><subject>Temperature distribution</subject><issn>1551-3203</issn><issn>1941-0050</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kEtPAjEUhRujiYjuTdw0cT3YJ7RLJCIkGIzO2qYzvdWSYWbsDAv89RYhru4j59yb8yF0S8mIUqIf8uVyxAhjI84YZVSfoQHVgmaESHKeeilpxhnhl-iq6zaE8AnheoA-pnixL2Jw-KVxUGHfRPwYqgp6nNu2DfUnzmHbQrT9LgJ-jeBC2YemxrZ2eN32YRt-7N8i1PgNviANyTTfxdqWcI0uvK06uDnVIXqfP-WzRbZaPy9n01VWMk37TDivHCjhldSUFpZaLwtCCkhhyrLwggNMrFTgiaOFdJJ5oRRNCbiSJR-i--PVNjbfO-h6s2kO_6vOMMUnVEg5GScVOarK2HRdBG_aGLY27g0l5sDQJIbmwNCcGCbL3dESAOBfrrUQaiz4Lwz7bXE</recordid><startdate>20230801</startdate><enddate>20230801</enddate><creator>Yu, Hong</creator><creator>Gong, Jiangnan</creator><creator>Wang, Guoyin</creator><creator>Chen, Xiaofang</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><orcidid>https://orcid.org/0000-0002-8521-5232</orcidid><orcidid>https://orcid.org/0000-0002-7188-032X</orcidid><orcidid>https://orcid.org/0000-0003-0667-8413</orcidid></search><sort><creationdate>20230801</creationdate><title>A Hybrid Model for Billet Tapping Temperature Prediction and Optimization in Reheating Furnace</title><author>Yu, Hong ; Gong, Jiangnan ; Wang, Guoyin ; Chen, Xiaofang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c291t-4df8de84f85911ba1af5b00be322ccbf43ee7a58ef0d1b5d52f4881039385c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Billets</topic><topic>Conduction cooling</topic><topic>Conduction heating</topic><topic>Conductive heat transfer</topic><topic>deep learning</topic><topic>Error reduction</topic><topic>Furnaces</topic><topic>Heating</topic><topic>Heating furnaces</topic><topic>Heating systems</topic><topic>Iron and steel plants</topic><topic>Mathematical models</topic><topic>multi-stage prediction</topic><topic>Optimization</topic><topic>Predictive models</topic><topic>process industry</topic><topic>process optimization</topic><topic>Process parameters</topic><topic>Reheating furnace</topic><topic>Rolling mills</topic><topic>Temperature distribution</topic><toplevel>online_resources</toplevel><creatorcontrib>Yu, Hong</creatorcontrib><creatorcontrib>Gong, Jiangnan</creatorcontrib><creatorcontrib>Wang, Guoyin</creatorcontrib><creatorcontrib>Chen, Xiaofang</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>IEEE transactions on industrial informatics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Yu, Hong</au><au>Gong, Jiangnan</au><au>Wang, Guoyin</au><au>Chen, Xiaofang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Hybrid Model for Billet Tapping Temperature Prediction and Optimization in Reheating Furnace</atitle><jtitle>IEEE transactions on industrial informatics</jtitle><stitle>TII</stitle><date>2023-08-01</date><risdate>2023</risdate><volume>19</volume><issue>8</issue><spage>1</spage><epage>10</epage><pages>1-10</pages><issn>1551-3203</issn><eissn>1941-0050</eissn><coden>ITIICH</coden><abstract><![CDATA[To predict and optimize the billet heating process in reheating furnace for rolling mills, this paper proposes a hybrid model that combines data-driven model with traditional mechanism knowledge, abbreviated as HMDM. By examining the heat conduction mechanism, a billet temperature distribution equation is established. Then, the billet temperature distribution in each heating zone is calculated and spliced with the corresponding process parameters. The Stacked-AutoEncoder is utilized to extract the features of process parameters, and the Long Short Term Memory model is employed to predict the temperature. Finally, using the previous predictions, the parameters of the subsequent heating stage are optimized and adjusted during the heating process. The experimental results on the real steel plant verify the effectiveness of HMDM. For example, the temperature prediction error has been reduced to less than <inline-formula><tex-math notation="LaTeX">4^{\circ }</tex-math></inline-formula>C, and the number of billets with abnormal tapping temperature has been decreased by <inline-formula><tex-math notation="LaTeX">42.9\%</tex-math></inline-formula>.]]></abstract><cop>Piscataway</cop><pub>IEEE</pub><doi>10.1109/TII.2022.3221219</doi><tpages>10</tpages><orcidid>https://orcid.org/0000-0002-8521-5232</orcidid><orcidid>https://orcid.org/0000-0002-7188-032X</orcidid><orcidid>https://orcid.org/0000-0003-0667-8413</orcidid></addata></record> |
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| subjects | Billets Conduction cooling Conduction heating Conductive heat transfer deep learning Error reduction Furnaces Heating Heating furnaces Heating systems Iron and steel plants Mathematical models multi-stage prediction Optimization Predictive models process industry process optimization Process parameters Reheating furnace Rolling mills Temperature distribution |
| title | A Hybrid Model for Billet Tapping Temperature Prediction and Optimization in Reheating Furnace |
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