Fuzzy Classifier Design for Development Tendency of Hot Metal Silicon Content in Blast Furnace
Since the hot metal silicon content simultaneously reflects the product quality and the thermal state of the blast furnace, accurately predicting the development tendency of hot metal silicon content has the immensely guiding role for blast furnace operators. This paper focuses on fuzzy classifier d...
Gespeichert in:
| Veröffentlicht in: | IEEE transactions on industrial informatics 2018-03, Vol.14 (3), p.1115-1123 |
|---|---|
| Hauptverfasser: | , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 1123 |
|---|---|
| container_issue | 3 |
| container_start_page | 1115 |
| container_title | IEEE transactions on industrial informatics |
| container_volume | 14 |
| creator | Li, Junpeng Hua, Changchun Yang, Yana Guan, Xinping |
| description | Since the hot metal silicon content simultaneously reflects the product quality and the thermal state of the blast furnace, accurately predicting the development tendency of hot metal silicon content has the immensely guiding role for blast furnace operators. This paper focuses on fuzzy classifier design for the development tendency of hot metal silicon content based on blast furnace operation data. The cross characteristic of binary classification problem was found via embedding high-dimensional blast furnace data into a two-dimensional space. Then, presented a nonparallel hyperplanes based fuzzy classifier, which conquered the cross classification still holding the interpretability advantage as fuzzy classifier. The proposed method was tested on No.2 blast furnace of Liuzhou Steel in China, that demonstrated the excellent performance compared with some other classifier algorithms. |
| doi_str_mv | 10.1109/TII.2017.2770177 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_RIE</sourceid><recordid>TN_cdi_crossref_primary_10_1109_TII_2017_2770177</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>8097020</ieee_id><sourcerecordid>2010545780</sourcerecordid><originalsourceid>FETCH-LOGICAL-c291t-95cc6faf56dc4fdad8e32cd1cddf0ae0e3035490da5d0e2707427a6706ca70a43</originalsourceid><addsrcrecordid>eNo9kMFLwzAUxoMoOKd3wUvAc-dL0zTrUatzg4kH59USkhfJ6JLZdML215uy4el9h9_38fgRcstgwhhUD6vFYpIDk5NcynTkGRmxqmAZgIDzlIVgGc-BX5KrGNcAXAKvRuRrtjsc9rRuVYzOOuzoM0b37akNQ_zFNmw36Hu6Qm_Q6z0Nls5DT9-wVy39cK3TwdM6-H6gnKdPaaqns13nlcZrcmFVG_HmdMfkc_ayqufZ8v11UT8uM51XrM8qoXVplRWl0YU1ykyR59owbYwFhYAcuCgqMEoYwFyCLHKpSgmlVhJUwcfk_ri77cLPDmPfrMPwQRubJAVEIeQUEgVHSnchxg5ts-3cRnX7hkEzWGySxaEgm5PFVLk7Vhwi_uNTqCQkmX8p3W4a</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2010545780</pqid></control><display><type>article</type><title>Fuzzy Classifier Design for Development Tendency of Hot Metal Silicon Content in Blast Furnace</title><source>IEEE Electronic Library (IEL)</source><creator>Li, Junpeng ; Hua, Changchun ; Yang, Yana ; Guan, Xinping</creator><creatorcontrib>Li, Junpeng ; Hua, Changchun ; Yang, Yana ; Guan, Xinping</creatorcontrib><description>Since the hot metal silicon content simultaneously reflects the product quality and the thermal state of the blast furnace, accurately predicting the development tendency of hot metal silicon content has the immensely guiding role for blast furnace operators. This paper focuses on fuzzy classifier design for the development tendency of hot metal silicon content based on blast furnace operation data. The cross characteristic of binary classification problem was found via embedding high-dimensional blast furnace data into a two-dimensional space. Then, presented a nonparallel hyperplanes based fuzzy classifier, which conquered the cross classification still holding the interpretability advantage as fuzzy classifier. The proposed method was tested on No.2 blast furnace of Liuzhou Steel in China, that demonstrated the excellent performance compared with some other classifier algorithms.</description><identifier>ISSN: 1551-3203</identifier><identifier>EISSN: 1941-0050</identifier><identifier>DOI: 10.1109/TII.2017.2770177</identifier><identifier>CODEN: ITIICH</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>Blast furnace ; Blast furnace practice ; Blast furnaces ; Classification ; Classifiers ; data visualization ; fuzzy classifier ; Hot blast ; hot metal silicon content ; hyperplane ; Hyperplanes ; Metals ; Predator prey systems ; Prediction algorithms ; Predictive models ; Repair & maintenance ; Silicon ; Steel industry ; Support vector machines</subject><ispartof>IEEE transactions on industrial informatics, 2018-03, Vol.14 (3), p.1115-1123</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2018</rights><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c291t-95cc6faf56dc4fdad8e32cd1cddf0ae0e3035490da5d0e2707427a6706ca70a43</citedby><cites>FETCH-LOGICAL-c291t-95cc6faf56dc4fdad8e32cd1cddf0ae0e3035490da5d0e2707427a6706ca70a43</cites><orcidid>0000-0001-6311-2112 ; 0000-0003-4028-2703</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/8097020$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,792,27901,27902,54733</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/8097020$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Li, Junpeng</creatorcontrib><creatorcontrib>Hua, Changchun</creatorcontrib><creatorcontrib>Yang, Yana</creatorcontrib><creatorcontrib>Guan, Xinping</creatorcontrib><title>Fuzzy Classifier Design for Development Tendency of Hot Metal Silicon Content in Blast Furnace</title><title>IEEE transactions on industrial informatics</title><addtitle>TII</addtitle><description>Since the hot metal silicon content simultaneously reflects the product quality and the thermal state of the blast furnace, accurately predicting the development tendency of hot metal silicon content has the immensely guiding role for blast furnace operators. This paper focuses on fuzzy classifier design for the development tendency of hot metal silicon content based on blast furnace operation data. The cross characteristic of binary classification problem was found via embedding high-dimensional blast furnace data into a two-dimensional space. Then, presented a nonparallel hyperplanes based fuzzy classifier, which conquered the cross classification still holding the interpretability advantage as fuzzy classifier. The proposed method was tested on No.2 blast furnace of Liuzhou Steel in China, that demonstrated the excellent performance compared with some other classifier algorithms.</description><subject>Blast furnace</subject><subject>Blast furnace practice</subject><subject>Blast furnaces</subject><subject>Classification</subject><subject>Classifiers</subject><subject>data visualization</subject><subject>fuzzy classifier</subject><subject>Hot blast</subject><subject>hot metal silicon content</subject><subject>hyperplane</subject><subject>Hyperplanes</subject><subject>Metals</subject><subject>Predator prey systems</subject><subject>Prediction algorithms</subject><subject>Predictive models</subject><subject>Repair & maintenance</subject><subject>Silicon</subject><subject>Steel industry</subject><subject>Support vector machines</subject><issn>1551-3203</issn><issn>1941-0050</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kMFLwzAUxoMoOKd3wUvAc-dL0zTrUatzg4kH59USkhfJ6JLZdML215uy4el9h9_38fgRcstgwhhUD6vFYpIDk5NcynTkGRmxqmAZgIDzlIVgGc-BX5KrGNcAXAKvRuRrtjsc9rRuVYzOOuzoM0b37akNQ_zFNmw36Hu6Qm_Q6z0Nls5DT9-wVy39cK3TwdM6-H6gnKdPaaqns13nlcZrcmFVG_HmdMfkc_ayqufZ8v11UT8uM51XrM8qoXVplRWl0YU1ykyR59owbYwFhYAcuCgqMEoYwFyCLHKpSgmlVhJUwcfk_ri77cLPDmPfrMPwQRubJAVEIeQUEgVHSnchxg5ts-3cRnX7hkEzWGySxaEgm5PFVLk7Vhwi_uNTqCQkmX8p3W4a</recordid><startdate>20180301</startdate><enddate>20180301</enddate><creator>Li, Junpeng</creator><creator>Hua, Changchun</creator><creator>Yang, Yana</creator><creator>Guan, Xinping</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/0000-0001-6311-2112</orcidid><orcidid>https://orcid.org/0000-0003-4028-2703</orcidid></search><sort><creationdate>20180301</creationdate><title>Fuzzy Classifier Design for Development Tendency of Hot Metal Silicon Content in Blast Furnace</title><author>Li, Junpeng ; Hua, Changchun ; Yang, Yana ; Guan, Xinping</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c291t-95cc6faf56dc4fdad8e32cd1cddf0ae0e3035490da5d0e2707427a6706ca70a43</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Blast furnace</topic><topic>Blast furnace practice</topic><topic>Blast furnaces</topic><topic>Classification</topic><topic>Classifiers</topic><topic>data visualization</topic><topic>fuzzy classifier</topic><topic>Hot blast</topic><topic>hot metal silicon content</topic><topic>hyperplane</topic><topic>Hyperplanes</topic><topic>Metals</topic><topic>Predator prey systems</topic><topic>Prediction algorithms</topic><topic>Predictive models</topic><topic>Repair & maintenance</topic><topic>Silicon</topic><topic>Steel industry</topic><topic>Support vector machines</topic><toplevel>online_resources</toplevel><creatorcontrib>Li, Junpeng</creatorcontrib><creatorcontrib>Hua, Changchun</creatorcontrib><creatorcontrib>Yang, Yana</creatorcontrib><creatorcontrib>Guan, Xinping</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>Li, Junpeng</au><au>Hua, Changchun</au><au>Yang, Yana</au><au>Guan, Xinping</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Fuzzy Classifier Design for Development Tendency of Hot Metal Silicon Content in Blast Furnace</atitle><jtitle>IEEE transactions on industrial informatics</jtitle><stitle>TII</stitle><date>2018-03-01</date><risdate>2018</risdate><volume>14</volume><issue>3</issue><spage>1115</spage><epage>1123</epage><pages>1115-1123</pages><issn>1551-3203</issn><eissn>1941-0050</eissn><coden>ITIICH</coden><abstract>Since the hot metal silicon content simultaneously reflects the product quality and the thermal state of the blast furnace, accurately predicting the development tendency of hot metal silicon content has the immensely guiding role for blast furnace operators. This paper focuses on fuzzy classifier design for the development tendency of hot metal silicon content based on blast furnace operation data. The cross characteristic of binary classification problem was found via embedding high-dimensional blast furnace data into a two-dimensional space. Then, presented a nonparallel hyperplanes based fuzzy classifier, which conquered the cross classification still holding the interpretability advantage as fuzzy classifier. The proposed method was tested on No.2 blast furnace of Liuzhou Steel in China, that demonstrated the excellent performance compared with some other classifier algorithms.</abstract><cop>Piscataway</cop><pub>IEEE</pub><doi>10.1109/TII.2017.2770177</doi><tpages>9</tpages><orcidid>https://orcid.org/0000-0001-6311-2112</orcidid><orcidid>https://orcid.org/0000-0003-4028-2703</orcidid></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 1551-3203 |
| ispartof | IEEE transactions on industrial informatics, 2018-03, Vol.14 (3), p.1115-1123 |
| issn | 1551-3203 1941-0050 |
| language | eng |
| recordid | cdi_crossref_primary_10_1109_TII_2017_2770177 |
| source | IEEE Electronic Library (IEL) |
| subjects | Blast furnace Blast furnace practice Blast furnaces Classification Classifiers data visualization fuzzy classifier Hot blast hot metal silicon content hyperplane Hyperplanes Metals Predator prey systems Prediction algorithms Predictive models Repair & maintenance Silicon Steel industry Support vector machines |
| title | Fuzzy Classifier Design for Development Tendency of Hot Metal Silicon Content in Blast Furnace |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-03T02%3A32%3A35IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_RIE&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Fuzzy%20Classifier%20Design%20for%20Development%20Tendency%20of%20Hot%20Metal%20Silicon%20Content%20in%20Blast%20Furnace&rft.jtitle=IEEE%20transactions%20on%20industrial%20informatics&rft.au=Li,%20Junpeng&rft.date=2018-03-01&rft.volume=14&rft.issue=3&rft.spage=1115&rft.epage=1123&rft.pages=1115-1123&rft.issn=1551-3203&rft.eissn=1941-0050&rft.coden=ITIICH&rft_id=info:doi/10.1109/TII.2017.2770177&rft_dat=%3Cproquest_RIE%3E2010545780%3C/proquest_RIE%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2010545780&rft_id=info:pmid/&rft_ieee_id=8097020&rfr_iscdi=true |