The endpoint forecast of AOD stove ferroalloy steel-making based on wavelet neural network
The AOD stove ferroalloy steel-making endpoint temperature and the ingredient are the control objectives of AOD stove ferroalloy steel-making, which has serious nonlinear relations with variables such as oxygen blown quantity and the quantity of molten steel and is unable to measure continuously onl...
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| creator | Yangxiao Hong Xu Jing Yanghong Tao |
| description | The AOD stove ferroalloy steel-making endpoint temperature and the ingredient are the control objectives of AOD stove ferroalloy steel-making, which has serious nonlinear relations with variables such as oxygen blown quantity and the quantity of molten steel and is unable to measure continuously online. This article develops a set of AOD stove smelt ferroalloy end-point control model based on the wavelet neural network and some actual data of a 180t AOD ferroalloy stove in Jilin Ferroalloy Factory to conduct the model verification research. By forecasting the end-point temperature and the carbon content and gathering the spot operating data and the practical application, we can see that the double hit probability of carbon and temperature reaches above 80%. |
| doi_str_mv | 10.1109/CCDC.2010.5498689 |
| format | Conference Proceeding |
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This article develops a set of AOD stove smelt ferroalloy end-point control model based on the wavelet neural network and some actual data of a 180t AOD ferroalloy stove in Jilin Ferroalloy Factory to conduct the model verification research. 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This article develops a set of AOD stove smelt ferroalloy end-point control model based on the wavelet neural network and some actual data of a 180t AOD ferroalloy stove in Jilin Ferroalloy Factory to conduct the model verification research. By forecasting the end-point temperature and the carbon content and gathering the spot operating data and the practical application, we can see that the double hit probability of carbon and temperature reaches above 80%.</description><subject>AOD Stove</subject><subject>Argon</subject><subject>Biological neural networks</subject><subject>Chromium</subject><subject>Control engineering</subject><subject>Ferroalloy</subject><subject>Forecast</subject><subject>Iron</subject><subject>Neural networks</subject><subject>Predictive models</subject><subject>Steel</subject><subject>Temperature control</subject><subject>Temperature distribution</subject><subject>Wavelet Neural Network</subject><issn>1948-9439</issn><issn>1948-9447</issn><isbn>1424451817</isbn><isbn>9781424451814</isbn><isbn>1424451825</isbn><isbn>9781424451821</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2010</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpFkMtuwjAURN0HUoHyAVU3_oFQP65je4lCXxISG7rpBjnkuk0JMXJcEH_fSEXtbI5mRprFEHLH2ZRzZh-KYl5MBeutAmtyYy_IiIMAUNwIdUmG3ILJLIC--i-4vv4rpB2QkWDMWglS8hsy6bov1guU4FoPyfvqEym21T7UbaI-RNy4LtHg6Ww5p10KB6QeYwyuacKpDxCbbOe2dftBS9dhRUNLj-6ADSba4nd0TY90DHF7SwbeNR1OzhyTt6fHVfGSLZbPr8VskdVcq5TlSpeojBa6AmMrwZSSWohSCWcZB883Mhe58tYxdBuJpmSllCxnXgNYruSY3P_u1oi43sd65-JpfT5M_gA9SVfa</recordid><startdate>201005</startdate><enddate>201005</enddate><creator>Yangxiao Hong</creator><creator>Xu Jing</creator><creator>Yanghong Tao</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201005</creationdate><title>The endpoint forecast of AOD stove ferroalloy steel-making based on wavelet neural network</title><author>Yangxiao Hong ; Xu Jing ; Yanghong Tao</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-657be58727d489d20553722b52a9014f1c36265f9a0eac3e8b0b33060f7449153</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2010</creationdate><topic>AOD Stove</topic><topic>Argon</topic><topic>Biological neural networks</topic><topic>Chromium</topic><topic>Control engineering</topic><topic>Ferroalloy</topic><topic>Forecast</topic><topic>Iron</topic><topic>Neural networks</topic><topic>Predictive models</topic><topic>Steel</topic><topic>Temperature control</topic><topic>Temperature distribution</topic><topic>Wavelet Neural Network</topic><toplevel>online_resources</toplevel><creatorcontrib>Yangxiao Hong</creatorcontrib><creatorcontrib>Xu Jing</creatorcontrib><creatorcontrib>Yanghong Tao</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Xplore</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Yangxiao Hong</au><au>Xu Jing</au><au>Yanghong Tao</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>The endpoint forecast of AOD stove ferroalloy steel-making based on wavelet neural network</atitle><btitle>2010 Chinese Control and Decision Conference</btitle><stitle>CCDC</stitle><date>2010-05</date><risdate>2010</risdate><spage>2882</spage><epage>2886</epage><pages>2882-2886</pages><issn>1948-9439</issn><eissn>1948-9447</eissn><isbn>1424451817</isbn><isbn>9781424451814</isbn><eisbn>1424451825</eisbn><eisbn>9781424451821</eisbn><abstract>The AOD stove ferroalloy steel-making endpoint temperature and the ingredient are the control objectives of AOD stove ferroalloy steel-making, which has serious nonlinear relations with variables such as oxygen blown quantity and the quantity of molten steel and is unable to measure continuously online. This article develops a set of AOD stove smelt ferroalloy end-point control model based on the wavelet neural network and some actual data of a 180t AOD ferroalloy stove in Jilin Ferroalloy Factory to conduct the model verification research. By forecasting the end-point temperature and the carbon content and gathering the spot operating data and the practical application, we can see that the double hit probability of carbon and temperature reaches above 80%.</abstract><pub>IEEE</pub><doi>10.1109/CCDC.2010.5498689</doi><tpages>5</tpages></addata></record> |
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| ispartof | 2010 Chinese Control and Decision Conference, 2010, p.2882-2886 |
| issn | 1948-9439 1948-9447 |
| language | eng |
| recordid | cdi_ieee_primary_5498689 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | AOD Stove Argon Biological neural networks Chromium Control engineering Ferroalloy Forecast Iron Neural networks Predictive models Steel Temperature control Temperature distribution Wavelet Neural Network |
| title | The endpoint forecast of AOD stove ferroalloy steel-making based on wavelet neural network |
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