Recognition of the operational states in electric arc furnaces
For the optimization of the operation of electric arc furnaces (EAFs) it is important that the actual operational state of the furnace can be quickly and exactly determined. This paper presents a new approach that allows tracking of the melting process. This method uses a neural network in order to...
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| creator | Raisz, D. Sakulin, M. Renner, H. Tehlivets, Y. |
| description | For the optimization of the operation of electric arc furnaces (EAFs) it is important that the actual operational state of the furnace can be quickly and exactly determined. This paper presents a new approach that allows tracking of the melting process. This method uses a neural network in order to classify the dynamic characteristics and is compared in this paper with other methods, like the smoothed standard deviation of arc voltages and the partial harmonic distortion approaches. Finally, an application example for the introduced procedure is shown. |
| doi_str_mv | 10.1109/ICHQP.2000.897725 |
| format | Conference Proceeding |
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This paper presents a new approach that allows tracking of the melting process. This method uses a neural network in order to classify the dynamic characteristics and is compared in this paper with other methods, like the smoothed standard deviation of arc voltages and the partial harmonic distortion approaches. Finally, an application example for the introduced procedure is shown.</description><identifier>ISBN: 9780780364998</identifier><identifier>ISBN: 0780364996</identifier><identifier>DOI: 10.1109/ICHQP.2000.897725</identifier><language>eng</language><publisher>IEEE</publisher><subject>Electrodes ; Furnaces ; Iron ; Neural networks ; Power generation economics ; Power system economics ; Power system protection ; Productivity ; Slag ; Steel</subject><ispartof>Ninth International Conference on Harmonics and Quality of Power. Proceedings (Cat. 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Finally, an application example for the introduced procedure is shown.</description><subject>Electrodes</subject><subject>Furnaces</subject><subject>Iron</subject><subject>Neural networks</subject><subject>Power generation economics</subject><subject>Power system economics</subject><subject>Power system protection</subject><subject>Productivity</subject><subject>Slag</subject><subject>Steel</subject><isbn>9780780364998</isbn><isbn>0780364996</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2000</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotj9tKxDAURQMiKGM_QJ_yA60nlzbJiyBFnYEBL-jzcJqeaKS2QxIf_HtHRtiwYW1YsBm7FNAIAe5606-fnxoJAI11xsj2hFXOWDhEddo5e8aqnD8PO-hWdw7O2c0L-eV9jiUuM18CLx_Elz0l_AM48VywUOZx5jSRLyl6jsnz8J1m9JQv2GnAKVP13yv2dn_32q_r7ePDpr_d1lEYXWoPavTGStSDAikEtiACKRPcoAVq6ZwAHP1gzdhBADVgR86jQdkaN45WrdjV0RuJaLdP8QvTz-54Uv0C1mVHyA</recordid><startdate>2000</startdate><enddate>2000</enddate><creator>Raisz, D.</creator><creator>Sakulin, M.</creator><creator>Renner, H.</creator><creator>Tehlivets, Y.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>2000</creationdate><title>Recognition of the operational states in electric arc furnaces</title><author>Raisz, D. ; Sakulin, M. ; Renner, H. ; Tehlivets, Y.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i174t-c03dc782a4b30211a501fe37f9b41a429910adcb87d60f03ba6e9ca7a2579dd83</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2000</creationdate><topic>Electrodes</topic><topic>Furnaces</topic><topic>Iron</topic><topic>Neural networks</topic><topic>Power generation economics</topic><topic>Power system economics</topic><topic>Power system protection</topic><topic>Productivity</topic><topic>Slag</topic><topic>Steel</topic><toplevel>online_resources</toplevel><creatorcontrib>Raisz, D.</creatorcontrib><creatorcontrib>Sakulin, M.</creatorcontrib><creatorcontrib>Renner, H.</creatorcontrib><creatorcontrib>Tehlivets, Y.</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 Electronic Library (IEL)</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>Raisz, D.</au><au>Sakulin, M.</au><au>Renner, H.</au><au>Tehlivets, Y.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Recognition of the operational states in electric arc furnaces</atitle><btitle>Ninth International Conference on Harmonics and Quality of Power. Proceedings (Cat. No.00EX441)</btitle><stitle>ICHQP</stitle><date>2000</date><risdate>2000</risdate><volume>2</volume><spage>475</spage><epage>480 vol.2</epage><pages>475-480 vol.2</pages><isbn>9780780364998</isbn><isbn>0780364996</isbn><abstract>For the optimization of the operation of electric arc furnaces (EAFs) it is important that the actual operational state of the furnace can be quickly and exactly determined. This paper presents a new approach that allows tracking of the melting process. This method uses a neural network in order to classify the dynamic characteristics and is compared in this paper with other methods, like the smoothed standard deviation of arc voltages and the partial harmonic distortion approaches. Finally, an application example for the introduced procedure is shown.</abstract><pub>IEEE</pub><doi>10.1109/ICHQP.2000.897725</doi></addata></record> |
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| identifier | ISBN: 9780780364998 |
| ispartof | Ninth International Conference on Harmonics and Quality of Power. Proceedings (Cat. No.00EX441), 2000, Vol.2, p.475-480 vol.2 |
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| language | eng |
| recordid | cdi_ieee_primary_897725 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Electrodes Furnaces Iron Neural networks Power generation economics Power system economics Power system protection Productivity Slag Steel |
| title | Recognition of the operational states in electric arc furnaces |
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