Blast Furnace Hot Metal Temperature Prediction through Neural Networks-Based Models

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概要

• 論文の詳細を見る

• Blast furnace hot metal temperature prediction, by mean of mathematical models, plays an interesting role in blast furnace control, helping plant operators to give a faster and more accurate answer to changes in blast furnace state. In this work, the development of parametric models based on neural networks is shown. Time has been included as an implicit variable to improve consistency. The model has been developed departing from actual plant data supplied by Aceralia from its steel works located in Gijón.

• 社団法人 日本鉄鋼協会の論文
• 2004-03-15

著者

• De Ayala

  Centro Nacional De Investigaciones Metalugicas C/ Gregorio Del Amo Gijon Esp

• De Ayala

  Aceralia (verina)

• Jimenez Juan

  Centro Nacional De Investigaciones Metalugicas C/gregorio Del Amo

• Mochon Javier

  Centro Nacional De Investigaciones Metalugicas C/gregorio Del Amo

• OBESO Faustino

  Aceralia (Verina)

関連論文

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• Blast Furnace Hot Metal Temperature Prediction through Neural Networks-Based Models
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• Results of the Application of the Mdn in the Improvement of the Design of an Electrical Furnace that produces Low Carbon Ferromanganese
• The Influence of Different Iron Ores Mixtures Composition on the Quality of Sinter
• Fuzzy Control of Micum Strength for Iron Ore Sinter

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