A New “User-friendly” Blast Furnace Advisory Control System Using a Neural Network Temperature Profile Classifier
スポンサーリンク
概要
- 論文の詳細を見る
The adaptation of blast furnaces to the new technologies has increased the operation information so that the sensor information can be known at every moment. However this often results in the supply of excessive data volume to the plant operators. This paper describes an industrial application for self-organized maps (SOM) in order to help them make decisions regarding blast furnace control by means of pattern recognition and the matching of temperature profiles supplied by the thermocouples placed on the above burden. The classification of patterns via easy color coding indicates to the operator what the blast furnace operational situation is, thus making the necessary corrections easier.
著者
-
García Francisco
Escuela Técnica Superior de Ingenieros Industriales, Universidad Politécnica de Madrid
-
Campoy Pascual
Escuela Técnica Superior de Ingenieros Industriales, Universidad Politécnica de Madrid
-
Mochón Javier.
Centro Nacional de Investigaciones Metalúrgicas (CSIC-CENIM)
-
Ruiz-Bustinza Iñigo
Centro Nacional de Investigaciones Metalúrgicas (CSIC-CENIM)
-
Verdeja Luis
Escuela Técnica Superior de Minas de Oviedo, Universidad de Oviedo
-
Duarte Ramon
Centro Nacional de Investigaciones Metalúrgicas (CSIC-CENIM)
関連論文
- A New “User-friendly” Blast Furnace Advisory Control System Using a Neural Network Temperature Profile Classifier
- A New "User-friendly" Blast Furnace Advisory Control System Using a Neural Network Temperature Profile Classifier
- Results of the Application of the Mdn in the Improvement of the Design of an Electrical Furnace that produces Low Carbon Ferromanganese