Blast furnace hot metal temperature prediction through neural networks-based models

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 Gijon.