This work describes the development and further validation of a model devot
ed to blast furnace hot metal temperature forecast, based on Fuzzy logic pr
inciples. The model employs as input variables, the control variables of an
actual blast furnace: Blast volume, moisture, coal injection, oxygen addit
ion, etc. and it yields as a result the hot metal temperature with a foreca
st horizon of forty minutes. As far as the variables used to develop the mo
del have been obtained from data supplied by an actual blast furnace sensor
s, it is necessary to properly analyse and handle such data. Especial atten
tion was paid to data temporal correlation, fitting by interpolation the di
fferent sampling rates. in the training stage of the model the ANFIS (Adapt
ive Neuro-Fuzzy inference System) and the Subtractive Clustering algorithms
have been used.