Hot metal temperature prediction in blast furnace using advanced model based on fuzzy logic tools

Abstract

The present work presents a model based on fuzzy logic tools to predict and simulate the hot metal temperature in a blast furnace (BF). As input variables this model uses the control variables of a current BF such as moisture, pulverised coal injection, oxygen addition, mineral/coke ratio and blast volume, and it yields as a result of the hot metal temperature. The variables employed to develop the model have been obtained from data supplied by current sensors of a Spanish BF. In the model training stage the adaptive neurofuzzy inference system and the subtractive clustering algorithms have been used.