Resumen:
In the muti-hearth furnace, there is a problem related to the automatic operation
of the loops of temperature regulation in hearths four and six, since the same flow of
air diverged into two branches. In this work, the authors take advantage of the capacity
of artificial neural networks for the learning of complex relationships, starting from a
set of examples. A neuronal model of the post-combustion sub-process in an Indus-trial
furnace, which will serve to raise an automatic control strategy, is obtained. Experiments
were carried out with binary pseudo-random sequences of modulated amplitude on the
flow of ore, and the openings of the regulating valves of air flow to hearths mentioned
before, to determine their effect on the temperature. The trial and error process enabled
to obtain an artificial neural network of multilayer perceptron type, capable of predicting
the temperature of hearth four with errors less than 0.5%, and 0.9% for the hearth six.
