Identification of Post-Combustion Sub-Process Using Artificial Neural Networks

Abstract

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.