RODRIGUES, N. T. J.; http://lattes.cnpq.br/3177793164802505; RODRIGUES, Nahanni Taynah Jácome.
Resumen:
The billet reheating process within the rolling industry is a crucial step in obtaining products
with the desired quality, as it ensures that the parts have the ideal mechanical properties, such
as hardness, elasticity, ductility and tensile strength. The temperature inside the oven needs to
be controlled within a range of 1000 to 1300°C, which varies according to the operation of the
oven, so that the parts do not melt or lose the desired properties, as well as waste of out-of-
quality parts. and fuel used to promote heat transfer to the parts through combustion in the oven
zones. Thus, the present work presents the creation of Neural Networks of the LSTM type to
predict patterns in the billet reheating process, so that it can be used in the construction of PI
type controllers, to be used in the eight zones present in the furnace under study. Different
methods were used to process the input data to create the model, such as normalization,
sampling rate variation, elimination of outliers, in order to discover their influence on the
accuracy of the RNA obtained. The results show that the developed RNA were satisfactory to
predict data of complex processes that involve many variables, as the one studied, as well as
presents the obtaining of a PI controller that allows to keep each zone of the oven within the
desired temperature values for it, however. The RNA obtained for the sampling rate of 30
seconds showed a better learning of the process data. The controller developed was able to
maintain the setpoint temperatures established for each zone, showing an effective tool to obtain
parts with the desired quality for the mill, avoiding waste, increasing the mill's useful life, and
consuming the necessary amount of fuels that feed the oven and promote the combustion
necessary to transfer heat to the parts.
