The liquidus temperature is an important parameter in understanding the
crystalline behavior of materials and in the operation of blast furnaces. Its
modeling can be carried out by linear and nonlinear methods through data,
considering the artificial neural network a modeling method with high
efficiency because it presents the theorem of universal approximation and with
that better performances and possibility of greater oscillations. The best
linear model and the best nonlinear model were modeled by structural parameters
and presented a good numerical approximation, thus demonstrating that
mathematical modeling can be performed using structural arguments and also
showing a dimensionality reduction method for modeling a thermophysical
property of the materials.Comment: 11 pages, 8 figures, 3 table