  Viscosity in the metallurgical and glass industry plays a fundamental role in
its production processes, also in the area of geophysics. As its experimental
measurement is financially expensive, also in terms of time, several
mathematical models were built to provide viscosity results as a function of
several variables, such as chemical composition and temperature, in linear and
nonlinear models. A database was built in order to produce a nonlinear model by
artificial neural networks by variation of hyperparameters to provide reliable
predictions of viscosity in relation to chemical systems and temperatures. The
model produced named Viskositas demonstrated better statistical evaluations of
mean absolute error, standard deviation and coefficient of determination in
relation to the test database when compared to different models from literature
and 1 commercial model, offering predictions with lower errors, less
variability and less generation of outliers.
