FARIAS, G. N. R.; RACHED, G. N. F.; NAYEF, GIRRAD.; http://lattes.cnpq.br/8252497014365763; FARIAS, Girrad Nayef Rached.
Resumo:
Coal is an input used as a reducer and/or energy source in several processes. This fuel
is found in such a way that it cannot be used without previous treatment, requiring
operations to reduce granulometry and moisture. Thus, this work aims to develop a
phenomenological model of the coal definition process in Aspen Plus to serve as a data
generator for the creation of metamodels based on Artificial Neural Networks, and thus, to
evaluate routine optimization process scenarios. Together, a Genetic Algorithm was
applied to determine ANN architectures, in order to reduce the metamodel prediction error.
With the simulation validated from industrial data, it was possible to optimize the
architecture of the network, there was a change in the number of layers to two and neurons,
contributing to improve the prediction of the process, obtaining average errors from 0.01%
to 1.66 % of predicted variables. The optimization of the operating scenario managed to
significantly reduce the consumption of fuels used for heating the drying gas, with all
process quality variables being met, resulting in a reduction of 15% of blast furnace gas
and 80% of natural gas, reducing the energy consumption of the process.