Utilização de aprendizado supervisionado na predição da demanda de energia no processo de produção de cumeno
Resumen
Data Science is a field of knowledge very requested nowadays, specially for
digital and technology industries. Despite the increasing investment in this area,
also by chemical industries, some elements of data science, such as Artificial
Intelligence and machine learning have been used by these industries since
1980s, with artifical neuralnetworks (ANN) being the most used type of
algorithms. ANN are great at solving complex non-linear problems but this type
of algorithms have some disadvantages such as poor comprehensibility and high
hardware dependence. Therefore, this study is about the use of three supervised
learning models (linear regression, decision tree and random forest) of high
comprehensibility and low hardware dependence for the prediction of total energy
and specific energy (energy per mass of cumene) of a cumene producing plant
by benzene with propene alkylation. The features are molar flow of propene,
molar flow of benzene, temperature and pressure. Data were generated by
simulations in Aspen Plus® software, treated and modeled using Python and MS
Excel. The models were better at prediction of total energy than specific energy.
Even with all the complexity of a chemical process, the models were able to
predict the energy required with approximately ±10% errors.
Colecciones
El ítem tiene asociados los siguientes ficheros de licencia: