Hybrid neural network model of an industrial ethanol fermentation process considering the effect of temperature.
In this work a procedure for the development of a robust mathematical model for an industrial alcoholic fermentation process was evaluated. The proposed model is a hybrid neural model, which combines mass and energy balance equations with functional link networks to describe the kinetics. These networks have been shown to have a good nonlinear approximation capability, although the estimation of its weights is linear. The proposed model considers the effect of temperature on the kinetics and has the neural network weights reestimated always so that a change in operational conditions occurs. This allow to follow the system behavior when changes in operating conditions occur.
Mantovanelli
Ivana C C
IC
Department of Biotechnological Processes, School of Chemical Engineering-UNICAMP, Cidade Universitária Zeferino Vaz, Caixa Postal 6066, CEP 13083-970, Campinas SP, Brasil.
Rivera
Elmer Ccopa
EC
da Costa
Aline C
AC
Maciel Filho
Rubens
R
D003198:Computer Simulation; D000431:Ethanol / Q000378:metabolism*; D005285:Fermentation; D005947:Glucose / Q000378:metabolism*; D008954:Models, Biological; D016571:Neural Networks, Computer; D012441:Saccharomyces cerevisiae / Q000378:metabolism*; D013696:Temperature
D000431:Ethanol; D005947:Glucose