title: Hybrid neural modeling of bioprocesses using functional link networks.
abstract: The objective of this work was to develop a model for an extractive ethanol fermentation in a simple and rapid way. This model must be sufficiently reliable to be used for posterior optimization and control studies. A hybrid neural model was developed, combining mass and energy balances with neural networks, which describe the process kinetics. To determine the best model, two structures of neural networks were compared: the functional link networks and the feedforward neural networks. The two structures are shown to describe well the process kinetics, and the advantages of using the functional link networks are discussed.
authors.affiliation: DPQ/FEQ/UNICAMP, Campinas, SP, Brazil.
authors.forename: Layse H P
authors.initials: LH
authors.lastname: Harada
authors.forename: Aline C
authors.initials: AC
authors.lastname: da Costa
authors.forename: Rubens
authors.initials: R
authors.lastname: Maciel Filho
mesh_terms: D001695:Biology / Q000379:methods*; D003198:Computer Simulation; D000431:Ethanol / Q000378:metabolism; D005285:Fermentation; D007700:Kinetics; D016571:Neural Networks, Computer; D015203:Reproducibility of Results; D012441:Saccharomyces cerevisiae / Q000254:growth & development / Q000502:physiology*
chemical_list: D000431:Ethanol