Item request has been placed! ×
Item request cannot be made. ×
loading  Processing Request

Hybrid neural modeling of bioprocesses using functional link networks.

Item request has been placed! ×
Item request cannot be made. ×
loading   Processing Request
Read More Add to Saved list
  • Author(s): Harada LH;Harada LH; da Costa AC; Maciel Filho R
  • Source:
    Applied biochemistry and biotechnology [Appl Biochem Biotechnol] 2002 Spring; Vol. 98-100, pp. 1009-23.
  • Publication Type:
    Journal Article
  • Language:
    English
  • Additional Information
    • Source:
      Publisher: Humana Press Country of Publication: United States NLM ID: 8208561 Publication Model: Print Cited Medium: Print ISSN: 0273-2289 (Print) Linking ISSN: 02732289 NLM ISO Abbreviation: Appl Biochem Biotechnol Subsets: MEDLINE
    • Publication Information:
      Original Publication: Clifton, N.J. : Humana Press, c1981-
    • Subject Terms:
      Neural Networks, Computer*; Biology/*methods ; Saccharomyces cerevisiae/*physiology; Computer Simulation ; Ethanol/metabolism ; Fermentation ; Kinetics ; Reproducibility of Results ; Saccharomyces cerevisiae/growth & development
    • 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.
    • Accession Number:
      3K9958V90M (Ethanol)
    • Publication Date:
      Date Created: 20020523 Date Completed: 20021106 Latest Revision: 20191210
    • Publication Date:
      20240513
    • Accession Number:
      10.1385/abab:98-100:1-9:1009
    • Accession Number:
      12018225