On-line estimation of biomass concentration using a neural network and information about metabolic state.
This paper deals with the design of a neural network-based biomass concentration estimation system. This system is enhanced by the incorporation of information about the actual metabolism of the microorganism cultivated, which is taken from an on-line knowledge-based system. Two different design approaches have been investigated using the fed-batch cultivation of baker's yeast as the model process. In the first, metabolic state (MS) data were passed as additional input to the neural network; in the second, these data were used to select a neural network suitable for the specific MS. Two neural network types--feed-forward (Levenberg-Marquardt) and cascade correlation--were applied to this system and tested, and the performances of these neural networks were compared.
Department of Computing and Control Engineering, Institute of Chemical Technology, Prague Technická 5, 166 28 Prague 6, Czech Republic.
M
M
Vanek
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P
Hrncirík
J
J
Vovsík
J
J
Náhlík
D000465:Algorithms; D018533:Biomass; D049109:Cell Proliferation; D003198:Computer Simulation; D004734:Energy Metabolism / Q000502:physiology*; D016247:Information Storage and Retrieval / Q000379:methods*; D008954:Models, Biological; D016571:Neural Networks, Computer; D009862:Online Systems; D010101:Oxygen Consumption / Q000502:physiology; D012441:Saccharomyces cerevisiae / Q000254:growth & development* / Q000378:metabolism*