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.
Vanek
M
M
Department of Computing and Control Engineering, Institute of Chemical Technology, Prague Technická 5, 166 28 Prague 6, Czech Republic.
Hrncirík
P
P
Vovsík
J
J
Náhlík
J
J
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*