Neural Model Taking into Account “Culture Memory”

Neural network modelling of fermentation processes. Microorganisms cultivation model

“…The training set consists of normalized biomass and substrate data from steady state and transient regimes obtained by dynamic step response experiments (Figs. 6,7,8,9) [12]. In case of step-type changes of the dilution rate D the culture requires some time to adapt to the new environmental conditions.…”

“…6,7,8,9) that the neural network A cannot be trained to fit the specific dynamic responses of the cell population and substrate concentration the step changes of the dilution rate before reaching the steady state, and especially the undershoots (overshoots). In contrast, the neural network models with time delay connections AC predict much better the biomass evolution (Figs.…”

“…In contrast, the neural network models with time delay connections AC predict much better the biomass evolution (Figs. 6,8). Comparison between the results according to the substrate concentration (Figs.…”

“…Neural networks appear as a promising alternative for non-linear systems modelling [4,5,6]. Their advantages over the classical mathematical models are the simultaneous structure and parameter identification as well as the ability to learn by examples [7].…”

Neural network modelling of fermentation processes

“…There are numerous examples of NN applications in modelling and control of fermentation processes [4,5,6,7,8]. Two approaches for neural network modelling of chemostat both accounting for the culture memory were advanced recently [7].…”