Abstract
Improvement of the fermentation efficiency of poly-β-hydroxybutyrate (PHB) may make it competitive with chemically synthesized petroleum-based polymers. One step toward this is optimization of fluid dispersion and the feed rates to a fed-batch bioreactor. In a recent study using a fermentation model, dispersion corresponding to a Peclet number of ≈20 was shown to maximize the productivity of PHB. Here further improvement has been investigated using neural optimization. A comparison of seven neural topologies has shown that while feed-forward and radial basis neural networks are computationally efficient, recurrent networks generate higher concentrations of PHB. All networks enhanced the productivity by 16–93% over model-based optimization.
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Patnaik, P.R. Neural network designs for poly- β-hydroxybutyrate production optimization under simulated industrial conditions. Biotechnol Lett 27, 409–415 (2005). https://doi.org/10.1007/s10529-005-1775-0
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DOI: https://doi.org/10.1007/s10529-005-1775-0