Neural network designs for poly-beta-hydroxybutyrate production optimization under simulated industrial conditions.
Improvement of the fermentation efficiency of poly-beta-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 approximately 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.
Patnaik
P R
PR
Institute of Microbial Technology, Sector 39-A, Chandigarh, 160 036, India. pratap@imtech.res.in
D019149:Bioreactors; D005285:Fermentation; D006885:Hydroxybutyrates / Q000378:metabolism*; D007218:Industrial Microbiology / Q000379:methods*; D016571:Neural Networks, Computer; D011091:Polyesters / Q000378:metabolism*; D000094122:Polyhydroxybutyrates
D006885:Hydroxybutyrates; D011091:Polyesters; C003182:poly-beta-hydroxybutyrate; D000094122:Polyhydroxybutyrates