An evaluation of a neural network for the start-up phase of a continuous recombinant fermentation subject to disturbances

✍ Scribed by P. R. Patnaik

• Book ID: 104647039
• Publisher: Springer-Verlag
• Year: 1995
• Tongue: English
• Weight: 423 KB
• Volume: 9
• Category: Article
• ISSN: 0951-208X
• DOI: 10.1007/bf00156358

✦ Synopsis

A radial basis neural network wa.s applied to a, process for glyceraldehyde-3phosphate dehydrogenase produced by an Escherichia coli strain containing the plasmid pBR Eco gap. A neural network tra.ined with a pure culture predicted the performance of a fermentation containing wild type cells and/or product in the inoculum better than in the reverse case; this is explained.

In general, the network learnt the trends in the concentrations of plasmid-comaining cells and the recombinant product, more accurately than those of wild type cells and the substrate. This similarity with deterministic networks and the good predicta.bility with some training vectors suggests tha.t neural networks ca.n be used to simulate the start-up phase of recombinant fermenta.tions corrupted by disturba.nces.