Fermentation seed quality analysis with self-organising neural networks

Industrial fermentation processes operate under well defined operating conditions to attempt to minimise production variability. Variability occurs for many reasons but a long held belief is that variation in the state of the seed is highly influential. In this paper a seed stage (a batch process) of an industrial antibiotic fermentation is considered and the performance of the main production fermentations is correlated with the quality of the seed using an unsupervised Kohonen self-organising feature map (SOM). It is shown that using only seed information poor performance in the final stage fermentations can be predicted. Data from industrial penicillin G fermenters is used to demonstrate the procedure. Copyright 1999 John Wiley & Sons, Inc.

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