Fermentation seed quality analysis with self-organising neural networks

Industrial fermentation processes operate under well defined operating cond itions to attempt to minimise production variability. Variability occurs fo r 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) o f an industrial antibiotic fermentation is considered and the performance o f the main production fermentations is correlated with the quality of the s eed 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 fermenter s is used to demonstrate the procedure. (C) 1999 John Wiley & Sons, Inc.