The on-line control of enzyme-production processes is difficult, owing to t
he uncertainties typical of biological systems and to the lack of suitable
on-line sensors for key process variables. For example, intelligent methods
to predict the end point of fermentation could be of great economic value.
Computer-assisted control based on artificial-neural-network models offers
a novel solution in such situations. Well-trained feedforward-backpropagat
ion neural networks can be used as software sensors in enzyme-process contr
ol; their performance can be affected by a number of factors.