Application of neural networks to lysine production

Lysine is an essential amino acid in human nutrition and also widely used in animal feed formulations. It is produced on a large scale by fermentation in stirred tank bioreactors. In the present work lysine was produced by fed-batch fermentation with an industrial Brevibacterium flavum strain grown in a 115 m³ fermentor on a beet molasses based medium. The difficulties in on-line monitoring of substrate consumption and of product formation complicate real-time process control. We demonstrate that well-trained backpropagation multilayer neural networks can be employed to overcome such problems without detailed prior knowledge of the relationships of process variables under investigation. Neural network models programmed in MS-Visual C++ for Windows and implemented on a personal computer were constructed and applied to state estimation and multi-step-ahead prediction of consumed sugar and produced lysine on the basis of on-line measurable variables for process control purposes.