NEURAL STATE ESTIMATION AND PREDICTION IN AMINO-ACID FERMENTATION

On-line control of amino acid fermentations is complicated by uncertai nties typical of biological processes and by difficulties in real-time monitoring of key process variables. Lysine is an essential amino aci d in human nutrition, and also widely used in animal feed formulations . The paper discusses the construction and application of feed-forward , back-propagation neural networks as software 'sensors' in state esti mation, and multi-step ahead prediction of produced lysine and consume d sugar. Neural networks were programmed in MS Visual C++ for Windows for implementation in a PC, with a userfriendly interface for convenie nce and ease of operation. It is demonstrated that a well trained neur al network of optimal architecture can be succesfsully used in control of amino acid production