Neural state estimation and prediction in amino acid fermentation

Summary On-line control of amino acid fermentations is complicated by uncertainties typical of biological processes and by difficulties in real-time monitoring of key process variables. Lysine is an essential amino acid 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 estimation, and multi-step ahead prediction of produced lysine and consumed sugar. Neural networks were programmed in MS Visual C++ for Windows for implementation in a PC, with a userfriendly interface for convenience and ease of operation. It is demonstrated that a well trained neural network of optimal architecture can be succesfsully used in control of amino acid production