Abstract
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
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
