Abstract
Conventional modeling, state estimation, multi-step ahead prediction, and control of bioprocesses is often difficult owing to the uncertainties involved. Back-propagation multi-layer neural network models were constructed to overcome such problems. Neural networks were programmed in MS-Visual C++ for Windows and implemented in a personal computer with an 80486/66 MHz processor. State estimation and multi-step ahead prediction of consumed sugar and produced lysine in a industrial scale bioprocess were successfully demonstrated.
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