Hybrid neural simulation of a fed-batch bioreactor for a nonideal recombinant fermentation

Fermentations employing genetically modified microbes under industrial conditions are difficult to monitor on line or to describe by simple, good mathematical models. So, a practically convenient approach is to combine mathematical models of some aspects with artificial neural networks of those aspects which are difficult to measure or model. Such hybrid models have been applied earlier to laboratory-scale bioreactors. In the present work, a model based on laboratory data for the synthesis of recombinant beta-galactosidase was corrupted by adding imperfect mixing and noise in the feed stream to generate data mimicking a real nonideal operation. These data were used to train a recurrent Elman neural network and a hybrid neural network, and it was seen that a hybrid network provides more accurate estimates of both extracellular and intra-cellular variables. The benefit is enhanced by the hybrid network's superiority being more pronounced for the intra-cellular recombinant protein, beta-galactosidase, which is the main product of interest.