TY  - JOUR
N1  - Copyright of this article belongs to Springer Science.
ID  - open873
UR  - http://dx.doi.org/10.1007/s004490100246
IS  - 3
A1  - Patnaik, P
Y1  - 2001///
N2  - 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 #-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 extra-cellular and intra-cellular variables. The benefit is enhanced by the hybrid network's superiority being more pronounced for the intra-cellular recombinant protein, #-galactosidase, which is the main product of interest.
PB  - Springer Science
JF  - Bioprocess and Biosystems Engineering
VL  - 24
SN  - 1615-7591
TI  - Hybrid neural simulation of a fed-batch bioreactor for a nonideal recombinant fermentation
SP  - 151
AV  - restricted
EP  - 161
ER  -