Nonlinear Identification of Recombinant E. coli Fed-Batch Fermentation with
a Hybrid Method
The paper deals with the identification of a fed-batch
biotechnological process based on experimental data from
process. The process considered in the study is fed-batch
cultivation of E. coli BL21 (DE3) [pET3a-ifn_] under maximum
attainable specific growth rate for producing _-Interferon
protein that maximum cell density (107 g/L DCW) and rhIFN-_
concentration (26.5 g/L) after 16.5 h were achieved. For
Nonlinear identification of recombinant E coli Fed-batch
fermentation Wiener model based on neural network were used.
The linear part of Wiener model is represented in state space
form and nonlinear part is estimated with a single layer neural
network. After identification Wiener Model is optimized with
numerical optimization algorithm. Validation results show good
compatibility with experimental data and Euclidean norm of the
estimated outputs error are less than 2.72 and residual errors
between estimated outputs and experimental data are less than
0.01.
Index Terms
E. coli, Fed-batch fermentation, MOESP algorithms, neural network,
numerical optimization, Wiener model.