A recurrent neural network for a fed-batch fermentation with recombinant Escherichia coli subject to inflow disturbances

Abstract

A fed-batch fermentation for β-galactosidase production by a recombinant Escherichia coli strain has been simulated with interruptions in the inflow rates of a concentrated substrate and a diluent. Data covering a 12 h fermentation period were simulated by a 6-12-4 Elman neural network with two extra- and two intracellular variables as outputs. Three types of inflow failure were considered: either of the two feed streams separately or both together. Despite the fermentation performance being quite different for each type of failure, a network trained with data pertaining to one kind of failure was able to mimic the performance adequately for the other two data sets. The largest error and root mean square error were 14 and 10%, respectively, among the plasmid DNA and the intracellular protein concentrations, and 9 and 6% for the concentrations and mass fractions of recombinant cells. The accuracy is better than that reported for back-propagation networks.