Neural simulation of an unsteady state continuous recombinant fermentation with imperfect mixing

Summary A continuous fermentation based on a recombinant Escherichia coli strain producing tryptophan synthetase has been simulated by a back-propagation neural network. Data for the network were generated through known kinetics applied to a reactor model with an adjustable degree of macromixing of the broth. A network with just one hidden layer performed satisfactorily for both poor and good macromixing. The best performance was at an intermediate level of mixing, in the region of maximum productivity of the recombinant protein.