A neural network for the optimization of fed-batch glutamic acid production

A neural network system, which is composed of a simulation subnetwork and an optimization subnetwork, was constructed to predict and optimize the industrial fed-batch fermentation of glutamic acid. A data-compression and filtration network structure was incorporated into the simulation subnetwork to extract "noise-free" patterns from the input signals. The sole-variant optimization and the multiple-variant optimization can be easily carried out by the neural network developed. A satisfactory result has been achieved.