A NEURAL-NETWORK-BASED MODEL OF BIOREACTION KINETICS

A neural-network based model of the kinetics in a fermentation process is presented. In the development and application of the model, the st ate of the process was estimated by an independent model based on elem ental balances and an electroneutrality condition. The aim of the work was a model that describes the overall kinetic relations well enough to be used for on-line simulation and prediction. Both feed-forward an d partially recurrent networks were investigated. The networks were tr ained off-line, but in the on-line application they are adapted to the process conditions by adjusting a set of correction factors. The appr oach is illustrated on a number of batch runs of Saccharomyces cerevis iae, where the networks are demonstrated to be able to accurately desc ribe the biphasic growth.