A new training method for hybrid models of bioprocesses

Modeling of bioprocesses for engineering applications is a very difficult a nd time consuming task, due to their complex nonlinear dynamic behavior. In the last years several propositions for hybrid models, and especially seri al approaches, were published and discussed, in order to combine analytical prior knowledge with the learning capabilities of Artificial Neural Networ ks (ANN). These approaches often require synchronous and equi-distant sampl ed training data. However, in practice concentrations are mostly off-line m easured, rare, and asynchronous. In this paper a new training method especi ally suited for very few asynchronously sampled data is presented and appli ed for modeling animal cell cultures. The achieved model is able to predict the concentrations of the reaction components inside a stirred tank biorea ctor.