Pattern recognition for modeling and online diagnosis of bioprocesses

Bioprocesses are highly nonlinear and they operate within a wide range of o perating regimes. Proper modeling and control of these processes necessitat e real-time identification of these regimes, In this paper, we introduce an approach for the development of a fuzzy neural network (NN) model for a bi oprocess based on decomposition of the process into its different regimes. The model consists of multiple linear local models, one for each regime, an d its output is the interpolation of the outputs from the local models. Reg ime identification is performed using fuzzy clustering and NNs. The outcome of this identification technique is a set of membership functions which in dicate to what degree the process is governed by the three operating regime s at any given point in time. The method is illustrated through the develop ment of a real-time product estimation model for a simulated gluconic acid batch fermentation.