Modeling and control of fed-batch fermentation processes are inherently difficult due to the characteristics of complex non-linearity

non-steady-state behavior and batch-to-batch variation.Moreover

the process variables are always hard to measure online and the quality of the product is difficult to define.A novel key kernel network (KKN) was proposed for online modeling of nonlinear MIMO processes.The model complexity could be controlled by the criterion of only adding the “key nodes” to the learning machine; further

the prior knowledge of the process could be easily combined.Consequently the generalization ability and modeling speed were improved.The forward learning of KKN was efficiently updated whenever a new key node was introduced.The proposed KKN was applied to the online modeling of fermentation processes.The simulations on the fed-batch penicillin fermentation process showed that the biomass and penicillin concentrations could be predicted simultaneously with high precision and fast learning ability.Furthermore

the modeling precision can be improved because the process knowledge could be accumulated batch-to-batch.