A hybrid neural model for the optimization of fed-batch fermentations

In this work a hybrid neural modelling methodology, which combines mass bal ance equations with functional link networks (FLNs), used to represent kine tic rates, is developed for bioprocesses. The simple structure of the FLNs allows the easy and rapid estimation of network weights and, consequently, the use of the hybrid model in an adaptive form. As the proposed model is a ble to adjust to kinetic and environmental changes, it is suitable for use in the development of optimization strategies for fed-batch bioreactors. Th e proposed methodology is used to model the processes for penicillin and et hanol production, and the development of an adaptive optimal control scheme is discussed using ethanol fermentation as an example.