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A HYBRID NEURAL MODEL FOR THE OPTIMIZATION OF FED-BATCH FERMENTATIONS

1999; Springer Science+Business Media; Volume: 16; Issue: 1 Linguagem: Inglês

10.1590/s0104-66321999000100006

1678-4383

Aline Carvalho da Costa, A.S. Henriques, Tito Lívio Moitinho Alves, Rubens Maciel Filho, Enrique Luis Lima,

Viral Infectious Diseases and Gene Expression in Insects

In this work a hybrid neural modelling methodology, which combines mass balance equations with functional link networks (FLNs), used to represent kinetic 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 able to adjust to kinetic and environmental changes, it is suitable for use in the development of optimization strategies for fed-batch bioreactors. The proposed methodology is used to model the processes for penicillin and ethanol production, and the development of an adaptive optimal control scheme is discussed using ethanol fermentation as an example.