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An adaptive optimal control scheme based on hybrid neural modelling

1998; Elsevier BV; Volume: 22; Linguagem: Inglês

10.1016/s0098-1354(98)00166-5

1873-4375

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

Microbial Metabolic Engineering and Bioproduction

A hybrid neural modelling procedure which enables the implementation of an adaptive control scheme for the optimization of fed-batch fermentations is presented. Simulations for the processes of cell mass production and ethanol fermentation by Sacharomyces cerevisae show that, in the presence of modelling errors, the adaptive control leads to nearly optimal results, while open-loop control leads to bad results. Experimental studies show that, for the process of ethanol fermentation by Zymomonas mobilis, a hybrid neural model can be developed with relatively few experimental data and the use of an approximate mathematical model.