Implementation of an adaptive controller for the startup and steady-state running of a biomethanation process operated in the CSTR mode.

An adaptive control algorithm has been implemented on a biomethanation process to maintain propionate concentration, a stable variable, at a given low value, by steering the dilution rate. It was thereby expected to ensure the stability of the process during the startup and during steady-state running with an acceptable performance. The methane pilot reactor was operated in the completely mixed, once-through mode and computer-controlled during 161 days.

On-line prediction of fermentation variables using neural networks.

This article presents an introduction to the use of neural network computational algorithms for the dynamic modeling of bioprocesses. The dynamic neural model is used for the prediction of key fermentation variables. This relatively hew method is compared with a more traditional prediction technique to judge its performance for prediction.

Optimal control of biomass growth in a mixed culture.

It is shown that, in a mixed culture, under realistic assumptions, the optimal temperature profile maximizing the final biomass production under the constraint of the final relative proportions of the two populations is constant. This result is illustrated with a simulation experiment on a lactic fermentation model.