Adaptive Extremum Seeking Control of Enzyme Production in Filamentous Fungal Fermentation

The aim of this paper is to investigate, via a case study, an adaptive extremum seeking control scheme for product formation in fed-batch bioreactors. The presented approach utilizes the structure information of the Haldane kinetic model, to derive an extremum seeking algorithm that drives the system to the desired set points with the objective to maximize the product formation rate. The adaptive extremum seeking algorithm consists of a control law and parameter learning laws designed by using Lyapunov’s stability arguments. The adaptive extremum seeking control scheme is applied to the maximization of the enzyme production yield in filamentous fungal fermentation. Numerical simulations are provided in order to investigate the effectiveness of the proposed scheme

Optimal Sensor Location based on the Observability Measures for an Anaerobic Wastewater Treatment Process

In this paper the choice of the optimal sensor location, especially of the gas product in an anaerobic wastewater treatment process is discussed. First, we have applied the observability measures theory considered in Waldraff, et al. (1998) on the distributed parameter model of an anaerobic wastewater treatment process (Schoefs, et al., 2003), which is discretized by using the orthogonal collocation method. Then a state observer has been implemented to illustrate the results. These investigations lead to recommend appropriate sensor location