The production of high-content fructo-oligosaccharides by fermentation in a fed-batch bioreactor is considered. Fed-batch scenario allows to reduce inhibition mechanisms caused by the presence of byproducts, such as glucose, while maximizing biomass production. The determination of the feed flow rate profile that maximizes the concentration of fructo-oligosaccharides is performed by resorting to the maximum principle of Pontryagin. The optimization procedure takes into account inequality constraints on the state and control variables and allows the determination of the unspecified final fermentation time corresponding to maximal fructo-oligosaccharides content. The performance of the approach is demonstrated by simulations in the case of fructo-oligosaccharide production by Aspergillus sp.
International audienceThis paper presents a refined instrumental variable method for identifying partial differential equation models of distributed parameter systems directly from discrete-time sampled input-output data. The proposed method is compared with conventional least-squares and other instrumental variable-based techniques. Monte Carlo simulation analysis results are presented to illustrate the effectiveness and superiority of the proposed method in the presence of additive output measurement noise and under different spatio-temporal sampling conditions
This paper presents instrumental variable methods for identifying partial differential equation models of distributed parameter systems in presence of output measurement noise. Two instrumental variable-based techniques are proposed to handle this continuous-time model identification problem: a basic one using input-only instruments and a more sophisticated refined instrumental variable method. Numerical examples are presented to illustrate and compare the performances of the proposed approaches.
This paper deals with the identification of linear parameter varying (LPV) models described by partial differential equations (PDE). A direct identification of continuous space-time LPV-EDP systems in an input-output setting is investigated in the case of an additive output noise. The continuous space-time LPV-PDE model is firstly proposed to be rewritten as a multi-input single-output linear time-space invariant model and an iterative optimization is then developed to estimate efficiently the model parameters. The performance of the proposed method is then illustrated via a representative simulation example based on an Alsace river-flow measurement.
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