Ghizlane Hafidi

Nonlinear predictive control for continuous microalgae cultivation process in a photobioreactor

This paper presents a nonlinear predictive control (NMPC) strategy applied to the continuous microalgae cultivation process in a closed photobioreactor. The photo-bioreactor system is programmed to operate in a constant biomass density mode, in order to maintain the culture at the optimal population density and sustain high biomass production levels. This objective is achieved by regulating the biomass density while tracking a reference culture medium feeding profile determined off-line. The proposed NMPC approach is validated in simulation, and performances and benefits for the cell cultivations are compared to those obtained when applying a generic model control (GMC) law.

Supervisory Control of Hybrid Powertrains: An Experimental Benchmark of Offline Optimization and Online Energy Management

Abstract The paper describes a chain of tools aimed at the development and validation of energy management strategies (EMS) for hybrid powertrains. These tools comprise an offline optimizer based on Pontryagin minimum principle (PMP) and the online equivalent consumption minimization strategy (ECMS), both implemented in a dynamic simulation platform and as a real-time controller in a semi-physical testing equipment. The results presented are aimed at illustrating the continuity of the various approaches by comparing the offline-generated energy management laws with their online counterparts, both in terms of trajectories over time and in terms of global results (fuel consumption, state-of-charge deviations).

Robustness analysis of nonlinear predictive control of fed-batch E. Coli cultures

This paper presents a nonlinear predictive control strategy for maximizing the biomass growth in fed-batch cultures of E. Coli. This objective is achieved by regulating the acetate concentration at a low measurable value, while tracking (in a compromising way) an optimal glucose feed-rate trajectory determined off-line. Robustness with respect to model uncertainties is improved by taking system/model mismatch during prediction into account. Robustness is practically assessed by means of a Monte Carlo procedure, considering statistical results obtained in the case of model parameter uncertainties.

Nonlinear Model Predictive Control applied to E. Coli Cultures

This paper proposes a nonlinear model predictive control scheme with particular application to the regulation of acetate concentration in order to maximize the biomass growth during fed-batch cultures of E. Coli. A reference feed rate which enables to maintain the acetate concentration at a specified level is first determined by means of optimal conditions. This feed rate is further used as the reference control trajectory within the NMPC algorithm. Finally, to avoid discretization problems during NMPC application, the on-line optimization is moved into a nonlinear programming strategy using the control vector parameterization approach (CVP). Some simulation results obtained on a fed-batch E. Coli bioreactor validate the efficiency of the proposed control strategy.

Towards nonlinear model predictive control of fed-batch cultures of E. Coli

This paper proposes the design of a nonlinear model predictive control (NMPC) scheme for the regulation of the acetic acid concentration (leading to the biomass growth maximization) during high cell density fed-batch cultures of Escherichia coli. For NMPC purposes, an unstructured model for the growth is first derived with respect to the major metabolic pathways: oxidative growth on glucose, fermentative growth on glucose and oxidative growth on acetic acid. To avoid complexity in the application of the traditional version of NMPC to this process, the on-line optimization then proceeds using the control vector parameterization (CVP) approach. Finally, some simulation results are given to illustrate the efficiency of the proposed control strategy.

TOWARDS A GENERIC MODEL STRUCTURE OF BIOREACTORS

Abstract Model-based control of bioprocesses is a difficult task, mainly due to the associated modelling challenges. Therefore, robust controllers are a valuable solution, which efficiently allows for model uncertainties. Simplified generic models can thus be considered during controller design phases. In this direction, the paper proposes a methodological contribution towards a simplified batch or fed-batch bioreactor model structure. The first step defines a general straightforward framework of bioreactor model, based on its dynamical behaviour. The second characterises the specific growth rate of each process variable by a specified kinetics. This structure is validated in the particular yeast and penicillin production cases.