Chlamydomonas reinhardtii is a green microalga capable of turning its metabolism towards H2 production under specific conditions. However this H2 production, narrowly linked to the photosynthetic process, results from complex metabolic reactions highly dependent on the environmental conditions of the cells. A kinetic model has been developed to relate culture evolution from standard photosynthetic growth to H2 producing cells. It represents transition in sulfur-deprived conditions, known to lead to H2 production in Chlamydomonas reinhardtii, and the two main processes then induced which are an over-accumulation of intracellular starch and a progressive reduction of PSII activity for anoxia achievement. Because these phenomena are directly linked to the photosynthetic growth, two kinetic models were associated, the first (one) introducing light dependency (Haldane type model associated to a radiative light transfer model), the second (one) making growth a function of available sulfur amount under extracellular and intracellular forms (Droop formulation). The model parameters identification was realized from experimental data obtained with especially designed experiments and a sensitivity analysis of the model to its parameters was also conducted. Model behavior was finally studied showing interdependency between light transfer conditions, photosynthetic growth, sulfate uptake, photosynthetic activity and O2 release, during transition from oxygenic growth to anoxic H2 production conditions.

A constraint-based modeling approach was developed to investigate the metabolic response of the eukaryotic microalgae Chlamydomonas reinhardtii under photoautotrophic conditions. The model explicitly includes thermodynamic and energetic constraints on the functioning metabolism. A mixed integer linear programming method was used to determine the optimal flux distributions with regard to this set of constraints. It enabled us, in particular, to highlight the existence of a light-driven respiration depending on the incident photon flux density in photobioreactors functioning in physical light limitation.

No abstract

Industrial filamentous fungal fermentations are typically operated in fed-batch mode. Oxygen control represents an important operational challenge due to the varying biomass concentration. In this study, oxygen control is implemented by manipulating the substrate feed rate, i.e. the rate of oxygen consumption. It turns out that the setpoint for dissolved oxygen represents a trade-off since a low dissolved oxygen value favors productivity but can also induce oxygen limitation. This paper addresses the regulation of dissolved oxygen using a cascade control scheme that incorporates auxiliary measurements to improve the control performance. The computation of an appropriate setpoint profile for dissolved oxygen is solved via process optimization. For that purpose, an existing morphologically structured model is extended to include the effects of both low levels of oxygen on growth and medium rheological properties on oxygen transfer. Experimental results obtained at the industrial pilot-scale level confirm the efficiency of the proposed control strategy but also illustrate the shortcomings of the process model at hand for optimizing the dissolved oxygen setpoints.

International audienceAdvanced monitoring, fault detection, automatic control and optimisation of the beer fermentation process require on-line prediction and off-line simulation of key variables. Three dynamic models for the beer fermentation process are proposed and validated in laboratory scale: a model based on biological knowledge of the fermentation process, an empirical model based on the shape of the experimental curves and a black-box model based on an artificial neural network. The models take into account the fermentation temperature, the top pressure and the initial yeast concentration, and predict the wort density, the residual sugar concentration, the ethanol concentration, and the released CO 2. The models were compared in terms of prediction accuracy, robustness and generalisation ability (interpolation and extrapolation), reliability of parameter identification and interpretation of the parameter values. Not surprisingly, in the case of a relatively limited experimental data (10 experiments in various operating conditions), models that include more process knowledge appear equally accurate but more reliable than the neural network. The achieved prediction accuracy was 5% for the released CO 2 volume, 10% for the density and the ethanol concentration and 20% for the residual sugar concentration

A dynamic model for the photoautotrophic growth of microalgae in photobioreactor that describes the main variables of the system and allows the precise prediction of the pH in the culture was proposed and validated. The dynamic behavior of the biological system was expressed through a multistate model in continuous-time formulation, based on massbalance equations and local photosynthetic responses of the anisotropic medium, further associated with a set of algebraic equations that describes the thermodynamic properties of the ammonia-carbon dioxide-water ternary solute system. The global photoautotrophic growth model was validated on experimental data acquired from a torus reactor inoculated with Chlamydomonas reinhardtii cells. The model response was studied in simulation for all identified input variables (dilution rate, incident light intensity, temperature, and flow rates of input gases).

No abstract

No abstract