This thesis deals with the optimization of carbon dioxide consumption by microalgae. Indeed, following several current environmental issues primarily related to large emissions of CO2, it is shown that microalgae represent a very promising solution for CO2 mitigation. From this perspective, we are interested in the optimization strategy of CO2 consumption through the development of a robust control law. The main aim is to ensure optimal operating conditions for a Chlorella vulgaris culture in an instrumented photobioreactor. The thesis is based on three major axes. The first one concerns growth modeling of the selected species based on a mathematical model reflecting the influence of light and total inorganic carbon concentration. For the control context, the second axis is related to biomass estimation from the real-time measurement of dissolved carbon dioxide. This step is necessary for the control part due to the lack of affordable real-time sensors for this kind of measurement. Three observers structures have been studied and compared: an extended Kalman filter, an asymptotic observer and an interval observer. The last axis deals with the implementation of a non-linear predictive control law coupled to the estimation strategy for the regulation of the cellular concentration around a value which maximizes the CO2 consumption. Performance and robustness of this control law have been validated in simulation and experimentally on a laboratory-scale instrumented photobioreactor. This thesis represents a preliminary study for the optimization of CO2 mitigation strategy by microalgae.
