In this study, the parameters of a dynamic model of cultures of the microalgae Scenedesmus obliquus are estimated from datasets collected in batch photobioreactors operated with various initial conditions and light illumination conditions. Measurements of biomass, nitrogen quota, bulk substrate concentration, as well as chlorophyll concentration are achieved, which allow the determination of parameters with satisfactory confidence intervals and model cross-validation against independent data. The dynamic model is then used as a predictor in a nonlinear model predictive control strategy where the dilution rate and the incident light intensity are simultaneously manipulated in order to optimize the cumulated algal biomass production.

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Cultures of hybridoma cells in bioreactors are commonly used to produce monoclonal antibodies. As an alternative to nonlinear model predictive control, which has recently been applied successfully to optimize the process productivity, a simpler control approach is developed in the present study. This strategy is based on a classical PI controller and software sensors for the reaction rates, which exploit the particular structure of the dynamic model. The dynamic behavior of the process can indeed be subdivided into four operating zones, depending on the overflow metabolism of the hybridoma cells. In addition, robustness toward model uncertainties and measurement noise is investigated.