Optimization and Control of an Industrial Scale Multivariable Nonlinear Microalgae Fermentation

Abstract Different optimization and control techniques of an industrial microalgae fermentation have been studied in this work. The optimization techniques were verified in real time application on the fermenter and in addition in a theoretical analysis on the dynamical model of the fermentation process, which was also the subject for the verification of the different control algorithms. The optimization goal was to maximize the cellular productivity by using several techniques such as Pontryagin’s Maximum Principle, Feedback Sub-Optimal Policy and the Direct Search Method. The latter one has shown to be the preferable technique by applying it to the dynamical model of the fermentation. Real time Feedback Sub-Optimal Policy showed very good resemblance with the theoretical results. The reference conditions obtained from the optimization algorithms are the set points for the multi- variable controller. Linear State Variable Feedback has shown totally unsatisfactory behavior. Two advanced control techniques, Globally Linearizing Control and multivariable adaptive control have shown to perform very satisfactorily.