Abstract
The industrial production of microalgae is a highly sustainable and attractive process due to its variety of applications, especially when it is combined with wastewater treatment. From a control point of view, the optimization of the process requires a considerable effort regarding the culture conditions. The biological nature of the process renders its characterization considerably difficult, as well as the acquisition of models describing its dynamics. In this work, a complete methodology for pH control in raceway photobioreactors based on the Practical Nonlinear Model Predictive Control (PNMPC) approach is proposed. Models based on artificial Neural Networks (ANN) of the system have been obtained and validated, and a PNMPC controller based on these data-driven models has been developed and tested in a real raceway reactor. The results demonstrate the reliability of this type of models, and justify the possibility of combining them with predictive control algorithms in highly nonlinear systems.
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