The optimization of biomass production in a microalgae-based biorefinery can highly contribute to the generation of new clean resources and reduction of the industrial pollution. Pollution can be reduced at the source, by removing contaminants from wastewater, reusing reclaimed water, fixing CO 2 and thus contributing to its mitigation, and recovering useful bioproducts. On the other hand, microalgae are seen as bioprocesses with a relevant potential biomass-biofuel- biogas production. Moreover, an adequate microalgae-based biorefinery is sustainable both environmentally and economically, and has socioeconomic benefits, due to the relevance of the target bioproducts. As an essential component of a circular economy, wastewater use, biomass, biogas, biodiesel, and by-product recovery can generate new business opportunities and help to recover the costs of new, innovative, and adapted installations, allowing for the recovery of energy and nutrient expenses.
The process for microalgae biomass production presents complex and strongly nonlinear dynamics. Thus, these processes require adequate optimization control approaches to deal with these problems and reach the optimal operating points. The use of data-based and hybrid control solutions combined with MPC, event based, and robust control strategies can solve a number of these specific problems, as pointed out in the state-of-the-art section where it has been justified how these different approaches cope with the different challenges faced for optimizing the sustainable biorefinery. The organization of the tasks deals with these challenges.
The development of new results for the modeling, identification, and control algorithms of microalgae biomass production would imply a significant improvement in the operation, efficiency, and safety of this class of relevant systems. Due to the incomplete nature of the results on this relevant topic, the methodology and strategies resulting from this project are of potential great relevance in this emerging field. As the obtained results will be validated in real processes and facilities, the technology transfer of control strategies to the industry will be greatly facilitated.