A learning-based model predictive strategy for pH control in raceway photobioreactors with freshwater and wastewater cultivation media

This article presents a study on the pH control of raceway photobioreactors (PBRs) using a learning-based model predictive control (LBMPC) approach. The primary objective is to enhance the control performance and improve microalgae biomass production. The research builds upon existing control solutions for PBRs found in the literature by integrating the robust and adaptive capabilities of the LBMPC. Specifically, the control algorithm is modified to include a pre-established Linear Quadratic Tracking with a Feedforward controller, facilitating the formulation of the conventional LBMPC. To validate the proposed controller, implementation is performed in an existing facility at the IFAPA center, located near the University of Almeria in Southern Spain. The experiment involves two parallel PBRs operating under the same meteorological conditions with different microalgae strains and media (freshwater and wastewater). The LBMPC demonstrates satisfactory results and is compared with the conventional nominal MPC strategy through an analogous experiment. The LBMPC outperforms the conventional approach, achieving up to four times superior performance in terms of the average error index. The results highlight the importance of employing robust adaptive control strategies for highly nonlinear and multi-disturbed systems like the variant biological-chemical microalgae process in PBRs since the complexity associated with adapting the system's model can negatively impact the performance of conventional model-based controllers.