Enhancing pH control in microalgae raceway photobioreactors using 3DoF-KF model-on-demand model predictive control

Microalgae have gained increasing recognition as a sustainable resource with diverse applications ranging from biofuel production to wastewater treatment. Raceway reactors, the most widespread microalgae production system, are vulnerable to environmental fluctuations and external contamination, which can negatively impact microalgal growth and increase the emphasis on culture conditions, especially pH. In this study, a three-degree-of-freedom model predictive controller is presented, which is designed to regulate pH in raceway reactors. The controller employs a Model-on-Demand (MoD) approach for real-time data-driven estimation by using a database generated by exciting the system with control-relevant multisine signals. The estimated models demonstrate accurate goodness of fit across a wide range of prediction horizons, outperforming similar linear models. The controller formulation offers significant flexibility, enabling users to independently tune the speeds of setpoint tracking, measured disturbance rejection, and unmeasured disturbance rejection. The experimental results achieved in a pilot facility demonstrate that the proposed methodology is intuitive, straightforward and highly effective in controlling microalgae production systems.