Exploiting meteorological forecasts for the optimal operation of algal ponds

Abstract

Biofuel production from microalgae requires optimizing the operation of cultivation systems (i.e. outdoor raceway ponds) for this process to be economically sustainable. Controlling algal ponds is complex as the cultivation systems are exposed to fluctuating conditions. The strategy investigated in this study uses weather forecasts coupled to a predictive model of algal productivity to optimize pond operation. Productivity was optimized by dynamically controlling rates of fresh medium injection and culture removal into and from the pond. This optimization strategy when applied to a cultivation plant in Nice, South of France, increases the productivity by 2.13 compared to the reference case where the pond depth and dilution rate were kept constant over time. The underlying Model Predictive Control consists of playing with raceway pond thermal inertia and supplying of fresh water to reach rapidly optimal temperature, and then keep a balance between photosynthesis and respiration in the darkest layers of the raceway pond. The meteorological inaccuracy for forecasts beyond 24h was compensated by frequent updates of the optimal control problem. Finally, this scheme turned out to be robust to inaccurate weather forecasts, and the net productivity value reached was close to the productivity obtained for perfectly known meteorology.