Exploiting meteorological forecasts for the optimal operation of algal ponds

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