PNNL

Abstract

Microalgae have received increasing attention as a potential feedstock for biofuel or biobased products. Forecasting the microalgae growth is beneficial for managers in planning pond operations and harvesting decisions. This study proposed a biomass forecasting system comprised of the Biomass Growth Model, the Modular Aquatic Simulation System in Two Dimensions, ensemble data assimilation, and numerical weather prediction Global Ensemble Forecast System ensemble meteorological forecasts. This study introduces the theory behind the proposed integrated biomass forecasting system, with an application undertaken in pseudo-real-time in three outdoor ponds cultured with Chlorella sorokiniana in Delhi, California, U.S. Results from all three case studies demonstrate that the biomass forecasting system improved the short-term (i.e., 7-day) biomass forecasting skills by about 60% on average. Given the satisfactory performances achieved in this study, it is probable that the biomass forecasting system can be used operationally to inform managers in making pond operation and harvesting planning decisions.