Enhancing Biomass Productivity by Forecast-Informed Pond Operations.

Area of Science:

• * Agricultural Science
• * Biotechnology
• * Renewable Energy

Background:

• * Microalgal cultivation for biofuels and proteins faces economic challenges due to variable environmental conditions impacting biomass productivity.
• * Current practices rely on fixed dilution rates based on operator experience, which are suboptimal for variable conditions.

Purpose of the Study:

• * To introduce and validate a forecast-informed pond operation (FIPO) system for optimizing microalgal cultivation.
• * To enhance biomass production by using numerical weather prediction (NWP) ensemble forecasts to determine optimal daily dilution rates.

Main Methods:

• * Development of the FIPO system integrating numerical weather prediction (NWP) ensemble forecasts and the biomass assessment tool (BAT).
• * Experimental validation of FIPO in short-term and long-term microalgal growth scenarios.
• * Comparison of FIPO performance against batch growth and fixed dilution rate strategies.

Main Results:

• * FIPO increased short-term biomass production by 21.3% over batch growth and 7.4% over fixed dilution rates.
• * FIPO achieved biomass production comparable to using perfect weather forecasts, validating NWP accuracy for operations.
• * Long-term experiments showed FIPO increased biomass production by 13.3% and 17.8% compared to different fixed dilution rates.

Conclusions:

• * The FIPO system effectively optimizes microalgal cultivation by utilizing NWP forecasts to adjust daily dilution rates.
• * This approach enhances biomass yields and mitigates risks associated with environmental variability in microalgal production.
• * FIPO offers a viable strategy for improving the economic feasibility of commercial-scale microalgal cultivation.