Adaptive Algal Cultivation Enabled by a Monthly Biomass Forecasting System

Area of Science:

• Sustainable biofuels and bioproducts
• Algal cultivation and biomass production
• Environmental data assimilation and forecasting

Background:

• Outdoor microalgae cultivation faces challenges due to environmental variability, impacting biofuel and bioproduct development.
• Accurate forecasting is needed to optimize operational decisions like strain selection and pond management.
• Existing methods lack sufficient accuracy for adaptive microalgae farming.

Purpose of the Study:

• To develop and evaluate an experimental monthly biomass forecasting system for microalgae cultivation.
• To guide operational decisions, including optimal strain and pond depth selection.
• To enhance the adaptive capacity and weather resilience of algal production systems.

Main Methods:

• Utilized the Biomass Assessment Tool (BAT) with climatology-based (NLDAS-2) and multi-model ensemble (NMME) forecasting approaches.
• Evaluated biomass production strategies for two algal strains (Picochlorum celeri, Tetraselmis striata) across four pond depths (15-30 cm) in Arizona (2020-2024).
• Compared forecasting model accuracy in predicting optimal strain and pond depth against actual biomass yields.

Main Results:

• One NMME model achieved 84% accuracy in identifying optimal strain and pond depth monthly; NMME suite accuracies ranged from 74% to 84%.
• NLDAS-2 climatology-based approach yielded 78% accuracy; strain selection was more accurate (up to 92%) than pond depth selection.
• Forecast-informed strategies increased average biomass yields by 15% compared to the State-of-Technology, with some months exceeding 40% gains.

Conclusions:

• Forecast-guided strategies significantly enhance microalgae biomass production and operational efficiency.
• The developed system offers a scalable and flexible tool for adaptive, weather-resilient algal cultivation.
• This approach is crucial for advancing sustainable biofuel and bioproduct development under variable environmental conditions.