New model predicts algae-bacteria interactions in wastewater, boosting biomass yield and treatment efficiency

A team of researchers has developed a groundbreaking kinetic model that captures the complex dynamics between microalgae and bacteria in anaerobic digestion (AD) wastewater. The model, named ADBA (Anaerobic Digestion Bacteria Algae), is the first to simulate mixotrophic algal growth—where algae use both organic and inorganic carbon—while accounting for bacterial competition, light inhibition, and effluent turbidity.

Unlike previous models that often rely on synthetic media or assume autotrophic growth, ADBA is calibrated and validated using real AD effluent. The model focuses on Chlorella vulgaris CA1, a strain native to AD environments, and its interactions with heterotrophic bacteria. By incorporating both Monod and Haldane kinetics, ADBA describes how algae switch between autotrophic and heterotrophic modes depending on light and carbon availability.

The model introduces two novel parameters: the algae-bacteria interaction exponent and the effluent turbidity inhibition coefficient. These allow ADBA to quantify how bacterial presence and suspended particles in wastewater reduce algal growth. Simulations show that bacterial competition and turbidity can reduce algal productivity by up to 50%, especially under low light conditions. Conversely, sterilization and dilution of effluent can more than double biomass yield.

ADBA also predicts nutrient removal with high accuracy. In simulated scenarios, algae removed up to 80% of ammonium-nitrogen and 90% of organic carbon from the effluent. The model’s sensitivity analysis identified key parameters such as maximum growth rates, decay rates, and gas-liquid mass transfer coefficients as most influential, guiding future process optimization.

With only 25 parameters and 8 state variables, ADBA is significantly simpler than many existing models, which often require over 100 parameters. This makes it more practical for real-world applications, such as designing algal wastewater treatment systems or predicting biomass yields for biofuel production.

The researchers emphasize that ADBA is not just a theoretical tool. It can be used to identify optimal operating conditions—such as light intensity, turbidity levels, and nutrient concentrations—to maximize algal growth and wastewater treatment efficiency. For example, the model suggests keeping effluent turbidity below 0.5 absorbance units and ammonium-nitrogen between 100–1000 mg/L for optimal heterotrophic algal growth.

Future work will explore long-term dynamics and test the model with different algal strains and carbon sources, such as acetate, which may further reduce bacterial contamination and enhance yield. The team also plans to integrate physical treatment methods like membrane filtration or dissolved air flotation to reduce turbidity and improve light penetration.

By bridging the gap between lab-scale experiments and full-scale wastewater systems, ADBA offers a scalable, cost-effective pathway for sustainable algae-based wastewater treatment and biomass production.