M-ALBA: A modelling framework to guide the optimization of membrane-assisted algae-bacteria systems

Area of Science:

• Environmental Biotechnology
• Wastewater Treatment
• Microbial Ecology

Background:

• Biological systems combining microalgae and bacteria offer sustainable sanitation solutions, typically in high-rate algal-bacteria ponds (HRABP).
• HRABPs face limitations in effluent clarification and treatment load capacity.
• Membrane filtration can enhance HRABPs by retaining biomass and improving effluent quality, but the effects of decoupling SRT and HRT require further understanding.

Purpose of the Study:

• To predict the performance of a membrane-assisted high-rate algal-bacteria pond (HRABP) using a novel model.
• To investigate the impact of decoupling solid retention time (SRT) from hydraulic retention time (HRT) on system performance.
• To analyze the complex microalgae-bacteria dynamics within membrane-assisted HRABPs.

Main Methods:

• Development and application of the M-ALBA model, an extension of the ALBA model incorporating membrane separation.
• The model includes microalgae, heterotrophic bacteria, and nitrifying bacteria, with 34 state variables and 19 biological processes.
• Simulation of various scenarios by decoupling SRT (4.5-22.5 days) and HRT (0.5-4.5 days), validated with experimental data.

Main Results:

• Decoupling SRT from HRT significantly improves effluent quality by enhancing nitrogen removal.
• This decoupling strategy effectively avoids ammonia volatilization, a common issue in conventional systems.
• Optimal performance was achieved at a hydraulic retention time (HRT) of 1.5 days, demonstrating the benefit of lower HRT operation.

Conclusions:

• Membrane-assisted HRABPs offer a promising approach for advanced wastewater treatment and sustainable sanitation.
• Decoupling SRT and HRT is a key operational strategy to boost nitrogen removal efficiency and effluent quality.
• The M-ALBA model provides valuable insights into the complex microbial interactions and performance optimization of these systems.