The cultivation of micro-algae in photobioreactors offers a promising approach for the recovery of nutrients and other products from domestic wastewater, as well carbon capture. In realizing the potential of this approach, a number of challenges remain, and the goal of the research proposed here is to link microbial community structure, interactions and function to photobioreactor performance using machine learning approaches.
To this end, we will harness novel insights provided by data analytics to (1) determine how process parameters, including reactor type/configuration and operating conditions, can be optimized to improve performance and stability, (2) understand how co-cultures and consortia of microorganisms can be structured to augment recovery, and (3) explore how the impact of inhibitory compounds (e.g., antibiotics) in domestic wastewater can be negated or reduced.
To this end, we will harness novel insights provided by data analytics to (1) determine how process parameters, including reactor type/configuration and operating conditions, can be optimized to improve performance and stability, (2) understand how co-cultures and consortia of microorganisms can be structured to augment recovery, and (3) explore how the impact of inhibitory compounds (e.g., antibiotics) in domestic wastewater can be negated or reduced.
Researchers: Dr. Walker, Civil Eng. & Data Science faculty TBD