ERI: Mechanistic Insights into Process-Microbial Interactions in Partial Denitrification-Anammox Systems (PD/A) for High-Nitrate Wastewater Treatment

Clean water is essential for public health. However, cleaning wastewater is energy intensive and uses expensive chemicals. This project studies a new, more efficient way to remove nitrogen from industrial wastewater using special bacteria. Like other organisms, the bacteria are sensitive to changes in their environment. This research combines engineering, biotechnology, and artificial intelligence to learn how these bacteria behave under different conditions. The goal is to create computer models that can predict how well the system works in real time. This research helps create better, low-cost tools for water treatment plants. In addition, the project trains the next generation of engineers and scientists in process engineering and microbiology and teaches local k-12 students about the science of clean water. This research establishes an integrated experimental-computational framework to characterize and predict the performance of partial denitrification-anammox (PD/A) systems. The study utilizes a novel interdisciplinary approach that combines microbial ecology, environmental process engineering, and machine learning to evaluate how operational parameters (including carbon-to-nitrogen ratios, temperature, and substrate stoichiometry) shape microbial community dynamics and process performance. Bench-scale bioreactors and high-throughput metagenomic sequencing link taxonomic shifts and functional gene expression directly to nitrogen removal efficiency. These biological and operational datasets serve as inputs for predictive modeling using ensemble machine-learning algorithms to identify microbial indicators of system stability. Through interpretable machine learning, the project elucidates the competitive and cooperative interactions between denitrifiers and anammox bacteria under realistic environmental stressors. This data-driven methodology generates predictive tools that bridge the gap between microbial theory and applied engineering. The outcomes advance the design of low-input, microbiome-informed biotechnology for sustainable, high-nitrate wastewater management. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. NSF Award ID: 2553311 | Program: 01002627DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: Juliana Abraham | Institution: Stevens Institute of Technology, HOBOKEN, NJ | Award Amount: $199,972 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2553311 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2553311.html