CAREER: Mechanism-Driven Machine Learning for Water and Wastewater Treatment

Clean and reliable water treatment is critical to protect public health. However, most treatment decisions still rely on simple models that do not reflect real‑world conditions. Artificial intelligence (AI) has the potential to help engineers make more informed decisions. This CAREER project will develop AI models that help engineers understand how and why treatment processes work at full-scale. The project will reduce risk, improve reliability, and expand access to advanced tools for communities with limited technical resources. The outcomes of this research will be shared with water utilities and used across many treatment systems. The project will also address a national need for a workforce that can use AI responsibly by integrating data science into environmental engineering education. This project will support safer water systems, prepare future engineers, and show how AI can be used as a tool for scientific discovery. This CAREER project will develop an application‑driven AI framework for modeling engineered environmental systems, using water and wastewater disinfection as a representative, high‑risk unit process. The research will integrate multi‑facility operational and water quality data with hybrid modeling approaches that combine physics‑based process models and machine learning (ML). These approaches will include mechanistic ordinary differential equation models coupled with ML components, physics‑informed neural networks, and embedded neural differential equation formulations that constrain learning using known physical, chemical, and biological relationships. Model development and evaluation will explicitly address challenges common to environmental datasets, including data sparsity, autocorrelation, measurement uncertainty, and site‑specific variability, through time‑aware validation, uncertainty quantification, and risk‑based performance metrics. Mechanistic insights inferred from the models will be tested using a pilot‑scale disinfection system to distinguish true process behavior from artifacts introduced by data collection or modeling practices. The project will also develop protocols for model reuse and adaptation using transfer learning and privacy‑preserving federated learning, enabling models trained on multi‑facility data to be applied in data‑limited systems without sharing raw data. Together, these methods will advance the scientific use of AI in environmental engineering by enabling mechanistically grounded discovery, improving generalizability across real systems, and establishing a foundation for trustworthy, reusable models for infrastructure applications. 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: 2543135 | Program: 01002627DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: Kathryn Newhart | Institution: Oregon State University, CORVALLIS, OR | Award Amount: $550,000 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2543135 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2543135.html