Grant

• Identify and categorize the dominant failure modes of current aeration and process control implementations under real-world operating conditions.
• Develop guidance on data and instrumentation requirements for utilities to support adaptive control, including the characterization of existing sensor infrastructure and the use of soft sensors where hard sensors are limited.
• Develop practical adaptive control frameworks and implementation guidance that utilities can deploy without dependence on proprietary black-box tools or specialized data science expertise, including hybrid approaches that augment conventional controls with data-driven models (e.g., artificial intelligence (AI)/machine learning (ML), digital twins) for variable flow and concentration conditions.
• Benchmark control performance against biological capacity indicators across multiple full-scale facilities to quantify the gap between achievable and realized biological nutrient removal (BNR) performance.

WRF Technical Contact: Harry Zhang, PhD, PE

For instructions on how to submit proposals, refer to these instructions or this video.

The Potential Participants listed at the bottom of this page have indicated interest in participating in this research. This information is updated frequently as utilities are encouraged to volunteer throughout the RFP cycle.