Adaptive Control Frameworks to Unlock Full-Scale BNR Potential (Request for Proposal)

Description: 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. Applicants may request up to $300,000 in WRF funds for this project.