Improvements in process monitoring and control at water resource recovery facilities (WRRFs) could reduce electricity consumption, chemical inputs, and greenhouse gas emissions, while improving energy recovery. Many WRRF data collection, monitoring, and control approaches use 20th century process monitoring and control systems, which require large design safety factors to ensure reliability in the absence of more advanced, precise controls. Implementation of modern data-driven control tools could lead to more efficient operations that provide intrinsic reliability with better overall process performance at full-scale.
This project was funded through the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy.
Overview
The overall goals of this project were to (1) develop and demonstrate data-driven process controls at full-scale facilities for five promising WRRF applications (see below) that provide whole plant approaches and offer substantial energy and resource recovery benefits, and (2) create a Machine Learning (ML) Toolkit of data-driven process control approaches and an implementation guidance for accelerated adoption at WRRFs across the United States.
The five WRRF applications were:
1. Carbon diversion: high-rate contact stabilization
2. Biological nutrient removal (BNR): ammonium-based aeration control (ABAC) / ammonia vs. NOx (AvN) + partial denitrification with anammox (PdNA)
3. Disinfection: peracetic acid (PAA)
4. Phosphorus-recovery: MagPrex
5. Holistic biosolids optimization
Funding Information
- DOE Grant Number: DE-EE0009508
- Years Funded: 2012–2025
- Total Project Funding: $2,274,039
- WRF Project: Data-Driven Process Control for Maximizing Resource Efficiency (5141)
Project Team
- The Water Research Foundation, Jeff Moeller (PI)
- Hampton Roads Sanitation District, Charles Bott and Stephanie Klaus (Co-PI)
- DC Water, Haydee De Clippeleir (Co-PI)
- Metro Water Recovery, Josh Goldman and Rudy Maltos
- Oregon State University, Kathryn (Kate) Newhart (Co-PI)
- University of Michigan, Nancy Love (Co-PI) and Branko Kerkez (Co-PI)
- Northwestern University, George Wells (Co-PI)
- Oak Ridge National Laboratory, Kris Villez
- Black & Veatch, Andrew Shaw