Implementing Machine Learning Process Controls at Water Resource Recovery Facilities
Please log in to access this resource
This resource is only available to Public Plus or Subscriber accounts. If you are not a subscriber, you can register for a Public Plus account. Need to login as a Subscriber and unsure if you have login credentials? Try resetting your password. If we have that email on file, you will receive an automated email with a reset link. If, after resetting your password, you still don't have subscriber access, please Contact Us.
Do you have a Public+ account? Login Now
Associated Project
Improvements in process monitoring and control at water resource recovery facilities (WRRFs) could result in reductions in electricity consumption, chemical inputs, footprint, and greenhouse gas emissions, as well as increased energy and nutrient recovery and improved water quality. However, many current WRRF data collection, monitoring, and control approaches use 20th century process monitoring and control systems.
This webcast is designed for wastewater professionals looking to increase understanding of machine learning (ML) and how to implement data-driven process controls at WRRFs. The webcast features results from a U.S. Department of Energy-funded project Data-Driven Process Control for Maximizing Resource Efficiency (5141) which has developed and demonstrated data-driven process controls at full-scale facilities for a variety of applications that collectively provide a whole plant approach and offer substantial energy and resource recovery benefits.
The webcast will provide an overview of what artificial intelligence (AI) and ML are, including basic terminology. Participants will learn the steps necessary for ML control implementation based on a framework developed by the project team. Case studies will be presented to illustrate the steps involved. The presenters will also highlight an ML Toolkit that the project created, which includes project write-ups, code notebooks, video walkthroughs, and other resources for users.
Presenters:
- Kathryn Newhart, Assistant Professor, Oregon State University
- Joe Lybik, PhD Candidate, University of Michigan
- Jeff Sparks, Director of Digital Water, Hampton Roads Sanitation District
- Rudy Maltos, Associate Engineer, Metro Water Recovery
- Nam Ngo, Program Manager, Research, DC Water
Moderators:
- Jeff Moeller, Director of Research Services, The Water Research Foundation
- Nancy Love, Professor, University of Michigan