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DTSTART;TZID=America/Los_Angeles:20250318T120000
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SUMMARY:Implementing Machine Learning Process Controls at Water Resource Recovery Facilities
DESCRIPTION: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. \nThis 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. \nThe 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. \nPresenters: \n\nKathryn Newhart\, Assistant Professor\, Oregon State University\nJoe Lybik\, PhD Candidate\, University of Michigan\nJeff Sparks\, Director of Digital Water\, Hampton Roads Sanitation District\nRudy Maltos\, Associate Engineer\, Metro Water Recovery\nNam Ngo\, Program Manager\, Research\, DC Water\n\nModerators: \n\nJeff Moeller\, Director of Research Services\, The Water Research Foundation\nNancy Love\, Professor\, University of Michigan
URL:https://waterprogramportal.org/event/implementing-machine-learning-process-controls-at-water-resource-recovery-facilities/
LOCATION:Virtual
CATEGORIES:Webinar
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DTSTART;TZID=America/Los_Angeles:20250319T150000
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CREATED:20250311T150959Z
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SUMMARY:Addressing Emerging Contaminants in Wastewater Treatment Systems
DESCRIPTION:The efficacy of treatment systems in removing a range of emerging contaminants\, such as microplastics\, pharmaceuticals and personal care products\, harmful algal blooms\, 1-4-dioxane\, and 6-PPD\, from water is often unclear. States have expressed interest in understanding removal rates and viable treatment technologies to address public health concerns. This webinar will provide an overview of work by our Office of Research and Development to develop a framework of wastewater treatment processes\, focusing on removal rates of certain emerging contaminants\, as well as associated life cycle costs and environmental impacts. This work will give states and communities more resources to understand how to treat contaminants in their water systems\, helping ensure clean water for all Americans.
URL:https://waterprogramportal.org/event/addressing-emerging-contaminants-in-wastewater-treatment-systems/
LOCATION:Virtual
CATEGORIES:Webinar
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