Leveraging Big Data Platforms in Wastewater to Accelerate Machine Learning at a Regional Scale

Presentation Description: This paper describes successful approaches taken for developing and deploying wide-scale machine learning algorithms in the water and wastewater industry. This includes lessons on the level of effort, investment, challenges, and benefits. Two case studies are presented: Agua Nueva Water Reclamation Facility (WRF) that focuses on reducing aeration energy cost and nutrient management, as well as Wilmington wastewater treatment facility (WWTF) with a focus on disinfection chemical optimization.
The primary objectives of these case studies were to:
1. Increase operator engagement with tools developed by data scientists and process engineers to model the complexities of treatment facilities and translate those models into actionable workflows that operators can use to make their jobs easier while improving plant efficiency with chemical and power usage. Discussion will present how lead Jacobs operator at Agua Nueva and Wilmington leverages these tools developed by data scientist and process engineers to facilitate savings.
2. Build technical infrastructure that involves heavy data engineering to configuring data connectors and pipelines to ingest SCADA historian data, maintenance data, HACH WIMs data, and financial data all into a single unified platform and using data pipelines and cloud technology.
3. Develop and deploy machine learning that ingest all this information and provide actionable insights and recommendations to operators to fully leverage big data, cloud technology, data science, and wastewater subject matter expertise.
4. Optimize facilities for chemical and power usage using data science and machine learning.
5. Continuous monitoring – Monitor models for continued performance checks, monitor lab data for compliance, provide all stakeholders with monthly reporting on events, and ongoing support via call center.

These case-study demonstrates significant progress in the successful implementation of AI at a treatment facility with savings between 15-30%. These new workflows also aid in the empowerment of treatment plant operators and provide data-driven decisions in real-time allows them to address challenges, prevent potential failures, but more importantly, allow them to focus on more day-to-day tasks that keeps the critical infrastructures running.