Sullivan, Timothy P

Sullivan, Timothy P

Timothy P. Sullivan is a Collections Manager with Veolia North America, based out of Connecticut.

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About Sullivan, Timothy P

Timothy P. Sullivan is a Collections Manager with Veolia North America, based out of Connecticut.

Sullivan, Timothy P

Timothy P. Sullivan is a Collections Manager with Veolia North America, based out of Connecticut.

Titles from this speaker

Building the Foundation for Collection System AI - Norwalk, CT

Abstract

Building the Foundation for Collection System AI - Norwalk, CT Purpose As regulatory and financial climates continue to bring new levels of rigor to the wastewater collection space, utilities are challenged to make technological decisions related to operational needs and compliance requirements. A significant interest in data visualization and emerging smart technologies has flooded the market but is combined with ever growing threats of cybersecurity. These issues have been a point of focus along with a growing concern over the loss of institutional knowledge from the retirement of experienced staff known as the 'silver tsunami' that is projected over the next 5 years. In addition to these challenges, operators are faced with overwhelming amounts of data which is beyond their available resources to analyze. These concerns have driven the need for AI to be a necessary tool in the operations and maintenance of wastewater collection systems. As technology continues to advance it will continue to build on the successes it has already made to efficiency, reliability, sustainability, and customer service. For example, Geographic Information Systems (GIS) have made spatial analysis for utility networks more productive, with offerings that can manage projects from sewer jetting and cleaning operations to customer service. Smart manhole systems have brought more visibility to collection system operations leading to SSO reduction and acoustic vibration technology can now be used to rapidly assess sewer line conditions. These are just a few, with many more software and hardware solutions available in the market for integration, real-time analytics, and decision-making for optimal infrastructure, operations, and emergency response. The speed of technological advancement is not going to slow down. With over 1.35 million tech startups, the Global AI market is expected to reach $267 billion by 2027. It is estimated that by 2025, 38.6 billion smart devices will be collecting, analyzing, and sharing data. Utilities are adapting to these technological changes and looking for solutions to leverage data most efficiently. But what is AI and how can it help to optimize collection system operations and maintenance? One big way AI solutions can help is by processing data and providing recommendations that can be modeled or simulated with a 'digital twin'. This provides a great resource to best utilize the available data and analyze historical events providing output on how to manage and maintain the collection system during future events. The technology can generate simulations to predict extreme flow conditions and where the system is strained as flows escalate. These simulations can also be used for modeling the potential impacts of climate change to plan for optimal storm resiliency. As with SCADA, an AI platform requires quality data to be effective. The term 'garbage in equals garbage out' is particularly relevant with AI and will require operators to conduct a comprehensive analysis of their existing systems and think about what outputs they desire from AI. The availability of sensors, and data collection is a critical component and can be expensive and time consuming if not in place. Multiple data formats and interface platforms can also pose challenges. The presentation will use the City of Norwalk, CT as a case study on the steps taken to prepare for the future and build the foundation for AI. The City of Norwalk is located 70 miles southwest of Hartford, CT on the Long Island Sound. The City is a coastal community with over 90,000 residents with a large focus on the quality of their harbor, boating and swimming, and a thriving oyster industry. The wastewater collection system contains over 210 miles of gravity sewer lines and 23 pumping stations which convey flow to the 18 MGD WWTF. Like many older New England cities, the Norwalk system experiences increases in system flow during rain events which are carefully monitored by the existing systems and experienced staff. Through regular expansion, the City has built an outstanding SCADA system which provides comprehensive control and monitoring of the WWTF and pump stations. The City had a vision to extend their SCADA system into the collection system through the use of smart technologies. Beginning with a CCTV program, the City has continuously evaluated and integrated innovative technologies that provide valuable data from the collection system. The presentation will include a detailed evaluation of collections system technologies and data sources which have been implemented and are available for use with AI including: SCADA NASSCO PACP GIS / GPS Smart manhole covers Acoustic sewer line inspection Jetting and cleaning maintenance Manhole inspection program CCTV video management Force main evaluations Hydraulic modeling data Weather station data Historic weather and tide information Cybersecurity To capitalize on technological advancements, organizations must develop a methodology to assess, implement, and support its utilization and adoption of these technologies and what outputs are expected. Operators should be prepared to answer the question, What do you want from AI and do we have reliable input to analyze. The presentation will also include examples of output needs from AI including: Projection of flows during storm events.. Identification of priority areas based on projections. Recommendations for setpoint adjustments to collection system assets to prepare for flow increases. Development of 'simulations' to analyze the impacts of climate change. Capture and utilization of institutional knowledge for future operations and maintenance. Benefits of Presentation Discuss the applicability of AI in collection system optimization and climate resiliency. Engage utilities and operators to begin preparing for AI implementation. Emphasize the development of a methodology to assess, implement, and support its utilization and adoption of technologies. Provide case study of technology selection and methodology used in Norwalk, CT. Discuss methodology for determining output needs. Status of Completion The City has completed the assessment, implementation, and support milestone of its utilization and adoption of technologies. The City has now begun to evaluate AI technology options and implementation strategies. Conclusion AI can provide a valuable tool for operators of wastewater collection systems particularly those impacted by tidal and storm events. The technology can learn from historic events and develop accurate modeling for future storms and the impacts of climate change. To capitalize on technological advancements, organizations must develop a methodology to assess, implement, and support its utilization and adoption of these technologies. As with SCADA, an AI platform requires quality data to be effective and will require operators to conduct a comprehensive analysis of their existing systems and think about what outputs they desire from AI. Operators should be engaged in the planning and development process to transfer institutional knowledge. Operators and local staff can begin the process of validating inputs and data now to prepare for AI implementation as shown in the Norwalk, CT case study.

This paper was presented at the WEF Collection Systems and Stormwater Conference, April 9-12, 2024.

SpeakerSullivan, Timothy P

Presentation time

10:45:00

11:15:00

Session time

10:45:00

11:45:00

SessionSmart Collection System Tools

Session number17

Session locationConnecticut Convention Center, Hartford, Connecticut

TopicArtificial Intelligence, Coastal Systems, Combined Sewer Overflow, Digital Twin, Flow Projections, GIS, Machine Learning, Predictive Analytics

Author(s)

Winfree, Clure

Author(s)C. Winfree1, T. Sullivan1, N. Bhalerao1

Author affiliation(s)Veolia 1

SourceProceedings of the Water Environment Federation

Document typeConference Paper

PublisherWater Environment Federation

Print publication date Apr 2024

DOI10.2175/193864718825159408

Volume / Issue

Content sourceCollection Systems and Stormwater Conference

Word count11

Base text size -

Sullivan, Timothy P

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