A Practical Sewer Network Digital Twin: It is not a PIPE dream!

A Practical Sewer Network Digital Twin: It is not a PIPE dream!AbstractIntroduction Most major municipal areas in the United States are served by a combination of sanitary sewers, separate storm sewers, and combined sewers. With growing populations across urban areas, many sewer utilities are struggling to manage stormwater-related challenges such as flash flooding and combined sewer overflow (CSOs). Furthermore, inflow and infiltration (I&I) issues can cause damage to collection systems and increase the peak flow to wastewater treatment facilities leading to untreated wastewater and storm water being released to the environment including local waterways. This could result in their impairments and risks to public health. Several states have introduced public notification requirements to promote public awareness of sewage pollution with very short timeframes (within hours) for reporting. In order to achieve desired outcomes and get a real-time view of the collection systems, many utilities have invested in sensors and hydraulic models, but it remains a challenge to utilize these investments to comply with the legislation. Proactive utilities are looking to a digital-first resilient strategy which, not only allows them to comply with regulations, but also helps assess system performance, determine effective control strategies and streamline their operations and maintenance activities. Case Studies In this presentation, we will provide examples of sewer network digital twin (DT) (Figure 1) which brings in data from multiple sources (SCADA, IoT sensors, historical rainfall and weather forecasts, GIS, etc.) and uses hydraulic models and data analytics to deliver outcomes for sewer utilities. The presentation will include case studies using implementation of the digital twins for The Metropolitan District Hartford, Connecticut and The City of New Bedford, Massachusetts. This implementation of a sewer network DT platform has enabled the validation of false positive overflow readings through tying in maintenance activity data from CMMS, correct false positive notifications from flow meters by using a hydraulic model to validate overflows and provides a single dashboard combining all relevant data. The platform also uses machine learning to perform sensor data anomaly detection for short-term anomalies such as sensor drop-outs and maintenance-related issues and long-term anomalies such as sensor drift (Figure 2). The platform is able to use hydraulic models and weather forecasts to provide overflow forecasts enabling the utility to perform proactive maintenance and planning (Figure 3). Furthermore, use of hydraulic models in combination with sensor data enables utilities to perform data-driven maintenance activities (Figure 4). Public notifications can be automated through direct integration to the city's website and with a reverse 911 system which includes multilingual support. A capacity assessment tool enables the utility to leverage the hydraulic model to determine available capacity to make decisions on new permit requests. The platform is also able to drive pro-active maintenance by sending notifications to CMMS software. By providing a complete real-time and retrospective view of the sewer system, the sewer network DT can enable utilities to characterize the performance of their sewer network, prioritize maintenance and remediation needs and streamline regulatory reporting in one single platform.This paper was presented at the WEF Collection Systems and Stormwater Conference, April 9-12, 2024.SpeakerSrinivasan, VarunPresentation time14:30:0015:00:00Session time13:30:0015:00:00SessionAI and Digital TwinsSession number21Session locationConnecticut Convention Center, Hartford, ConnecticutTopicArtificial Intelligence, Collection Systems, Digital Twin, Emergency Preparedness And Response, Sensors, Smart Data Infrastructure, Smart Water Infrastructure, SSO Reduction, Utility of the Future, Wet WeatherTopicArtificial Intelligence, Collection Systems, Digital Twin, Emergency Preparedness And Response, Sensors, Smart Data Infrastructure, Smart Water Infrastructure, SSO Reduction, Utility of the Future, Wet WeatherAuthor(s)Srinivasan, VarunAuthor(s)V. Srinivasan1, J. Waterbury2, J. Costa3Author affiliation(s)Trinnex, 1; The Metropolitan District 2; City of New Bedford 3SourceProceedings of the Water Environment FederationDocument typeConference PaperPublisherWater Environment FederationPrint publication date Apr 2024DOI10.2175/193864718825159380Volume / Issue Content sourceCollection Systems and Stormwater ConferenceCopyright2024Word count13