lastID = -10101553
Skip to main content Skip to top navigation Skip to site search
Top of page
  • My citations options
    Web Back (from Web)
    Chicago Back (from Chicago)
    MLA Back (from MLA)
Close action menu

You need to login to use this feature.

Please wait a moment…
Please wait while we update your results...
Please wait a moment...
Description: Access Water
Context Menu
Description: Using Artificial Intelligence to Optimize Sewer Preventative Maintenance
Using Artificial Intelligence to Optimize Sewer Preventative Maintenance
  • Browse
  • Compilations
    • Compilations list
  • Subscriptions
Tools

Related contents

Loading related content

Workflow

No linked records yet

X
  • Current: 2024-02-20 11:26:46 Adam Phillips
  • 2024-02-20 09:41:30 Adam Phillips
  • 2024-02-13 12:33:23 Adam Phillips Release
  • 2024-02-12 16:31:23 Adam Phillips
Description: Access Water
  • Browse
  • Compilations
  • Subscriptions
Log in
0
Accessibility Options

Base text size -

This is a sample piece of body text
Larger
Smaller
  • Shopping basket (0)
  • Accessibility options
  • Return to previous
Description: Using Artificial Intelligence to Optimize Sewer Preventative Maintenance
Using Artificial Intelligence to Optimize Sewer Preventative Maintenance

Using Artificial Intelligence to Optimize Sewer Preventative Maintenance

Using Artificial Intelligence to Optimize Sewer Preventative Maintenance

  • New
  • View
  • Details
  • Reader
  • Default
  • Share
  • Email
  • Facebook
  • Twitter
  • LinkedIn
  • New
  • View
  • Default view
  • Reader view
  • Data view
  • Details

This page cannot be printed from here

Please use the dedicated print option from the 'view' drop down menu located in the blue ribbon in the top, right section of the publication.

screenshot of print menu option

Description: Using Artificial Intelligence to Optimize Sewer Preventative Maintenance
Using Artificial Intelligence to Optimize Sewer Preventative Maintenance
Abstract
Introduction WSSC Water has 5,700 miles of sewer pipeline. Due to foreign debris such as fats, oils and grease, root intrusion, silt/sediment, and debris, pipelines need to be cleaned periodically to remove these elements which sometimes create blockages and thus avoid Sanitary Sewer Overflows (SSOs). Cleaning a sewer pipeline requires a scouring process, which over time, can degrade wall thickness and reduce pipe life. WSSC Water currently has preventative maintenance (PM) schedules for much of our sewer pipeline system. WSSC Water would like to optimize the sewer PM schedules to save money and to extend the life of our sewer pipelines. Sewer Maintenance Prediction technology can help optimize sewer PM activities by using Artificial Intelligence (AI) to recommend when cleaning is needed even before sewer levels reach an alarm state. The Sewer Maintenance Prediction Pilot tested two technologies, ADS Echo and SmartCover. These technologies can be used for comprehensive sewer performance monitoring, early warning and notification of impending overflows, SSO monitoring, and sewer capacity studies. However, this pilot focused on the technologies' ability to accurately provide early warning and notification of sewer blockages to predict when cleaning is required. Pilot Plan The year-long pilot installed two technologies in 10 manholes each. Each technology was assigned 9 sites located upstream of pipes with 3-month cleaning schedules and 1 site with a high scouring velocity with little to no maintenance issues to be used as a control. After 6 months at the 10 initial locations, some of the equipment was swapped and data was collected at the alternate locations. The swapped locations were those that had the most activity to eliminate any site-specific advantages there may have been. During the year of testing, data was collected to confirm the technologies accurately alarm at full pipe, 1 foot above bench, and 2 feet from top of frame as well as predict when cleaning is needed. The pilot would be considered successful if the technology was able to accurately alarm at pre-defined levels and predict the need for cleaning as verified by WSSC Water crews. Avoided costs associated with reduced cleaning was also considered as part of the evaluation. Pilot Results This presentation will explore the findings of the two technologies and offer practical solutions for wastewater utilities. These findings will be presented in a manner that allows utilities to extrapolate potential cost savings from the optimization of sewer pipeline cleaning using artificial intelligence within their own systems.
This paper was presented at the WEF/AWWA Utility Management Conference, February 13-16, 2024.
SpeakerTitus, Sara
Presentation time
13:30:00
15:00:00
Session time
13:30:00
15:00:00
SessionReal World Applications of Artificial Intelligence
Session number24
Session locationOregon Convention Center, Portland, Oregon
TopicDigital Transformation including AI and ChatGPT
TopicDigital Transformation including AI and ChatGPT
Author(s)
Titus, Sara
Author(s)S. Titus1
Author affiliation(s)Washington Suburban Sanitary Commission 1;
SourceProceedings of the Water Environment Federation
Document typeConference Paper
PublisherWater Environment Federation
Print publication date Feb 2024
DOI10.2175/193864718825159278
Volume / Issue
Content sourceUtility Management Conference
Word count9

Purchase price $11.50

Get access
Log in Purchase content Purchase subscription
You may already have access to this content if you have previously purchased this content or have a subscription.
Need to create an account?

You can purchase access to this content but you might want to consider a subscription for a wide variety of items at a substantial discount!

Purchase access to 'Using Artificial Intelligence to Optimize Sewer Preventative Maintenance'

Add to cart
Purchase a subscription to gain access to 18,000+ Proceeding Papers, 25+ Fact Sheets, 20+ Technical Reports, 50+ magazine articles and select Technical Publications' chapters.
Loading items
There are no items to display at the moment.
Something went wrong trying to load these items.
Description: Using Artificial Intelligence to Optimize Sewer Preventative Maintenance
Using Artificial Intelligence to Optimize Sewer Preventative Maintenance
Pricing
Non-member price: $11.50
Member price:
-10101553
Get access
-10101553
Log in Purchase content Purchase subscription
You may already have access to this content if you have previously purchased this content or have a subscription.
Need to create an account?

You can purchase access to this content but you might want to consider a subscription for a wide variety of items at a substantial discount!

Purchase access to 'Using Artificial Intelligence to Optimize Sewer Preventative Maintenance'

Add to cart
Purchase a subscription to gain access to 18,000+ Proceeding Papers, 25+ Fact Sheets, 20+ Technical Reports, 50+ magazine articles and select Technical Publications' chapters.

Details

Description: Using Artificial Intelligence to Optimize Sewer Preventative Maintenance
Using Artificial Intelligence to Optimize Sewer Preventative Maintenance
Abstract
Introduction WSSC Water has 5,700 miles of sewer pipeline. Due to foreign debris such as fats, oils and grease, root intrusion, silt/sediment, and debris, pipelines need to be cleaned periodically to remove these elements which sometimes create blockages and thus avoid Sanitary Sewer Overflows (SSOs). Cleaning a sewer pipeline requires a scouring process, which over time, can degrade wall thickness and reduce pipe life. WSSC Water currently has preventative maintenance (PM) schedules for much of our sewer pipeline system. WSSC Water would like to optimize the sewer PM schedules to save money and to extend the life of our sewer pipelines. Sewer Maintenance Prediction technology can help optimize sewer PM activities by using Artificial Intelligence (AI) to recommend when cleaning is needed even before sewer levels reach an alarm state. The Sewer Maintenance Prediction Pilot tested two technologies, ADS Echo and SmartCover. These technologies can be used for comprehensive sewer performance monitoring, early warning and notification of impending overflows, SSO monitoring, and sewer capacity studies. However, this pilot focused on the technologies' ability to accurately provide early warning and notification of sewer blockages to predict when cleaning is required. Pilot Plan The year-long pilot installed two technologies in 10 manholes each. Each technology was assigned 9 sites located upstream of pipes with 3-month cleaning schedules and 1 site with a high scouring velocity with little to no maintenance issues to be used as a control. After 6 months at the 10 initial locations, some of the equipment was swapped and data was collected at the alternate locations. The swapped locations were those that had the most activity to eliminate any site-specific advantages there may have been. During the year of testing, data was collected to confirm the technologies accurately alarm at full pipe, 1 foot above bench, and 2 feet from top of frame as well as predict when cleaning is needed. The pilot would be considered successful if the technology was able to accurately alarm at pre-defined levels and predict the need for cleaning as verified by WSSC Water crews. Avoided costs associated with reduced cleaning was also considered as part of the evaluation. Pilot Results This presentation will explore the findings of the two technologies and offer practical solutions for wastewater utilities. These findings will be presented in a manner that allows utilities to extrapolate potential cost savings from the optimization of sewer pipeline cleaning using artificial intelligence within their own systems.
This paper was presented at the WEF/AWWA Utility Management Conference, February 13-16, 2024.
SpeakerTitus, Sara
Presentation time
13:30:00
15:00:00
Session time
13:30:00
15:00:00
SessionReal World Applications of Artificial Intelligence
Session number24
Session locationOregon Convention Center, Portland, Oregon
TopicDigital Transformation including AI and ChatGPT
TopicDigital Transformation including AI and ChatGPT
Author(s)
Titus, Sara
Author(s)S. Titus1
Author affiliation(s)Washington Suburban Sanitary Commission 1;
SourceProceedings of the Water Environment Federation
Document typeConference Paper
PublisherWater Environment Federation
Print publication date Feb 2024
DOI10.2175/193864718825159278
Volume / Issue
Content sourceUtility Management Conference
Word count9

Actions, changes & tasks

Outstanding Actions

Add action for paragraph

Current Changes

Add signficant change

Current Tasks

Add risk task

Connect with us

Follow us on Facebook
Follow us on Twitter
Connect to us on LinkedIn
Subscribe on YouTube
Powered by Librios Ltd
Powered by Librios Ltd
Authors
Terms of Use
Policies
Help
Accessibility
Contact us
Copyright © 2024 by the Water Environment Federation
Loading items
There are no items to display at the moment.
Something went wrong trying to load these items.
Description: WWTF Digital Boot 180x150
WWTF Digital (180x150)
Created on Jul 02
Websitehttps:/­/­www.wef.org/­wwtf?utm_medium=WWTF&utm_source=AccessWater&utm_campaign=WWTF
180x150
Titus, Sara. Using Artificial Intelligence to Optimize Sewer Preventative Maintenance. Water Environment Federation, 2024. Web. 9 May. 2025. <https://www.accesswater.org?id=-10101553CITANCHOR>.
Titus, Sara. Using Artificial Intelligence to Optimize Sewer Preventative Maintenance. Water Environment Federation, 2024. Accessed May 9, 2025. https://www.accesswater.org/?id=-10101553CITANCHOR.
Titus, Sara
Using Artificial Intelligence to Optimize Sewer Preventative Maintenance
Access Water
Water Environment Federation
February 15, 2024
May 9, 2025
https://www.accesswater.org/?id=-10101553CITANCHOR