Wilson, Joel

Wilson, Joel

Joel Wilson was a speaker at the WEFTEC 2024 conference in New Orleans, LA October 5-9, 2024.

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About Wilson, Joel

Joel Wilson was a speaker at the WEFTEC 2024 conference in New Orleans, LA October 5-9, 2024.

Wilson, Joel

Joel Wilson was a speaker at the WEFTEC 2024 conference in New Orleans, LA October 5-9, 2024.

Titles from this speaker

Description: 15 Years (2007-2023) of AI Optimization Technology Innovation at Johnson County...

15 Years (2007-2023) of AI Optimization Technology Innovation at Johnson County Wastewater

Abstract

INTRODUCTION In 2007 Johnson County Wastewater (JCW) was the first US utility to pilot emerging genetic algorithm optimization technology applied to collection system master planning. The technology enabled JCW to perform an exhaustive analysis of complex and interdependent SSO remedial alternatives including conveyance, storage and inflow and infiltration (I/I) reduction. The innovative approach and project success provided the foundations for ongoing technology development and advancements in the associated engineering practices. The most distinct advantage realized by JCW has been the ability to evaluate thousands of potential I/I reduction targets in each catchment and find the most cost-effective strategy that reduces conveyance and storage upgrades based on a holistic consideration of complex system-wide interdependencies, and evaluating the efficiency of rerunning the optimization with varying degrees of assumed I/I reduction effectiveness to identify sensitivities and determine resilient strategies that can be adapted over time. This presentation reflects on the ongoing application of Optimizer across five collection system basins at JCW, the innovative techniques that have been applied to maximize cost savings, the technology innovations that have occurred, and the possibilities for future progression. SIGNIFICANCE - Demonstrate value added to collection system planning by supporting innovation. - Provide a case study for best practice methodologies for using master planning decision support systems. - Provide a data driven approach I/I reduction with continuous updates to refine and reevaluate assumptions at critical milestones. - Share benefits and challenges of implementing formal optimization technology from a client's perspective. METHODOLOGY The Nelson Complex collection system will be the primary case study presented to highlight the following technology and methodology innovations between 2007 and 2022: - In 2007, the optimization engine relied solely on Genetic Algorithms to evaluate SSO remedial alternatives, whereas, in 2022 a range of intelligent algorithms and implicit heuristics customized specifically for collection systems are incorporated in Optimizer. -In 2007, limited empirical I/I reduction data was available. Running the optimization for a broad range of sensitivity analyses helped show areas for I/I reduction that would be cost-effective irrespective of the assumptions. As more pre- and post-I/I reduction flow monitoring was completed, the I/I reduction effectiveness assumptions were verified and refined to allow more accurate I/I reduction optimization over time. - Progressively rerunning the optimization each year as new data becomes available helps refine the roadmap and makes use of the efficiency of having an optimization model that can easily be updated with modular inputs. - The 2020 Nelson Complex master plan update incorporated a novel application of Optimizer to prioritize the sequence of master plan project implementation to maximize return on investment by identifying the project sequence to maximize SSO reduction at least cost. - The methodology for evaluating storage alternatives changed after the 2007 project to enable a more effective analysis. Rather than modeling storages with specific volumes, the storage is modeled at the largest feasible size and the storage is costed based on the volume received which is controlled by weir height alternatives and downstream capacity constraints. - An advancement in the approach to evaluating gravity alternatives included considering asset condition and available easement to determine whether upgrades would be implemented parallel to the existing asset or as a replace and upsize capacity improvement. - Flow diversion alternatives create significant complexity for an optimization algorithm due to introducing local minima within the search space. To help manage this, the optimization technology now allows for 'logical adjusters' to be included such as, if flow-path A is selected, then flow path B must not be selected, in the case where it is only ever logical to construct an upgrade along one of the flow path alternatives. - To reduce cloud computing cost and run time, the model is trimmed to remove upstream assets that don't have capacity constraints and apply time-series boundary conditions. This approach makes the optimization of much larger and complex systems more feasible. In 2007 only the Turkey Creek basin was evaluated, whereas both the Turkey Creek and Mission Main basins could be evaluated in a single optimization and the interdependencies between basins tied to the capacity of the Nelson Wastewater Treatment Complex were thoroughly evaluated. RESULTS The updated Nelson Complex collection system master plan was completed in 2019. JCW completed master plan optimizations for Leawood basin in 2021 and Indian Creek basin in 2022 with continuous improvement with each subsequent project. The Nelson Complex preferred master plan solution was based on the lowest life-cycle cost combination of conveyance, storage, treatment, and I/I reduction strategies. In addition, the engineering team integrated non-cost objectives into the evaluation. These included community impacts associated with large capital projects, operational flexibility, and resilience for the plan to be adapted over time. Total life-cycle cost of the least-cost optimized was approximately $205M and the final preferred master plan solution accounting for non-cost objectives, illustrated in Figure 1, was approximately $220M. To compare solutions with distinctly different characteristics, optimization scenarios were completed with different sets of alternatives included. Once the optimization model was formulated, the range of allowable alternatives were easily toggled on and off so that different solutions would be produced automatically when the cloud computing run was performed. The cost-effective analysis curve in Figure 2 compares solutions for the conveyance-only scenario (i.e. without storage alternatives, with I/I reduction limited to 10%) with the least-cost optimized solution (all alternatives considered) and the solution where treatment plant capacity is constrained to existing capacity. Once the capital improvement plan was optimized, the next task was to prioritize the implementation schedule. Optimizer was used to evaluate thousands of possible scheduling alternatives to determine the sequence that would maximize return on investment based on the following objectives: - reduce basement backups; - reduce modeled manhole flooding; - reduce modeled SSOs at Flow Protection Devices; - reduce flows to downstream agencies; and - reduce discharge at Peak Extraneous Flow Treatment Facilities, (wet weather/high rate treatment facilities). The return on investment achieved in the prioritization analysis is illustrated in Figure 3. The results from the various JCW basin optimization and prioritization projects have helped inform JCW on future capital works leading to maximum benefits to the community and the environment while also providing the operational flexibility and resilience to be adapted over time. CONCLUSIONS While technology innovations and advancements provide engineers with useful tools, the value that can be achieved using technology is dependent on solid foundational engineering skills and advancements in the techniques applied to support technology implementation. The JCW optimization projects completed from 2007 to 2022 highlight the value that can be achieved when both technology and technical application innovations are supported by the industry and continuously improved over time.

This paper was presented at the WEF Collection Systems Conference, June 27-30, 2023.

SpeakerWilson, Joel

Presentation time

10:30:00

11:15:00

Session time

08:30:00

11:45:00

SessionSession 17: Local Utilities

Session number17

Session locationKansas City Convention Center

TopicDesign & Construction, Asset Management and CMOM, Wet Weather Management & Control (CSOs/SSOs)

Author(s)

Wilson, Joel

Author(s)J. Wilson1; J. Matton1; A. Erikson2; R. Eisele3; A. Witt4;

Author affiliation(s)WCS Engineering1; Optimatics2; HDR, Inc.3; Johnson County Wastewater4;

SourceProceedings of the Water Environment Federation

Document typeConference Paper

PublisherWater Environment Federation

Print publication date Jun 2023

DOI10.2175/193864718825158891

Volume / Issue

Content sourceCollections

Word count13

Description: Best Practice of Intelligent Algorithms to Optimize And Prioritize Capital...

Best Practice of Intelligent Algorithms to Optimize And Prioritize Capital Investments And Asset Rehabilitation For Jefferson County, AL

Abstract

This presentation illustrates that for digital-water solutions to be successful we need to shift the focus from "digital" and "smart," data and tools to the people and value added. The Jefferson County, AL, application of intelligent algorithms to optimize and prioritize capital investments and asset rehabilitation demonstrates a collaborative approach through every stage of the project and delivers simple to understand optimal solutions based on cutting-edge technology while delivering significant value added / return on investment through close collaboration with the utility staff on the implementation of the solution.

The following conference paper was presented at WEFTEC 2021, October 16-20, 2021. To read the full abstract, see "Abstract" tab below.

SpeakerWilson, Joel

Presentation time

8:30:00

08:50:00

Session time

08:30:00

09:30:00

SessionInnovation in Asset Portfolio Decision Making

Session number410

TopicAsset Management, Policy and Regulation, Utility Management and Leadership

Author(s)

Joel Wilson

Author(s)J. Wilson1; D.F. Garcia3; S. King4; D.A. White5; S. Tomic2;

Author affiliation(s)WCS Engineering, Kingscliff, AU1WCS Engineering, Kingscliff, AU2WCS Engineering, TX3Hazen and Sawyer, AL4Jefferson County, AL5

SourceProceedings of the Water Environment Federation

Document typeConference Paper

PublisherWater Environment Federation

Print publication date Oct, 2021

DOI10.2175/193864718825158091

Volume / Issue

Content sourceWEFTEC

Word count19

Description: Maximizing Affortable Housing with Minimal Water Environment Impacts--Innovative...

Maximizing Affortable Housing with Minimal Water Environment Impacts--Innovative Pilot for ChristChurch, New Zealand

Abstract

PURPOSE This paper presents an innovative, first-of-its kind in Australia and New Zealand, application of intelligent algorithm optimization to evaluate urban development alternatives and identify specific regions within the city that can accommodate growth with minimal impact to the performance of the wastewater network. The existing wastewater network has wet weather overflows that exceed consent targets. Interim to completion of capital projects to resolve existing capacity constraints, Christchurch City Council and Kainga Ora (supporting affordable homes and community well-being) undertook the development opportunity optimization to identify opportunities for development potential without increasing environmental impacts. BENEFITS WCS brings to the market a novel AI-driven framework to urban planning that promises sustainable, strategic, and environmentally conscious decision-making platform, adaptable to any utility, regardless of system size and hydraulic complexity. Unlike traditional modeling and planning methodologies, WCS' AI-powered workflow allows the utility and engineers to spend their time where it's most efficient — understanding the problem, formulating the problem, and making informed decisions. This project displays an innovative roadmap, including the construction of an intuitive Business Intelligence decision-making platform to aid in urban growth to ultimately enhance urban living standards and accommodate growing populations more effectively. INTRODUCTION Christchurch City Council (CCC) has a large and complex wastewater network serving approximately 373,000 people with a single wastewater treatment plant capable of treating approximately 160 megaliters/day. The city has two main river systems: the Avon River and the Heathcote River, which both flow into the Avon-Heathcote Estuary which then flows into the Pacific Ocean. CCC holds a resource consent (permit) for the overflows to waterways. The resource consent allows an overflow frequency to each of these receiving environments which decreases over time to a 2-year recurrence interval, based on continuous simulation of 15 years rainfall data. In addition, no overflow site may overflow more than every six months on average, based on the same long time series modeling. While CCC is tracking well against progress targets for wet weather overflow abatement, it is reluctant to permit development in regions that could significantly exacerbate existing overflows or create new overflows. The current city population is forecast to increase from the current population of approximately 370,000 to a 2054 population of over 450,000; however, the maximum potential population if all land parcels are developed to their maximum land use density is over 2,000,000. There are many considerations when determining the effect of growth on a city. To assess water and wastewater network capacity impacts associated with proposed development, it is common for manual hydraulic modeling to be completed for each development application. Not only is this approach time consuming and repetitive, but it is also difficult to establish any significant impact associated with an individual development. By applying intelligent algorithm optimization using cloud computing to simulate thousands of urban growth alternatives, the complex hydraulic interdependencies and cumulative impact of growth were synergistically considered in a holistic, effective planning analysis. DETAILS The existing system performance (baseline) was assessed based on 15-year continuous simulation of historic rainfall to assess wet weather overflow frequencies, volumes and peak discharge rates. The results were then used to construct a composite rainfall event from various historic events that correspond to a 2-year return period specific to each catchment. The 'representative 2-year design storm' was used to prepare an optimized master plan based on a life-cycle cost evaluation of conveyance, storage and inflow and infiltration (I/I) reduction alternatives. The master plan was then prioritized to determine the sequence of project implementation that maximizes return on investment with respect to reducing wet weather overflows (weighted by customer and environmental impact) as cost-efficiently as possible. Interim to scheduled capital improvements, the impact of development can result in deteriorating performance if not managed appropriately. Maximum population growth alternatives to be considered for each wastewater subcatchment were determined based on consideration of the CCC 2068 growth model and the maximum density yield based on land-use. Each alternative was formulated in Optimizer-ICMTM such that the optimization algorithm would trial combinations of growth intensities and assess hydraulic performance for each trial scenario. Hydraulic performance was assessed relative to the baseline existing system performance (total overflow volume and available freeboard in the representative 2-year design storm). Individual manhole changes in overflow volume or freeboard reduction were quantified as a percentage relative of their existing values. Each of the thousands of population growth trial solutions evaluated in the optimization analysis was retained in a data portal and post-processed using Power BI to demonstrate the relationship between optimized growth and system performance impacts (Figure 1). Spatial probability algorithms were scripted in Power BI to identify subcatchments and regions with the maximum potential for growth with minimal impact (Figure 2). ArcGIS was used to communicate the optimization results based on high-resolution grouped subcatchments (Figure 3) and macro-scale suburb (Figure 4) growth potential weightings. COMPLETION The benefit of the optimization approach has already been demonstrated. In a first for Aotearoa, a housing optimization project was successfully carried out on Council's wastewater network in 2023. Thousands of intensification scenarios were evaluated using the AI framework. The project identified the Council's wastewater subcatchments and suburbs with the greatest capacity for growth, with results showing that housing for up to 35,000 additional people could be accommodated with virtually no increase in wastewater overflows (see Figures 3 and 4).

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

SpeakerWilson, Joel

Presentation time

11:15:00

11:45:00

Session time

10:45:00

11:45:00

SessionSmart Systems - Stormwater

Session number18

Session locationConnecticut Convention Center, Hartford, Connecticut

TopicArtificial Intelligence, Combined Sewer Overflow, Combined Sewer System, Continuous Monitoring and Adaptive Control, Optimization, Stormwater Case Study/Application, Sustainability And Social Responsibility, Utility Management

Author(s)

Wilson, Joel

Author(s)J. Wilson1, B. O'Brien2, M. McDonald3, G. Hughes4

Author affiliation(s)WCS Engineering 1; WSP 2; Christchurch City Council 3; Kainga Ora 4

SourceProceedings of the Water Environment Federation

Document typeConference Paper

PublisherWater Environment Federation

Print publication date Apr 2024

DOI10.2175/193864718825159392

Volume / Issue

Content sourceCollection Systems and Stormwater Conference

Word count14

Description: Sewer Blockage Sensor Placement Optimization and Prioritization Using AI, Advanced...

Sewer Blockage Sensor Placement Optimization and Prioritization Using AI, Advanced Data Analytics, and AGOL Dashboards

Abstract

KEYWORDS Sensor Placement, Emergency Response, Asset Management, Strategic Planning, Artificial Intelligence, AI, Python Scripts, Data Analytics, Optimization, Prioritization, ArcGIS Online, Dashboarding. PURPOSE Urban Utilities, WCS Engineering and Optimatics pioneered an innovative sensor deployment optimization and prioritization decision-support platform to help prevent dry weather overflows resulting from pipe blockages. Harnessing Artificial Intelligence (AI) infrastructure, cloud computing and custom Python optimization scripts, a sensor deployment roadmap based on comprehensive data was generated from thousands of model simulations to optimize sensor placement, maximizing high-risk pipe coverage with minimum sensor units. ArcGIS Online dashboarding simplified complex data into an easy-to-use interface, which, paired with deployed sensors' early warning system, enables Urban Utilities' staff to make optimized sensor placement decisions and quickly respond to emergency situations. BENEFITS The presentation introduces a groundbreaking convergence of intelligent algorithms, custom Python scripting, hydraulic modeling, and dashboarding; offering a tangible and accessible solution to utilities as evidenced by its successful implementation in Urban Utilities' Breakfast Creek collection system catchment. This sensor deployment optimization and prioritization framework is impactful, customizable, adaptable, and scalable for any utility regardless of the size or complexity of their collection system. INTRODUCTION Urban Utilities engaged WCS Engineering (WCS) to apply Optimizer-ICMTM (Optimizer) and customized scripts to select sensor placement in the pilot catchment of Breakfast Creek (spanning 407 km of sewer main) that would maximize coverage of gravity sewers with higher risk of blockage and consequence of failure (CoF). The primary intent of the sensor placement is to provide an early warning system prior to dry weather overflows and to provide an emergency response framework that identifies potential overflow locations when a sensor high-level alarm is triggered. Secondary benefits of sensor placement include the potential for enhanced model calibration data and real-time system performance forecasting. DETAILS Leveraging Optimizer's capabilities of intelligent algorithm optimization technology and cloud computing, the hydraulic model of the Breakfast Creek catchment was simulated over 12,000 times. Each model run represented a scenario in which a single pipe blockage was simulated during peak dry weather flow (PDWF). Optimizer captured depth timeseries data for each blockage model run (Figure 1). The raw output from the Optimizer blockage analysis included extensive, large databases that required customized post-processing using Python scripts to consolidate and translate the data into more manageable database formats. Those databases then informed the custom sensor placement optimization and prioritization algorithm developed by WCS in Python. The algorithm first traces downstream of every potential sensor location, checking sensor alarm levels against the blockage surcharge response levels to determine the coverage reach of each sensor (Figure 2). The algorithm then optimizes and prioritizes the sensor deployment by computing a Sensor Effectiveness Score (SES), a custom quantity cumulatively calculated from parameters related to the pipes covered by each sensor. Through the tracing and prioritization of sensors, the algorithm engine collects and synthesizes key information and metrics pertaining to the coverage of each sensor. Among other quantities, the following critical parameters are cumulatively stored, for each sensor: pipes covered, lengths of pipes covered (total, unique, and by risk category), potential overflow locations covered, maximum preventable overflow rate, overflow location environment, and the time between sensor high-alarm detection to overflow. The results from the tracing, optimization, and prioritization algorithms were then condensed into an interactive, data-driven, decision-support platform using ArcGIS Online dashboards. Sensitivity analyses were undertaken to test the effect of key inputs and assumptions on the sensor placement prioritization results. These included running scenarios with varying inputs for sensor placement site access constraints, asset risk score assumptions, SES weightings, minimum response time and minimum freeboard from alarm activation to overflow. The preferred SES calculation adopted was risk multiplied by length. The risk score is a function of CoF, structural grade and historical pipe blockages. The CoF incorporates the location of overflow which was previously calculated using the Optimizer-ICMTM blockage model (Moore, et al., 2021). A dynamic ArcGIS Online dashboard (Figure 3) was built to allow Urban Utilities to access the complex tracing and ranking results in an easy-to-use decision-support platform, allowing Urban Utilities' staff to focus their time on where it's most efficient — making informed decisions under critical, time-sensitive conditions. The dashboard gives a crystal-clear view of the sensor placement prioritization results (including all parameters related to each sensor location), potential network vulnerabilities, and how to best address them in a planning and emergency scenario. The dashboard interface clearly displays the prioritized order in which sensors may be deployed to maximize risk reduction. It also allows the user to select any alternative, non-optimal sensor location in the map to visualize the resulting coverage. If a proposed sensor location manhole is deemed inaccessible by the field team, for example, Urban Utilities can still use the dashboard to test adjacent, accessible manholes that potentially have similar coverage reach and dynamics to the optimal solution. In the event of a sensor high-level alarm trigger, Urban Utilities can interact with the dashboard to immediately visualize where potential overflow locations can occur, the maximum dry weather overflow rate to be tankered to prevent overflows in lieu of repairing the pipe blockage, which pipes could be the cause of the sensor alarm, and the response time between when the sensor's alarm is activated and overflows occur. The deployment of the Breakfast Creek pilot catchment dashboard is complete and the application of the sensor prioritization framework is being considered by Urban Utilities for other catchments. COMPLETION The deployment of the Breakfast Creek catchment dashboard is complete. WCS is currently in the works of deploying this platform in the rest of Urban Utilities' catchments. CONCLUSION The combination of the sensor placement optimization and prioritization paired with the dynamic ArcGIS Online dashboard enables the continued integrated planning of Urban Utilities' asset and overflow management program. The dashboard serves as an easy-to-use, impactful emergency management tool designed for real-time, critical decision making. The result is more than just a responsive system; it's a paradigm shift in how Urban Utilities can manage and prevent overflows. Yet, the true potential lies in scalability and customization. This approach, rooted in deep analysis and user-friendly visualization, can be adapted and expanded, promising utilities — regardless of their size — a future where system reliability is built from the root.

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

SpeakerWilson, Joel

Presentation time

14:00:00

14:30:00

Session time

13:30:00

15:00:00

SessionAI and Digital Twins

Session number21

Session locationConnecticut Convention Center, Hartford, Connecticut

TopicArtificial Intelligence, Collection Systems, Digital Twin, Emergency Preparedness And Response, Sensors, Smart Data Infrastructure, Smart Water Infrastructure, SSO Reduction, Utility of the Future, Wet Weather

Author(s)

Wilson, Joel

Author(s)J. Wilson1, L. Djehdian, K. Tennakoon2

Author affiliation(s)WCS Engineering 1; Urban Utilities 2

SourceProceedings of the Water Environment Federation

Document typeConference Paper

PublisherWater Environment Federation

Print publication date Apr 2024

DOI10.2175/193864718825159391

Volume / Issue

Content sourceCollection Systems and Stormwater Conference

Word count16

Sewer Blockage Sensor Placement Optimization and Prioritization using AI, Advanced Data Analytics, and AGOL Dashboards

Abstract

PURPOSE Urban Utilities, WCS Engineering and Optimatics pioneered an innovative sensor deployment optimization and prioritization decision-support platform to help prevent dry weather overflows resulting from pipe blockages. Harnessing Artificial Intelligence infrastructure, cloud computing and custom Python optimization scripts, a sensor deployment roadmap based on comprehensive data was generated from thousands of model simulations to optimize sensor placement, maximizing high-risk pipe coverage with minimum sensor units. ArcGIS Online dashboarding simplified complex data into an easy-to-use interface, which, paired with deployed sensors' early warning system, enables Urban Utilities' staff to make optimized sensor placement decisions and quickly respond to emergency situations. BENEFITS The presentation introduces a groundbreaking convergence of intelligent algorithms, custom Python scripting, hydraulic modeling, and dashboarding; offering a tangible and accessible solution to utilities as evidenced by its successful implementation in Urban Utilities' Breakfast Creek collection system catchment. This sensor deployment optimization and prioritization framework is impactful, customizable, adaptable, and scalable for any utility regardless of the size or complexity of their collection system. INTRODUCTION Urban Utilities engaged WCS Engineering (WCS) to apply Optimizer-ICMTM (Optimizer) and customized scripts to select sensor placement in the pilot catchment of Breakfast Creek (spanning 407 km of sewer main) that would maximize coverage of gravity sewers with higher risk of blockage and consequence of failure (CoF). The primary intent of the sensor placement is to provide an early warning system prior to dry weather overflows and to provide an emergency response framework that identifies potential overflow locations when a sensor high-level alarm is triggered. Secondary benefits of sensor placement include the potential for enhanced model calibration data and real-time system performance forecasting. DETAILS Leveraging Optimizer's capabilities of intelligent algorithm optimization technology and cloud computing, the hydraulic model of the Breakfast Creek catchment was simulated over 12,000 times. Each model run represented a scenario in which a single pipe blockage was simulated during peak dry weather flow (PDWF). Optimizer captured depth timeseries data for each blockage model run. The raw output from the Optimizer blockage analysis included extensive, large databases that required customized post-processing using Python scripts to consolidate and translate the data into more manageable database formats. Those databases then informed the custom sensor placement optimization and prioritization algorithm developed by WCS in Python. The algorithm first traces downstream of every potential sensor location, checking sensor alarm levels against the blockage surcharge response levels to determine the coverage reach of each sensor. The algorithm then optimizes and prioritizes the sensor deployment by computing a Sensor Effectiveness Score (SES), a custom quantity cumulatively calculated from parameters related to the pipes covered by each sensor. Through the tracing and prioritization of sensors, the algorithm engine collects and synthesizes key information and metrics pertaining to the coverage of each sensor. Among other quantities, the following critical parameters are cumulatively stored, for each sensor: pipes covered, lengths of pipes covered (total, unique, and by risk category), potential overflow locations covered, maximum preventable overflow rate, overflow location environment, and the time between sensor high-alarm detection to overflow. The results from the tracing, optimization, and prioritization algorithms were then condensed into an interactive, data-driven, decision-support platform using ArcGIS Online dashboards. Sensitivity analyses were undertaken to test the effect of key inputs and assumptions on the sensor placement prioritization results. These included running scenarios with varying inputs for sensor placement site access constraints, asset risk score assumptions, SES weightings, minimum response time and minimum freeboard from alarm activation to overflow. The preferred SES calculation adopted was risk multiplied by length. The risk score is a function of CoF, structural grade and historical pipe blockages. The CoF incorporates the location of overflow which was previously calculated using the Optimizer-ICMTM blockage model (Moore, et al., 2021). A dynamic ArcGIS Online dashboard was built to allow Urban Utilities to access the complex tracing and ranking results in an easy-to-use decision-support platform, allowing Urban Utilities' staff to focus their time on where it's most efficient — making informed decisions under critical, time-sensitive conditions. The dashboard gives a crystal-clear view of the sensor placement prioritization results (including all parameters related to each sensor location), potential network vulnerabilities, and how to best address them in a planning and emergency scenario. The dashboard interface clearly displays the prioritized order in which sensors may be deployed to maximize risk reduction. It also allows the user to select any alternative, non-optimal sensor location in the map to visualize the resulting coverage. If a proposed sensor location manhole is deemed inaccessible

Collection system level sensors are a fundamental component of digital-twin models and help to detect blockages and sewer main failure before overflows occur. Maximizing the return on investment from level sensors requires a tool that can select deployment locations and prioritize implementation to maximize coverage of high-risk assets. In this case study, we review the innovative approach to optimize and prioritize sensor deployment using AI, cloud computing, and ArcGIS dashboarding.

SpeakerWilson, Joel

Presentation time

15:40:00

15:50:00

Session time

15:30:00

17:00:00

SessionUnleashing the Power of Digital Tools for Your Collection System

Session number220

Session locationRoom 256

TopicAsset Management, Collection Systems, Intelligent Water, Intermediate Level, One Water Management

Author(s)

Djehdian, Lucas, Tennakoon, Kithsiri, Wilson, Joel

Author(s)L.A. Djehdian1, K. Tennakoon2, J. Wilson3

Author affiliation(s)1WCS Engineering, IL, 2Urban Utilities, 3WCS Engineering

SourceProceedings of the Water Environment Federation

Document typeConference Paper

PublisherWater Environment Federation

Print publication date Oct 2024

DOI10.2175/193864718825159580

Volume / Issue

Content sourceWEFTEC

Word count16

Description: SSO Consenting and Design Storm Selection Based on Continuous Simulation of...

SSO Consenting and Design Storm Selection Based on Continuous Simulation of Historical Rainfall

Abstract

Purpose of the Presentation
The purpose of the presentation is to demonstrate the innovative approach to assessing sanitary sewer overflow compliance being used by Christchurch City Council (CCC), New Zealand. Continuous simulation of long-term rainfall data is used by CCC to assess overflow compliance rather than using a synthetic rainfall pattern that is intended to represent the target return period. Furthermore, collection system planning is based on a design storm that was generated based on statistical analysis of the 25-year rainfall continuous model simulation results. The presentation will show that SSO frequencies are sometimes not well represented by rainfall events that have an equivalent return period and that antecedent rainfall conditions and hydrologic and hydraulic (H&H) routing of flows unique to each collection system can help to select a better design storm to simulate overflow return periods of interest.

Benefits of the Presentation
This presentation should be selected because it will provide the following benefits to the audience and industry:
-- it will illustrate the difference in overflows predicted using a synthetic return period rainfall event and the overflow frequencies calculated based on continuous simulation of long-term rainfall.
-- it will show how the environmental regulators for Christchurch, New Zealand require continuous simulation of long-term rainfall to validate overflow compliance.
-- it will demonstrate the value in reviewing continuous simulation overflow results to develop more accurate design storms to be used for infrastructure planning.
-- it will show how a utility can use an improved approach to developing design storms to ensure investments are made in new infrastructure and infrastructure rehabilitation programs that are targeted in the relevant area of the collection system in order to meet consent conditions effectively.
-- it will illustrate how three different 15-year windows of rainfall from a 25-year period of rainfall can affect the outcome of the overflow frequency assessment but that the climatic variations do not have a significant bearing on the outcome of the overflow compliance.

Project Background
Christchurch City Council provides drinking water, stormwater, and wastewater across five council areas in the North Island, New Zealand. Christchurch has a complex wastewater network serving 373,000 people. Christchurch has a large and complex wastewater network serving approximately 373,000 people with a single wastewater treatment plant capable of treating 650 megaliters/day (MLD). Christchurch has two main river systems: the Avon River and the Heathcote River, which both flow into the Avon-Heathcote Estuary which then flows into the Pacific Ocean (see Figure 1). Christchurch City Council (CCC) holds a resource consent (permit) for the overflows to waterways. The resource consent allows an overflow frequency to each of these receiving environments which decreases over time to a 2 year ARI, based on 15 years of long time series modelling. In addition, no overflow site may overflow more than every six months on average, based on the same long time series modelling. The Christchurch Overflow Compliance Assessment project was commenced and completed in 2020. The project was completed using the latest model calibration completed in 2020 and was an update to the overflow compliance assessment completed based on the model previously calibrated in 2015. The overflow consent conditions require that continuous simulation of the most recent 15-years of rainfall be completed to assess the system performance. This project included continuous simulation of the most recent 25-years of rainfall and analysis of overflow results from three different 15-year windows to investigate whether the overflow frequencies are significantly affected by the 15-year window of rainfall selected. Overflow control measure alternatives to achieve the 2-year overflow return period target were previously assessed using a synthetic rainfall pattern. To determine whether this rainfall pattern would be appropriate for future planning studies, the long-term continuous simulation results were used to predict the location of overflows occurring more frequently than once every two years and then compared to the locations predicted using the synthetic rainfall pattern. This assessment demonstrated that the synthetic event did not provide a good representation of actual overflows and a new design storm was developed based on historic events.

Project Details
The objective of the wet weather overflow compliance assessment was to determine wastewater overflow compliance with the consent conditions stipulated by the environmental protection agency, Environment Canterbury (ECAN). This was undertaken using the calibrated hydraulic model to simulate system performance based on continuous rainfall data from the period of 1995 to 2020. To determine whether the rainfall window could have an impact on any of the consent compliance results, the assessment of overflow frequency and volume was performed for the following time periods:
1. 1995 to 2020
2. 1995 to 2010
3. 2000 to 2015
4. 2005 to 2020

The compliance assessment results summarised in Table 1 and Table 2 below, show the level of variation in overflow response according to the rainfall window chosen. While the variations are significant in some time windows, the compliance outcomes would not have been affected. Table 1: Overflow volume in each catchment for different time periods Table 2: Overflow frequency in each catchment for different time periods The objective of the design storm review was to ensure the design storm used for wastewater infrastructure planning is representative of a 2-year return period based on the 25-year continuous simulation results. The assessment was based on overflow volume, peak and spatial distribution. The 25-year continuous simulation results were reviewed to identify historical events that corresponded to an overflow return period of approximately 2 years. Due to a high degree of rainfall spatial variance in the historical events that were close to a 2-year return period, it was decided to repeat the overflow statistical analysis by receiving environment rather than system-wide. Based on the results from the overflow statistical analysis, several 2-year design storm alternatives (synthetic and historical) were shortlisted. The preferred 2-year design storm, presented in Figure 1, was the August 5, 1995 (northern basins) and April 17, 2014 (southern basins) composite event. This event provided the best representation of 2-year ARI overflow volumes, peaks and spatial distribution and comprised events of sufficiently short duration to be convenient for planning.

Conclusion
The results from this study demonstrate a novel and improved approach to developing design storms to ensure investments are made in new infrastructure and infrastructure rehabilitation programs that are targeted in the relevant area of the collection system in order to meet design criteria and/or compliance targets.

The following conference paper was presented at Collection Systems 2021: A Virtual Event, March 23-25, 2021.

SpeakerWilson, Joel

Presentation time

15:00:00

15:20:00

Session time

15:00:00

16:00:00

SessionWet Weather Issues

Session number6

Session locationLive

TopicClimate Change Adaptation, Combined Sewer Overflow, composite synthetic design storms, Consent Orders, Continuous Rainfall Simulation, maintenance optimization, Modeling, SSO Reduction, Work Order Management And Scheduling

Author(s)

J. WilsonT. McMillan

Author(s)J. Wilson1; T. McMillan2

Author affiliation(s)WCS Engineering1; Christchurch City Council2

SourceProceedings of the Water Environment Federation

Document typeConference Paper

PublisherWater Environment Federation

Print publication date Mar 2021

DOI10.2175/193864718825157917

Volume / Issue

Content sourceCollection Systems Conference

Word count14

Base text size -

Wilson, Joel

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