Ranck, Chris

Chris Ranck is the national planning leader at Black Veatch for collection systems and wet-weather programs and has 23 years experience in hydraulic modeling, water quality modeling, tunnel transient modeling, CSO regulatory, sustainability, and machine learning projects. Chris is the second vice chair for WEFs Collection System Committee and has previously served in WEF as a chair for the Collection System modeling technical practice group and the Envision Task Force.

Titles from this speaker

Description: Modern Master Planning in South Florida: Resilience and Condition Assessment...

Modern Master Planning in South Florida: Resilience and Condition Assessment Considerations for Regional Wastewater Transmission

Abstract

Our presentation will summarize the importance of incorporating resiliency and condition assessment into regional wastewater master planning to develop a balanced long-term capital improvement plan. The plan addresses traditional master planning considerations such as growth, hydraulic capacity and level of service, but also addresses asset condition, remaining useful life of critical assets, and system resiliency. Broward County Water and Wastewater Services provides drinking water for 59,000 customers while serving as a regional wastewater authority for over 600,000 residents in South Florida, with treatment at its North Regional Wastewater Treatment Plant. The plant and the regional transmission system are presented in Figure 1. The regional system is a pressurized transmission system receiving wastewater from both local systems and large wholesale customers, with eleven master pump stations that serve multiple gravity collection systems. The root need for the master plan is to address the aging infrastructure in the regional system and ensure reliable service for both the local and wholesale customers. While traditional master planning activity including hydraulic modeling and capacity assessment were performed, the traditional evaluation was augmented by the following modern master planning activities:
1. Condition assessment of the eleven master pump stations was performed for individual architectural, structural, building mechanical, process mechanical, and instrumentation and control assets. Figure 2 presents the asset score for one of the pump stations, with lower scores assigned to better asset conditions. As shown in the figure, the pump station was evaluated on nearly eighty individual assets to develop specific recommendations for each key asset in the station.
2. The hydraulic capacity assessment included a system resiliency evaluation to determine which branches of the transmission system have adequate redundancy in the event of a main break, and which branches can be more reliably served with a parallel relief main. In the example presented in Figure 3, a transmission main break downstream of four master pump stations can be mitigated by a regional relief main to the east. The evaluation determined that the majority of the central system has adequate redundancy to maintain service in the event of a break, following the installation of isolation valves to route the flow to the Northwest plant via one of the looped mains.
3. Transient modeling was performed to identify potential hydraulic issues from a sudden shutdown of a master pump station. In the example presented in Figure 4, the addition of a combination air and vacuum valve can mitigate the potential for vacuum conditions in the transmission mains following the sudden shutdown of the pump station.

From the elements of the planning process, a capital improvement plan that addresses hydraulic capacity, transient mitigation, asset condition, and resiliency was developed for the County's long-term implementation. By addressing each of these elements together, the plan is holistic and adaptable as the County moves forward. At the time of this abstract, the condition assessment, hydraulic modeling, transient modeling, resiliency analysis, and capital improvement plan development are final and complete. The overall master plan will be complete by November 2020. Our presentation will benefit any wastewater utility that is in the master planning process or considering developing or updating their master plan. While the case study is applicable to any wastewater utility, there will be additional benefits to any utility that operates regional transmission mains or manifold force mains with multiple pump station inputs.

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

SpeakerRanck, Chris

Presentation time

11:00:00

11:20:00

Session time

11:00:00

12:00:00

SessionPlanning

Session number4

Session locationSimu-live

TopicCollection Systems, Combined Sewer System, Condition Assessment, Deep sewer tunnel, Design considerations, Force Mains, Project Management, Public Utility, Resiliency

Author(s)

C. RanckJ. SeidenA. SchwermanB. BallA. Zarazua

Author(s)C. Ranck1; J. Seiden2; A. Schwerman3; B. Ball4; A. Zarazua5

Author affiliation(s)Black & Veatch1; Broward County Water and Wastewater Services2

SourceProceedings of the Water Environment Federation

Document typeConference Paper

PublisherWater Environment Federation

Print publication date Mar 2021

DOI10.2175/193864718825157708

Volume / Issue

Content sourceCollection Systems Conference

Word count16

Description: Transforming Design Tools into Predictive Operational Tools through Artificial...

Transforming Design Tools into Predictive Operational Tools through Artificial Intelligence and Machine Learning (AI/ML)

Abstract

Our presentation will summarize the development and implementation of artificial intelligence and machine learning (AI/ML) tools Citizens Energy Group has developed to provide predictive operational decision support for combined sewer overflow (CSO) facilities. Citizens developed these tools by efficiently leveraging its prior investments in collection system modeling and metering. Our AI/ML tools consist of artificial neural networks fit to long-term datasets to predict inflows to CSO facilities using the 72-hour NOAA radar rainfall forecast. Both the rainfall and CSO facility inflow are embedded in a Power BI dashboard for efficient communication to Citizens staff. We developed this dashboard without incurring any additional software cost and believe any wastewater utility operating wet weather facilities can implement a similar AI/ML tool to ours. Citizens Energy Group is the combined water, wastewater, natural gas, steam, and chilled water utility in Indianapolis. Citizens is presently implementing a 20-year CSO consent decree with required completion in 2025. Consent decree compliance is primarily achieved through the DigIndy Tunnel system, a 28-mile long, 18-foot diameter tunnel network. Citizens is also implementing a pair of storage facilities in the Upper White River and Upper Pogues Run watersheds. Citizens, like all utilities implementing a consent decree, has made significant investments in modeling and metering its collection system, and has multiple years of meter data and model results on the shelf from recent regulatory and design support activity. While Citizens has confidence in its InfoWorks ICM model of the collection system, computational limitations prevent the model from being applied in real time or for immediate prediction of future rainfall events. In order to transform the collection system model from a design tool into a predictive operational tool, Citizens developed neural networks for the DigIndy Tunnel system and the Upper Pogues Run storage tank. These neural networks effectively serve as an extension of Citizens' collection system model to simulate inflow to the tunnel or tank in a matter of seconds. Since both facilities will begin operation at the beginning of 2022, Citizens utilized several years of collection system model input and output data to develop the neural networks. Figure 1 presents the architecture diagram for the AI/ML tools. Within Power BI, embedded Python scripts using the MetPy library (Unidata, 2021) connect to the NOAA server and obtain the 72-hour National Digital Forecast Database (NDFD) radar forecast for rainfall and temperature. The forecasted rainfall data is compiled for the CSO facility service area and temperature is utilized to estimate evapotranspiration (Hargreaves and Samani, 1982) for use in the neural networks. Citizens developed standard feed forward neural networks to predict inflow volume to the tunnel and storage tank similar to the collection system model. Figure 2 presents a scatterplot comparing each modeled inflow event to the tunnel system for precipitation years 2016 through 2019 to the neural network prediction. The inputs to the neural network are rainfall, peak intensity, antecedent dry days, the previous day's rainfall, and evapotranspiration. The total inflow volume from the neural network was within one percent of the InfoWorks collection system model simulation, with a R2 of 93%. Citizens evaluated input parameter sensitivity of the neural network and determined that for the storage tunnel, rainfall is the nearly an order of magnitude more important than any of the other input parameters. However, for the Upper Pogues Run storage tank that serves a much smaller watershed area, peak intensity is nearly as important as rainfall. Citizens initiated a desktop test deployment in early 2021 and progressed to an automated test deployment that has been completed at the time of the abstract. In the automated deployment, the rainfall forecast and subsequent inflow volume forecast is generated hourly, with results posted on Citizens' internal SharePoint. The rainfall forecast and forecasted inflow volume are presented though an online Power BI dashboard. We will present our method of deployment of this tool as well as other options for automated deployment that wastewater utilities may consider. Table 1 presents an example forecast from the neural network for July 15 â€“ July 17, 2021. The forecasted inflow volume is based on the NDFD on July 14, 2021. In other words, should the tunnel system have been operational on July 16th, operational staff would have had an estimate of the magnitude of inflow more than a day before the event occurred. Over the course of the deployment, the BI dashboards were expanded to also collect NWS river stage forecasts and actual rainfall data through the application programming interface (API) provided by ADS as part of the PRISM interface to Citizens' rain gauge and flow meter network. Using the API key, Citizens developed a process to compare the forecasted rainfall from NOAA to the actual rainfall from the gauge network. From March 2021 through July 2021, the total forecasted rainfall was 23.5 inches, compared to 22.3 inches of actual gauge rainfall. An encouraging finding is that seventy-six percent (76%) of the rain events were forecasted within two-tenths of an inch of the actual gauge rainfall. We believe our presentation will benefit any wastewater utility that is operating or soon to be operating a wet-weather storage, treatment, or pumping facility to address combined sewer or sanitary sewer peak flows. We will provide a roadmap for any utility to implement a similar AI/ML solution for predictive operational decision support that can be implemented without any commercial software cost.

This paper was presented at the WEF Collection Systems Conference in Detroit, Michigan, April 19-22.

SpeakerRanck, Chris

Presentation time

14:00:00

14:30:00

Session time

13:30:00

16:30:00

Session number10

Session locationHuntington Place, Detroit, Michigan

TopicCombined Sewer Overflow, Machine Learning, Predictive Analytics

Author(s)

C. Ranck

Author(s)C. Ranck1; D. Sutton2; C. Bowers3

Author affiliation(s)Black & Veatch1; Citizens Energy Group2; WEF Member Account3

SourceProceedings of the Water Environment Federation

Document typeConference Paper

PublisherWater Environment Federation

Print publication date Apr 2022

DOI10.2175/193864718825158338

Volume / Issue

Content sourceCollection Systems

Word count15

Why Model Real Time When You Can Model the Future? The Fort Wayne Experience

Abstract

Our presentation will summarize the development and implementation of the forecasting tool for Fort Wayne City Utilities' collection system model. The tool uses an embedded Python script in PCSWMM to obtain the NOAA rainfall forecast and NWS river stage forecast to simulate 48 hours into the future for operational insight. In addition to the forecasting tool, a cloud-based Azure Function was developed to run the model in the cloud and reduce wear and tear on City computers. Fort Wayne City Utilities provides water, wastewater and stormwater to the second largest city in Indiana with approximately 72,200 customers serviced by 1,500 miles of sewers over a 68 square mile service area. City Utilities is presently implementing a combined sewer overflow (CSO) consent decree that will complete in 2025 that includes the Three Rivers Protection & Overflow Reduction Tunnel (3RPORT). As the tunnel and other consent decree projects are brought online, City Utilities needs to leverage its long term investment in hydraulic modeling by incorporating near-term forecasting. Figure 1 presents the forecasting process. The forecasting tool uses an embedded Python script in PCSWMM to obtain the following public domain, free forecasts: 1.NOAA High Resolution Rapid Refresh (HRRR). The 2.5-km, 1-hour resolution forecast provides the next 18 hours of rainfall for the wastewater service area. 2.NOAA Global Forecasting System (GFS). The 20-km, 3-hour resolution forecast provides the next 72 hours of temperature. 3.NWS Advanced Hydrologic Prediction Service (AHPS). The 6-hour resolution forecast provides the next 4-5 days of river stage from multiple gauging stations. Located at the confluence of the St. Marys, St. Joseph, and Maumee Rivers, the collection system is influenced by high river stages. The NWS river forecast is obtained by a simple web scraping technique while the NOAA forecasts are obtained using the MetPy library (Unidata, 2021). The three forecasts are then formatted to PCSWMM's structure, automatically added to the model as rainfall, climate, and river stage time series files, with the model simulation time set to the reflect the forecast period. Figure 2 presents City Utilities' model and the embedded script, which can be used to automatically run the model after obtaining the forecast. The forecasting script was completed in October 2022 and has been utilized by City Utilities staff ahead of storm events. For the October 31, 2022 event, the HRRR forecast ranged from approximately a quarter of an inch to two-thirds of an inch. Table 1 presents a comparison of the modeled forecast to actual metered CSO data. As shown in the table, the actual rainfall was consistent with the NOAA forecast, and forecast model is conservative consistent with City Utilities' periodic comparisons of historical model simulations and outfall metering data. Similar to many combined sewer communities with a Consent Decree, City Utilities frequently simulates a typical 5-year period as part of assessing CSO controls. The 5-year duration has provided challenges to staff running the long-term simulation on their laptops. To ease the burden on City Utilities' staff laptops, an Azure Function was developed to run the model in the Microsoft Azure cloud environment as a lower-cost alternative to a virtual machine (VM). The Azure function loads the model input file from an Azure storage container, runs the model using the PySWMM library (McDonnell et. al., 2020), compresses the model output, and saves it to the Azure container for City Utilities staff to download. The Azure function was completed in October 2022 and is presented in Figure 3. We believe our presentation will benefit any wastewater or stormwater utility with a hydraulic model and interest in forecast modeling or running the model in the cloud, applied hands-on by utility staff. We will present our lessons learned in deploying the forecasting tool and the cloud application and benefits to City Utilities.

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

SpeakerRanck, Chris

Presentation time

10:45:00

11:15:00

Session time

08:30:00

11:45:00

SessionSession 15: Optimization & Modelling

Session number15

Session locationKansas City Convention Center

TopicPressurized Systems, Integrated Planning, Intelligent/Smart Sewer Systems, Asset Management and CMOM, Wet Weather Management & Control (CSOs/SSOs)