A Transformation in Collection System Optimization: The Implementation of a Real Time Decision Support System in South Bend, IN

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1 A Transformation in Collection System Optimization: The Implementation of a Real Time Decision Support System in South Bend, IN August 25, 2015 Timothy Ruggaber, P.E. Patrick Henthorn, P.E.

2 Agenda Real Time Information and Optimization South Bend, IN Case Study Turning Data into Intelligence Model vs. data Which to trust? Turning Intelligence into Action Real Time Control Changing the future

3 The Claim We have the smartest sewers in the world. Mayor Pete Buttigieg South Bend, IN named one of the country s 10 Smartest Cities (Cisco Systems)

4 Real Time Control (RTC) Operating control assets on a locally reactive basis RTC is just inline storage based on local conditions Real Time Decision Support System (RT-DSS) Globally optimize and coordinate collection system operation through control recommendations We want to operate the entire collection system as an extension of the treatment plant.

5 RT-DSS Objectives Collect and transform rain gauge, sensor, and model data into actionable settings for each control asset in the collection system Globally, dynamically coordinate and optimize all key assets in the collection system during each unique storm event Visualize and dashboard complete system performance in real time and replay past storm events

6 Real Time Decision Support System RT-DSS

7 Background City of South Bend, IN 101,000 people St. Joseph River 40 mi 2 (20 mi 2 Combined) 36 CSOs 77 MGD WWTP $630M LTCP

8 Overflow History Before 70% reduction After Real Time Monitoring System Implementation Real Time Control System Implementation

9 Water Quality Improvements E. coli Concentrations Dry Weather Samples

10 Water Quality Improvements E. coli Concentrations Wet Weather Samples

11 TURNING DATA INTO INTELLIGENCE

12 The Backbone Real Time Data Installed in monitoring points 5 min real time data Focused along river Integrated with SCADA Uses System Characterization Blockage Detection Model Calibration Proactive Maintenance Real Time Control 40,000 data points daily!

13 Data vs. Good Data Auto-check sensors Early morning Most repeatable Also used for I/I Analysis All Day Backup prevention Flood prevention WW vs. DW User Defined /Text alert system

14 SCADA Integration Full SCADA integration Raw data Day to day operations Alarming functions Operations & maintenance

15 Deviations from Normal Small blockage forms CSO is vactored Weir elevation Average dry weather depth 16

16 Visualize

17 Dry Weather Overflow Elimination 100% DWO reduction from 2008 to 2011

18 Wet Weather Analysis EPA SSOAP Toolbox Integrate database with SSOAP Real time I/I analysis Track changes in I/I sources Follow impacts of sewer degradation

19 SSOAP Toolbox

20 Real Time I/I data

21 Groundwater Infiltration Detection 10 MGD of Dry Weather I/I removed since 2009

22 PACP and Groundwater

23 Sediment Detection SAVE

24 Increased O&M Benefits (Direct) Use of vactor trucks 50 additional days annually = $133K Clean 2,000 additional catchbasins annually = $40K Increase number of sewer inspections by 175% = $29K Same Staff Total Dynamic Maintenance Value = $202,000 Annually 25

25 Increased O&M Benefits (Indirect) Discover and eliminate I/I sources = 10 MGD decrease Find and address maintenance hotspots = Clean what needs to be cleaned when it needs it Pre-/post-construction monitoring Flood/backup warning system Sediment detection Indirect Value = $1.5 M Annually It s like hiring more personnel, but without the cost. Gary Gilot, President, Board of Public Works, City of South Bend, IN 26

26 BRING IN THE MODEL

27 Model vs. Real Life Model Data vs. RT Data Large differences in 7 areas Additional monitoring reduce new infrastructure CSO 028, May 2011 Model 55 hr. of overflow Data 19 hr. of overflow

28 Real Time Modeling Provide baseline Identify discrepancies Model vs. Reality Continuous, Real time Resolve discrepancies Infiltration rates High river stage Sediment

29 Real Time Modeling Automatic 15 min frequency Use real time rain data Hour simulation Hot start Extract 5 min data Depth Flow Velocity Any node or link Send data to website

30 Real Time Modeling Same site 1 week apart Entirely Different Response Why?

31 Real Time Calculation Convert RT Depth into RT Overflow Continuous calculation Include: Weirs Raised orifices Velocity head Duckbill valves St. Venant Equations Storm Classification

32 Overflow Volumes Measured vs. Modeled Overflows Baseline Model 2013 Revised Model Measured OF

33 Conventional Modeling Source: US EPA SWMM 5.0 Help Tutorial

34 Cognitive Hydraulic Response System Pattern Recognition System 5 years of available data Eliminate uncertainty in model Continuous calibration possible

35 CHRS Results Accurate Model = Right-Sized LTCP Direct Preand Post Construction Comparison Platform for Optimization Measured Data CHRS Data

36 TURNING INTELLIGENCE INTO ACTION

37 Precipitation Temporal and Spatial Variability

38 How Optimization Works WWTP: I ve got capacity at $2 per gallon CSO 044: CSO 044: Too rich for I ll buy it! my blood Interceptor: I ve got capacity at $3 per gallon CSO 003: Wait, I ll pay you $4 a gallon! CSO 22: WWTP is too expensive now I think I ll store Storage Basin: I ve got capacity at $3.50 per gallon Sources: Esri, HERE, DeLorme, USGS, Intermap, increment P Corp., NR China (Hong Kong), Esri (Thailand), TomTom, MapmyIndia, OpenStre User Community

39 Optimization Opportunity

40 Regulator Selection Selection Criteria D/S interceptor capacity Size of existing throttle pipe Peak flow in CSO trunkline Overflow volume Overflow frequency PM hotspot Proximity to river crossing 9 Regulators selected 26% of combined area 85% of total potential benefit

41 Regulator Automation

42 Distributed Real Time Control

43 Valve Installation 9 Control Valves 12 to 30 diameters $2.2 M Completed in Spring, 2011 Start-up Comm. connections Battery backup Valves CCTV verified Valves partially open

44 Modes of Operation Local Remote Manual From cabinet From SCADA Auto Local Reactive Distributed RTC Weekly valve exercise Fail-Safe UPS opens valve default position 30-50% open

45 SCADA Integration

46 Key Stakeholders Public Works Environmental Services (CSOs and WWTP) Long Term Control Plan Team Sewer Department Engineering Parks Regulators Rate Payers Implementation Team

47 Crawl: Manual Tests May 12, 2011 storm event Manually adjust two valves Crews stationed downstream SCADA operator had overview CSO % reduction (0.24 MG) CSO % reduction (0.37 MG) Goal: Proof of concept

48 Walk: Automation in Background August, 2011 Simplified Algorithm Implemented at 2 CSOs Only look at interceptor Start with fewest inputs possible The Gateway: Collects data Runs algorithm Provides valve setpoints Valve not allowed to move Goal: Understand algorithm

49 Speed Walk: Partial Automation November, 2011 Enable automation at 2 CSOs ±10% valve movement Base control on interceptor Goal: Confirm mechanics

50 Jog: Conservative Automation February, 2012 Full range of valve motion Interceptor and regulator Simplified, local communication Use conservative trigger points no surcharge Start just prior to overflow Goal: Increase comfort

51 Run: Full Automation May, 2012 and ongoing Distributed control Controlled surcharge Aggressive trigger points Minimize backflow Goal: Continual improvement

52 RTC Performance Review

53 South Bend Algorithm Performance 1 3 2

54 Algorithm Performance Overflow Original Overflow RTC Overflow duration dropped from 19.5 hrs to 2.3 hrs Overflow volume dropped 76% (0.87 MG to 0.21 MG)

55 CSO Overflow Reduction May July, 2014 Overflow Volume Original System (MG) Overflow Volume RTC System (MG) Volume Reduction (MG) Percentage Reduction % % % % % % % % % TOTAL %

56 RTC System Result Better use what you already have, then build new infrastructure Stakeholder buy-in is essential Faster implementations for future projects 55% overflow reduction at control sites 23% overall overflow reduction After WWTP expansion complete

57 The Long Term Control Plan The $630M Elephant in the Room

58 LTCP Program Phase 2 CSO Controls

59 Control Concept 1 Optimization to Avoid WWTP Expansion and Parallel Interceptor

60 Control Concept 2 Backdoor Interceptor

61 Control Concept 3 Get Rid of the Pipe No One Wants

62 Keep Going! Real Time Monitoring System Implementation Real Time Control System Implementation

63 Summary Real Time Intelligence opens up whole new worlds Improved O&M $200k/year direct, $1.5 M/year indirect Elimination of DWO Improved Modeling Highlight discrepancies Improved Operation Automated Throttles Proactive Maintenance 70% Overflow Reduction Saved City $120M to date Can save substantial portion of LTCP cost Smartest Sewers in the World