This document summarizes several work packages and deliverables related to pressure management. It discusses developing optimization algorithms to define optimal pressure zones and control schemes. It also covers integrating pump control with pressure control to minimize treatment and energy costs while maintaining good customer service and leakage. Case studies will apply these methods to real networks in Harrogate and Oldham.

1. RPA2 – Pressure Management RPA 2 Pressure Management University of Leicester WP 2.1 Redesign of press. schemes Exeter WP 2.2 Feedback control schemes Leic./De Mont. WP 2.3 Control Algorithms Leic./De Mont WP 2.4 Supervisory control scheme Leic./De Mont. WP 2.5 Integration of Pump Control De Montfort WP 2.6 Leakage & Burst Monitoring Leic./De Mont . DELIVERABLES

2. (Re)Design of Pressure Control Schemes RPA2 Work Package 2.1 Haytham Awad

3. Outline The net benefit cost model for pressure (re)zoning The optimisation algorithm for automated (re)zoning Demonstration off the above on the case study Objective Develop a methodology and software to define optimal zones for pressure management

4. Net benefit cost model Evaluating benefits and costs assuming the use of PRVs Developed as a standalone methodology using a GUI dialog Used for the optimisation algorithm that will automate the process of selecting, locating and setting up PRVs The methodology addresses benefits and costs arsing from: Reduced water leakage Reduced pipe burst frequency Reduced pressure-sensitive demand Reduced energy consumed Reduced active leakage control effort Reduced frequency of customer contacts Installation/operation of PRVs

5. GUI dialog

6. Output report Net Benefits & Benefit/Cost ratios using: Fixed setting PRVs Time-modulated PRVs (two/four switching periods) Flow-modulated PRVs Pressure at critical point for different schemes Total Head upstream and downstream the PRV location

7. Implementation 4 DMAs analysed (E020, E023, E025, and E204) Importance of the estimation of different pressure management related benefits and costs in addition to the conventional ones This seems to apply particularly to the net benefits associated with: burst frequency reduction pressure-sensitive demand reduction and customer contacts reduction

8. (Re)zoning algorithm Objective: m aximise total net benefit De cision variables: Locations and settings of PRVs introduced to form pressure zones PRV X X X X Closed valve

9. (Re)zoning algorithm

10. 33 Tree - 13 Connected - 37 Closed Example

11. Optimal steady state pressure control Hossam AbdelMeguid

12. Content Aim Recent technical developments Practical impact Deliverables and future work

13. Aim Develop theory and software and solve case studies for optimal steady state pressure control in the form of time schedules flow modulation curves

14. Recent technical developments Modification of the algorithm to include boundary and internal PRVs Implementation of GUI to pressure control module (PCM) Solution of the case studies E067 and E093 Isolation of the PCM from FINESSE and connection to PIGM Boundary PRV Boundary PRV Internal PRV

15. Technical developments

16. Technical developments

17. Command-line FINESSETools E067.pcm  Technical developments All other applications Read / write 800xA PGIM Read / write HDA / DA FINESSE modules, e.g Pressure Control, Pump Scheduling, Demand Prediction OPC clinet tool

18. YWS case-studies DMA E067-Waterside case study, leakage reduced by 30% DMA E093-Barrowby case study, leakage reduced by 45%

19. Practical applications and impact Leakage management and cost analysis Calculation of optimal time schedules for PRVs Calculation of optimal flow modulation curves for PRVs Used by supervisory pressure control system Embedded into decision support software Deliverables and future work D2.3 - Pressure control case studies, reports Future work – case studies for A016-HP and J712-HP DMAs equipped with Aquai-Mod controllers

20. Dynamic pressure control Bogumil Ulanicki

21. Aim Develop theory and software for robust dynamic pressure control for application in: electronic controllers hydraulic controllers

22. NEPTUNE Workshop Esholt Hall, 9th July 2008 PID controller uses only pressure measurements Previous work

23. NEPTUNE Workshop Esholt Hall, 9th July 2008 Modified controller uses information about both the outlet pressure and the valve flow. Recent technical developments

24. Proposing a new controller Performing simulation studies using Matlab/Simulink NEPTUNE Workshop Esholt Hall, 9th July 2008 Recent technical developments

25. Simulation result of pressure tracking with a linear flow modulation curve NEPTUNE Workshop Esholt Hall, 9th July 2008 Recent technical developments

26. Shows potential performance of electronic controllers Some ideas can be transferred to a hydraulic controller Reference point to compare the dynamic performance of the hydraulic controllers, e.g. Aquai-Mod controller Deliverables and future work D2.3 - Pressure control case studies, reports Future work – case studies for A016-HP and J712-HP DMAs equipped with Aquai-Mod controllers Generalisation of the controller algorithm for DMAs with many inlets NEPTUNE Workshop Esholt Hall, 9th July 2008 Practical applications and impact

27. Test rig experiments on Aquai-Mod controller Piotr Skworcow, Hossam AbdelMeguid (DMU) David Hurley, Mark Lock (Aquavent)

28. Aims Test Aquai-Mod controller in a test rig and field conditions Derive and calibrate a mathematical model of the controller Compare the performance of the hydraulic controller against the electronic controller through simulations

29. Recent technical developments Purchase and installation of the monitoring equipment: pressure transmitters, LVDT, flow meter, data acquisition card, LabView software, etc. Derivation of a mathematical model of the controller Carrying out test sessions

30. Mathematical model Understanding the valve operations General equations Parameter values derived from the controller dimensions Parameter values derived from measurements Model validation from dynamic measurements

31. Test set-up

33. Preliminary measurement results The measured flow modulation curve

34. Practical applications and impact Understanding steady-state and dynamic properties of the controller Benchmarking against standard pilot valve; assessing its value as a replacement for the traditional pilot valve Suggesting potential improvements to the controller Deliverables and future work D2.4 - Report on the test results Finishing the measurements, calibration of the model

35. Combined energy and pressure management Hossam AbdelMeguid, Peter Bounds, Piotr Skworcow , Bogumil Ulanicki

36. Purpose Proposed solution Software implementation Case study Conclusions Outline

37. Purpose Minimize cost of water treatment and pumps energy usage whilst supplying good service to customers without unnecessarily high pressure Integration and co-ordination of pump control in grid with pressure control of DMAs Pump scheduling Pressure control Combined energy and pressure management Calculate time schedules for treatment works, pumps, valves, reservoirs and PRV setpoints

38. Proposed Solution Formulate as optimisation problem; cost function: Total cost of water treatment and pumps energy use Inclusion of PRV model – setpoint as decision variable Network model with pressure dependent leakage: Excessive Pumping High energy cost High pressure Increased leakage More water from sources and to be pumped £ £ £

39. Implementation Embedded into the FINESSE software

40. Case Study 1 Harrogate (Yorkshire Water): 10621 Nodes 11293 Pipes 14 PS 6 Reservoirs 243 Valves Total Demand: 400 l/s

41. Case Study 2 Oldham (United Utilities): 3 WTW 10 Reservoirs 11 PS 80 DMA 3 Ind. Users 4m kWh £280k

42. Conclusions Minimize cost of water treatment and pumps energy usage whilst supplying good service to customers without unnecessarily high pressure Combined energy and pressure management Formulation as optimisation problem with network model with pressure dependent leakage Solver embedded into FINESSE software Cases to be solved: Harrogate (Yorkshire), Oldham (UU)