The practical uses of PODDS

Transcription

1 The practical uses of PODDS

2 Practical uses of PODDS Neil Croxton Principal Modelling Engineer Civil Engineering United Utilities The practical uses of PODDS Prediction Of Discolouration in Distribution Systems

3 Agenda Overview of the discolouration problem introduction to PODDS project Theory modelling practical uses

4 Background 5,400 square miles a population of over 7 million Over 2,500 district metered areas 455 service reservoirs 626 pumping stations 127 water treatment works treating 2,000 million litres of water daily 1,444 kilometres of aqueduct 40,000 kilometres of water main Carlisle Preston Liverpool Manchester Crewe

5 The discolouration problem 2008 ~ 2,000 complaints 2009 ~12,000 complaints 2010 ~10,000 complaints 2011 ~ 4,000 complaints

6 The discolouration problem Water, usually supplied like this sometimes like this

7 Discolouration - where from and why? corrosion products / biofilms historical output from treatment works complex water chemistry poor cleaning Why? bursts operational changes operator error increased demand illegal use

8 Turbidity what does it look like? 40 NTU 1 NTU 4 NTU 90 NTU 190 NTU

9 PODDS 3 aspects to consider 1. a research project undertaken by Sheffield University 2. a theory of how discolouration happens 3. a predictive software tool

10 PODDS the research project a research project undertaken by Sheffield University supported by some UK water companies a theory of how discolouration happens principal proponents: Prof Joby Boxall & Dr Stewart Husband the aim is to predict where discolouration is likely to occur - how much? when?

11 PODDS the theory

12 TRADITIONAL sediment theory normal flow increase to well above normal flow giving > 0.7 ms -1 = discolouration

13 the PODDS theory normal flow ANY value above normal flow = discolouration

14 PODDS the theory discolouration arises due to the erosion of layers of cohesive sticky material that builds up on the pipe walls Layers get used to the normal daily flow anything above this conditioned flow erodes the layers, causing discolouration

15 PODDS theory in motion pipe wall conditioned al flow; shears no l ayers removed pipe is NOT conditioned to No 2discolouration nd day higher flow; on 1 st day discolouration occurs build-up of material flow through pipe peak flow 1 st day flow broadly similar over previous few months peak flow 2 nd day

16 PODDS the software EPANET based public-domain open-source software simple or complex models hand-built or exported from other software Currently building up a set of field test based turbidity parameters searching for the ideal set based on pipe characteristics

17 Calibrating the PODDS model all mains model source flow simple model demand

18 Imposing the flow and monitoring the turbidity portable self-contained turbidity monitoring equipment x2

19 Model Calibration to WQ data modelled turbidity measured turbidity turbidity 2 days PODDS Model parameters altered so that model = reality (most influential being the value which represents the rate at which material is mobilised) imposed flow to create turbidity response flow - 2 days

20 Practical uses of PODDS some examples

21 EPAnet movement of turbidity across the network 400 l/s +50 l/s +50 l/s +50 l/s +50 l/s

22 EPAnet normal flow 09:00

23 EPAnet 2NTU 5NTU +30NTU 15NTU +50 l/s for 30 mins 10:00

24 EPAnet 2NTU 5NTU back to normal flow 11:00

25 EPAnet 2NTU 5NTU 12:00

26 EPAnet 2NTU 5NTU 13:00

27 EPAnet 5NTU 2NTU 14:00

28 EPAnet 5NTU 2NTU 15:00

29 EPAnet 5NTU 2NTU 16:00

30 EPAnet 5NTU 2NTU 20:00

31 EPAnet 2NTU 5NTU 24:00

32 EPAnet 5NTU 28:00

33 EPAnet back to normal turbidity levels >20 hours after 30 min flow event 31:30

34 Uses of PODDS priority ranking one more to construct

35 Uses of PODDS priority ranking modelled Turbidity (NTU) % 10% 20% Ranking of trunk mains using potential to cause discoloured water - modelled Turbidity (NTU) against increase in Flow (%) - PIPE 6 10% increase in flow = 4NTU HIGH RISK PIPE 1 >100% increase in flow = <4NTU LOWER RISK 30% 40% 50% 60% extra Flow (%) 70% 80% 90% 100% pipe 1 pipe 2 pipe 3 pipe 4 pipe 5 pipe 6 4 NTU network PCV failure

36 Uses of PODDS reducing the risk 1. Allows flow increase while maintaining a low turbidity response 2. Facilitates gradual removal of deposits, effectively cleaning or reducing the risk of future discolouration increases the headroom.

37 Uses of PODDS - summary to compare the potential discolouration risk to determine where the greatest investment need is required. cost savings by avoiding or deferring mains replacement operational charts used to gradually increase flow operational charts used to lower the risk of discolouration cleaning the pipes while maintaining the flow PODDS theory in a wider sense is being used regularly in the design of engineering solutions. mitigating risk in operational changes and enabling works large-scale cleaning of small diameter plastic mains using conditioning flow to design robust flushing schedules

38 Summary the discolouration problem introduction to PODDS project theory modelling field trials calibration practical uses priority ranking, getting the best use out of our finances lowering the risk regular use of PODDS concepts in engineering design

39 Neil Croxton, Principal Modelling Engineer, Clean Water Network Modelling Engineering and Capital Delivery United Utilities Thanks, any questions?

40 Uses of PODDS operational charts Changing flows in large diameter mains data lines produced by running scenario versions of the PODDS model with different initial conditioning flows e.g. 100, 150, 200 etc