System for early warning of deteriorating water quality in distribution networks

Transcription

1 System for early warning of deteriorating water quality in distribution networks Mirjam Blokker, Ilse Pieterse-Quirijns, Claudia Agudelo-Vera (KWR Watercycle Research Institute) Veere Sperber (Brabant Water) 1

2 Deteriorating water quality in DWDS Source: KNMI With climate change: Warmer summers Temperature of the drinking water in the DWDS may increase This may lead to increase in microbial activity in the DWDS No chlorination in the Netherlands. Bridging science to practice Aarhus, 21 23, January

3 How to design an Early Warning System? DWDS is complex system, impossible to measure everywhere and on time model is required PVC AC CI transit point Pumping station measurement location 1) Predict T_soil 2) Predict T_water 3) Predict growth of micro-organisms in DWDS 500 m 3

4 EWS development 4

5 DWDS model All pipes, stochastic demands PVC AC CI transit point Pumping station measurement location B PVC AC CI transit point Pumping station measurement location A Q (l/5 min) :00 6:00 12:00 18:00 24:00 ti 30 Q (l/5 min) Skeletonized model 500 m Prepared enabling change 0:00 0 6:00Aarhus, 12: :00, January 24:

6 EWS development 6

7 Soil temperature model The heat transfer processes : a) atmosphere and soil surface b) atmosphere and roughness layer c) roughness layer and soil surface d) soil surface and soil e) soil and pipe wall Blokker and Pieterse-Quirijns, 2013 Bridging science to practice Aarhus, 21 23, January

8 Modelling soil temperature Local parameters Soil type Surface cover Meteorological information Actual records KNMI *Interpolation Soil temperature model Actual soil temperature T soil,act 8

9 EWS development 9

10 Forecasting soil temperature Local parameters Soil type Surface cover Meteorological information Actual records KNMI Historic forecast HIRLAM 2 days *Interpolation *Interpolation Soil temperature model Actual soil temperature T soil,act Absolute error T soil,pred - T soil,act Predicted soil temperature T soil,pred 10

11 Accuracy in T_soil prediction 30 T_atm large deviations 25 Frequency Modelled Soil Temperature at -1.0 m ( C) 0 28 Based on HIRLAM predictions Based on actual meteorological records KNMI absolute error of predicted atmospheric temperature ( C) T_soil quite accurate Jun Jul Aug 11

12 EWS development 2 days ahead 7 days ahead 12

13 Modelling water temperature Assume T_soil as fixed; Heat transfer from soil through pipe wall to flowing water PVC pipe heat exchange times 13

14 Accuracy in T_water prediction Transport main 1.2 m cover, Ø m depth of heart Model T soil@-1.65 m, grass, sand = 14 C 18.5 km, 0.5 m/s 10.3 h AC pipe T PS, 0 km = 17.5 C T TM, 18.5 km = 16.7 C (measured) measured T TM, 18.5 km [ C] model with T soil, R 2 = 0.98 T PS, 0 km, R 2 = 0.91 x = y modelled T TM, 18.5 km [ C] Model T water = 16.7 C 14

15 Accuracy in T_water prediction Meas. 3 july 2013 Distribution system 1.0 m depth of heart T soil@-1.0 m, road, sand = 17 C AC and PVC pipes, <160 mm T TM, 0 km = 15.5 C T DN, xx km = 15.9 C (measured) C Boezelgracht 16.8 C Barkentijn Donate Steurhof 15.9 C 15,5 C Volendam Model T water = 16.1 C Minimum model: DS: 16,1 C Donata Steurhof, TD Donata Steurhof, BU1 Donata Steurhof, BU2 ingaand Ø500 Prepared enabling change 15.5 Aarhus, 21 23, January :00 03:00 06:00 09:00 12:00 15:00 18:00 21:00 00:00

16 T_water in Eindhoven PVC AC CI transit point Pumping station measurement location T_soil unknown 500 m 16

17 EWS development 2 days ahead 7 days ahead 17

18 Modelling micro-organisms X b X a 18

19 Modelling micro-organisms From 1 to 1,000 in 48 hours x µ [s -1 ] m.o. I m.o. II m.o. III T [ C] Aeromonas, µ opt = 7e-5 T min = 5 ºC T opt = 25 ºC 19

20 Micro organisms in Eindhoven modelling results High variation over the day and between days How to verify this model? 20

21 Micro organisms in Eindhoven PVC AC CI transit point Pumping station measurement location 500 m 21

22 EWS development 2 days ahead 7 days ahead 22

23 EWS model, as strong as the weakest link 2 days ahead 7 days ahead 23

24 Discussion The predicted temperature and number of microorganisms in the DWDS depend on customers demand. The predictions vary during the day, between days and between (closely located) nodes. A qualitative prediction of the microbial activity at each node is the highest possible that can be achieved with an EWS. No model for biofilm growth was included yet. 24

25 Discussion The predictions were based on a virtual microorganism, still conclusions based on measurements of bacteria, like Aeromonas, might be highly unreliable due to the high variations. This information is essential in developing and testing of measurement programmes. Current sampling programmes may be reconsidered. 25

26 Conclusions EWS for microbial growth: dependence on temperature AND on residence times Model can indicate zones with higher growth potential (red / orange / green) Measurements hard to interpret due to high variability (due to demand dependence) 26

27 Aarhus, 21 23, January 2014 Prepared enabling change 27