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J. A. Elías-Maxil Jan Peter van der Hoek Jan Hofman Luuk Rietveld SPN7.

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Presentation on theme: "J. A. Elías-Maxil Jan Peter van der Hoek Jan Hofman Luuk Rietveld SPN7."— Presentation transcript:

1 J. A. Elías-Maxil Jan Peter van der Hoek Jan Hofman Luuk Rietveld SPN7

2  Sustainability of the urban water cycle ◦ 80 % of energy input to urban water is heat  Strategies to improve sustainability: Heat recovery installations ◦ Operates in main sewers  Significant potential for heat recovery in small sewers

3  To estimate the potential temperature and flow data is needed  Flow measurements are some times difficult to obtain in small sewers ◦ Low flow rates ◦ Intermittent ◦ Difficult access ◦ Costly

4  Prediction of wastewater flow with little and if possible no measurements  Possibility to calculate intermittent wastewater flow  Possibility to use the flow patterns to calculate wastewater quality (temperature)

5  Wastewater flow modeling in sewer (a) ◦ Probability theory to produce expected flow ◦ Intermittent discharges from water consuming appliances were converted to continuous base flow ◦ The flow rate and arrival time at a certain point of the sewer was modeled with Saint Venant equations Background MethodsResultsConclusions (a) Butler, D. and N. J. D. Graham (1995). J. Environ. Eng. 121(2):

6 1. Stochastic modeling (Drinking water) ◦ Generation of water pulses ◦ Different for every activity 2. Adapted to wastewater discharge 3. Attenuation of intermittent flow Blokker, E. J. M., et al. (2010). Jour. Water. Res. Plan. and Man. 136(1): Background MethodsResultsConclusions

7  North of Amsterdam  97 household connections ◦ Clustered in 51 connections for the model  ~ 15 days  Geometry ◦ Mean slope < 2% ◦ PVC 250 mm  2 Monitoring campaigns Background Methods ResultsConclusions

8  Flow measurement by pumping time Background Methods ResultsConclusions

9  Generation of wastewater discharge patterns Background Methods ResultsConclusions

10  Generation of wastewater discharge patterns Equivalent Appliance D, sI, l/st s, sD s, s Shower Same as D Kitchen tap16|48|15| |0.125|0.0 83| Same as D Toilet | |609 Bathroom tap40 | | Same as D Wash machine120*0.167|0.083|0.0 83| |1260|114 0| * Dish Water21*0.19*1800*120* |: Separation of sub-activities or cycles *: The same parameter was included in the remaining 3 cycles Background Methods ResultsConclusions

11  Generation of wastewater discharge patterns Background Methods ResultsConclusions

12  Mean flow rate / day  Maximum flow rate in time period / day Flow patterns divided in time segments (6s – 1hr) Flow patterns divided in time segments (6s – 1hr) Q mean & Q max Q mean & Q max Percentiles of cumulative results obtained Comparison: RMSE R 2 Comparison: RMSE R 2 Background Methods ResultsConclusions

13 ModeledObserved Average daily flow, l/s ±0.3* *Expected flow from surveys: 0.4 l/s BackgroundMethods Results Conclusions

14 BackgroundMethods Results Conclusions

15 BackgroundMethods Results Conclusions

16 BackgroundMethodsResults Conclusions  A model that includes 1.Stochastic simulation of drinking water demand 2.Transformation of pulses to wastewater generation 3.Attenuation of discharge to the sewer Was found to be adequate to model the wastewater flow rate of a small sewer  The prediction was stable for time frames from 6 seconds to 1 hour ◦ RMSE ~ 20% ◦ R 2 > 85%  Future work: Validation of temperature model

17 Temperature Model Along the pipe Along the water depth Along the Distance of the pipe

18 Thank you!

19  Detection of pump intervals Background Methods ResultsConclusions

20 A2.Measurements  Error analysis of measurements  Hydraulic model calibration ◦ Roughness ◦ Pump capacity Effect of time resolution Level readings Background Methods ResultsConclusions

21 ParameterMeasured Conf. Int. (t on -t off )3.2, s Conf. Int. (t off -t off )3.1, s Roughness15, mm C-W Pump capacity8.24 ± 0.47, l/s BackgroundMethods Results Conclusions


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