A Bottom-up Approach to Estimate Dry Weather Flow

in Minor Sewer Networks

J. A. Elías-Maxil Jan Peter van der Hoek

Jan Hofman Luuk Rietveld

SPN7

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

Motivation

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

Motivation

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)

Motivation

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

Related researchBackgroun

d Methods Results Conclusions

(a) Butler, D. and N. J. D. Graham (1995). J. Environ. Eng. 121(2): 161-173.

Eq

Flow

Time

Intermittent inputs

EqFl

ow

Time

Continuous base flow

1. Stochastic modeling (Drinking water)◦ Generation of water pulses◦ Different for every activity

2. Adapted to wastewater discharge3. Attenuation of intermittent flow

Model approach

Blokker, E. J. M., et al. (2010). Jour. Water. Res. Plan. and Man. 136(1): 19-26.

Background Methods Results Conclusions

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

Case Study Background Methods Results Conclusions

Flow measurement by pumping time

Measurements

( 1) ( )

( 1) ( )

[ , ][ , ]

( 1) ( )[ ]off n on n

off n off n

tt t

off n off n

Cap tV

t t

Background Methods Results Conclusions

Generation of wastewater discharge patterns

Modeling approach

5500 6000 6500 7000 7500 8000 85000

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

Seconds

Dis

char

ge, l

/s

Drinking water at time of consumptionWastewater after being usedWastewater at sewer

I1

I2

In

D1

Dn

τ1 τ2 τn

Background Methods Results Conclusions

Generation of wastewater discharge patterns

Modeling approach

Equivalent Appliance

D, s I, l/s ts, s Ds, s

Shower 600 0.123 45 Same as DKitchen tap 16|48|15|

370.083|0.125|0.083|0.083

30 Same as D

Toilet 45-106 0.042|0.884 180|60 9Bathroom tap

40 | 15 0.042 | 0.042 0 Same as D

Wash machine

120* 0.167|0.083|0.083|0.083

3840|1260|1140|600

300*

Dish Water 21* 0.19* 1800* 120*

|: Separation of sub-activities or cycles*: The same parameter was included in the remaining 3 cycles

Background Methods Results Conclusions

Generation of wastewater discharge patterns

Modeling approach

5500 6000 6500 7000 7500 8000 85000

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

Seconds

Dis

char

ge, l

/s

Drinking water at time of consumptionWastewater after being usedWastewater at sewer

τ1+ts τ2+ts τn+ts

Ds1

Dsn

5500 6000 6500 7000 7500 8000 85000

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

Seconds

Dis

char

ge, l

/s

Drinking water at time of consumptionWastewater at sewerWastewater after being used

Background Methods Results Conclusions

Mean flow rate / day Maximum flow rate in time period / day

Comparison

1 1

2 2

1 1

&

n n

n n

Observed

Modeled

t Qt Q

t Qt Q

1 2

1 2

_& _

x

x x

ix n

Obs Mod

Q Q QQ Q

Q Q

Flow patterns dividedin time

segments(6s – 1hr)

_1 max_1

_ max_

_& _

mean

mean i i

Obs Mod

Q Q

Q Q

Qmean

&

Qmax

Percentiles of cumulative

results obtained

Comparison:RMSE

R2

Background Methods Results Conclusions

Modeled ObservedAverage daily flow, l/s 0.38 0.36±0.3*

Results

*Expected flow from surveys: 0.4 l/s

Background Methods Results Conclusion

s

0 1 20

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Qmax(3s), l/s

Cum

ulat

ive

prob

abilit

y

0 1 2Qmax(5min), l/s

0 1 2Qmax(hour), l/s

ObservedSimulated

ComparisonBackground Method

s Results Conclusions

Background Methods Results Conclusion

sComparison

6/60 0.5 10 20 30 40 50 6010

20

30

40

50

60

70

80

90

100

Time scale, min

Per

cent

age

Qmax - RSME

Qmax - R2

Qcumulative - RMSE

Qcumulative - R2

Background Methods Results Conclusion

sConclusions A model that includes

1. Stochastic simulation of drinking water demand2. Transformation of pulses to wastewater generation3. 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% ◦ R2 > 85%

Future work: Validation of temperature model

Temperature Model

Along the pipe

Along the water depth

Along the Distance of the pipe

Thank you!

Acknowledgements

400 600 800 1000 1200 14000

5

10

15

20

25

30

Seconds

Wat

er le

vel,

cm

2 21

2 21

0; 0; 0

0; 0; 0

n n n

n n

y y yOnt t ty y yPump Offt t t

Other

Detection of pump intervals

A1. Measurements Background Methods Results Conclusions

A2.Measurements Error analysis of

measurements Hydraulic model calibration

◦ Roughness◦ Pump capacity

00 05 10 15 20 25 30

On

Off

Seconds

Sensor 1Sensor 2Sensor 3

• Effect of time resolution

• Level readings

Background Methods Results Conclusions

Parameter Measured

Conf. Int. (ton-toff) 3.2, s

Conf. Int. (toff-toff) 3.1, s

Roughness 15, mm C-W

Pump capacity 8.24 ± 0.47, l/s

A3.ResultsBackground Method

s Results Conclusions