A Bottom-up Approach to Estimate Dry Weather Flow in Minor Sewer Networks
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Transcript of A Bottom-up Approach to Estimate Dry Weather Flow in Minor Sewer Networks
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