21/10/2013
Viability of Heat Recovery from
Combined Sewers
Mohamad Abdel-Aal
PhD Researcher at the University of Bradford
[email protected]
R.Smits**, M. Mohamed*, K.De Gussem**, A. Schellart*, S Tait*
*School of Engineering Design and Technology, University of Bradford, BD7 1PD, UK
** Department of Research, Aquafin, Dijkstraat 8, B-2630 Aartselaar, Belgium
1
Structure
• Why?
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• What?
• How?
• Data
• Model
• Results
• Conclusions
• Future work
2
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What
Heat
loss
Distance = ?
Large temperature drop = Problems in WwTP
3
•
UK Water
Industry
Over 500 wastewater heat pumps are in operation
worldEnergy
wide. Thermal ratings range from 10 kWBreakdown
to 20 MW.
• Sewage temperatures vary between 10 and 250C, in Europe,
all year around
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Why
• In Switzerland; 6000 GWh of thermal energy are lost annually
through the sewage system
4
How
Predictive
(Group method of data handling)
Comparison to literature
In-Sewer Temperatures
Pipe dimensions
Model
Analysis
Data
Deterministic
(Energy balance)
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Estimate distance required to compensate heat losses upstream
Daily variation pattern
Soil Temperatures
Flow Rate
5
• 6 Sites in Antwerp, Belgium
• Wastewater and in-sewer air temperatures, flow and soil
measured every 20 minutes for 6 - 12 months
Site
1
2
3
4
5
6
Average DWF
(m3/hour)
37
49
48
1000
1100
340
Pipe length
Pipe diameter
(m)
464
170
232
1031
1775
749
(m)
1.2
1.3
1.2
1.2
1.3
0.7
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Data- Aquafin
6
Data- Analysis (July)
Site 2 Upstream
Site 3 Upstream
Site 1 Downstream
Site 2 Downstream
Site 3 Downstream
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Site 1 Upstream
22.0
Temperature oC
21.5
21.0
20.5
20.0
19.5
00:01
01:41
03:21
05:01
06:41
08:21
10:01
11:41
Time
13:21
15:01
16:41
18:21
20:01
21:41
23:21
7
1.50 C difference
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Data- Site 2
Average WW temperature drop = 0.70 C or 40 C /km
8
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Data- Site 2
9
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Deterministic Model
Tj
Tj+n
q= thermal energy
w= wastewater, s= soil
m=mass flow rate
cp= thermal heat capacity
𝒎𝒄𝒑 𝑻𝒋 − 𝑻𝒋+𝒏 = 𝒒𝒘𝒂 + 𝒒𝒘𝒔 + 𝒒𝒓𝒆𝒄𝒐𝒗𝒆𝒓𝒆𝒅
10
Deterministic Model
1
𝑞𝑤𝑎 =
𝑇
− 𝑇𝑎𝑖𝑟
𝑅𝑤𝑎 𝑤𝑎𝑡𝑒𝑟
𝑞𝑤𝑠
Parameter
Thermal Resistivity between Wastewater and Air
Thermal Resistivity between Wastewater and Soil
Wastewater Temperature
In-Sewer Air Temperature
Soil Temperature
Mas flow rate
Specific Heat Capacity for water
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𝒎𝒄𝒑 𝑻𝒋 − 𝑻𝒋+𝒏 = 𝒒𝒘𝒂 + 𝒒𝒘𝒔 + 𝒒𝒓𝒆𝒄𝒐𝒗𝒆𝒓𝒆𝒅
1
=
𝑇
− 𝑇𝑠𝑜𝑖𝑙
𝑅𝑤𝑠 𝑤𝑎𝑡𝑒𝑟
Abbreviation
Value in Feb
Units
𝑅𝑤𝑎
𝑅𝑤𝑠
𝑇𝑤𝑎𝑡𝑒𝑟
𝑇𝑎𝑖𝑟
𝑇𝑠𝑜𝑖𝑙
m
cp
0.04
0.5
11.6
9
9.5
14
4.2
m2/0C
m2/0C
0C
0C
0C
Kg/s
kJ/kg.0C
11
Site 1
Site 2
Site 3
Site 4
Site 5
Site 6
y = 0.9248x + 1.45 y = 0.8619x + 2.77 y = 0.9555x + 0.93 y = 1.0478x - 0.22 y = 1.2014x - 1.94 y = 1.2734x - 2.57
R² = 0.998
R² = 0.995
R² = 0.997
R² = 0.989
R² = 0.998
R² = 0.992
Modelled downstream temperature 0C
21
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Deterministic Model- Results
19
17
15
13
11
9
9
11
13
15
17
Measured downstream temperature 0 C
19
21
12
Deterministic Model- Sensitivity Analysis
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Change in downstream temperature
270%
220%
170%
120%
70%
20%
-30%
-80%
25%
50%
100%
200%
Percentage of upstream temperature (100% = default value)
400%
13
Deterministic Model- Sensitivity Analysis
2.5%
2.1%
Pipe thermal conductivity, kp
Soil thermal conductivity , ks
1.7%
Flow surface width, b
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Change in downstream temperature
In-sewer air temperature Ta
Soil depth, ds
1.3%
Soil temperature, Ts
0.9%
Wastewater flow rate, Q
Wastewater velocity, uw
0.5%
Wetted perimeter, wet.p
0.1%
Water soil thermal resistivity, Rws
Water air thermal resistivity, Rwa
-0.4%
-0.8%
25%
50%
100%
200%
Percentage of default value (100% = default value)
400%
14
• Group method of data handling
• Trained on Sites 1&2 for 6 Months Data (6,000 entries)
• Predicted for Sites 3, 4, 5 & 6 (20,00 entries)
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Predictive Model
Error = 3%
Error = 9.4%
15
20.8
Deterministic model
Predective
y = 0.9962x + 0.0836
R² = 0.994
y = 0.9956x - 0.4502
R² = 0.996
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Predictive Model- Results (DWF )
Modelled downstream temperature
18.8
16.8
14.8
12.8
10.8
8.8
8.8
10.8
12.8
14.8
16.8
Measured downstream temperatures
18.8
20.8
16
Deterministic Model- Application
Business As Usual, Q= 37 m3/h
Soil
In-Sewer Air
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14
Downstream Temperature, 0C
12
10
8
6
4
2
0
0
1
2
3
4
5
6
Sewer Length, km
7
8
9
17
Deterministic Model- Application
Business As Usual, Q= 37 m3/h
20 kW Heat Recovered, Q= 37 m3/h
Soil
In-Sewer Air
21/10/2013
14
Downstream Temperature, 0C
12
10
8
6
4
2
0
0
1
2
3
4
5
6
Sewer Length, km
7
8
9
18
Deterministic Model- Application
14
20 kW Heat Recovered, Q= 37 m3/h
Soil
21/10/2013
Business As Usual, Q= 37 m3/h
250 kW Heat Recovered, Q= 37 m3/h
In-Sewer Air
Downstream Temperature, 0C
12
10
8
6
4
2
0
0
1
2
3
4
5
6
Sewer Length, km
7
8
9
19
Business As Usual, Q= 37 m3/h
250 kW Heat Recovered, Q= 37 m3/h
Soil
14
20 kW Heat Recovered, Q= 37 m3/h
500 kW Heat Recovered, Q= 37 m3/h
In-Sewer Air
Downstream Temperature, 0C
12
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Deterministic Model- Application
10
8
6
4
2
0
0
1
2
3
4
5
6
Sewer Length, km
7
8
9
20
Model- Application
Heat Exchanger Capacity
kW
1,000
250,000
500,000
Energy Recovered
MWh/yr
9
2,190
4,380
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• Heat exchanger operating all year around
• 170kWh/m2 annual heat demand
• 100% heat exchanger efficiency
m2 heated
50
13,000
26,000
21
• Data agrees with literature Schilperoort and Clemens (2009) and
Hoes et al. (2009)
• Field measurements showed heat fluxes in sewers and hence
there is potential for heat recovery
• Models showed R2 = 0.989 to 0.998
• Average errors are 0.3 and 0.40 C error for deterministic and
predictive models respectively
• Simple predictive model- two parameters
• In-sewer air and upstream wastewater temperatures are key
parameters
• Heat recovery has shown a potential in long sewer lines
21/10/2013
Conclusions
22
• Model temperature drop along a Belgian sewer network using
deterministic model (energy balance)
• Investigate the impact of transient deterministic model on
modelling accuracy
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Future Work
• Test the model the model on more data
• Investigate further predictive techniques that incoperate
sewer length
23
• SEWAGE WATER: INTERESTING HEAT SOURCE FOR HEAT PUMPS
AND CHILLERS (Felix Schmid, Energy-engineer FH, SwissEnergy
Agency for Infrastructure Plants Gessnerallee 38a, CH-8001 Zürich,
Switzerland )
• Renewable energy potential for the water industry (Environment
Energy Report: SC070010/R5)
• Heating energy consumption and resulting environmental impact of
European apartment buildings (Constantinos A. Balaras*, Kalliopi
Droutsa, Elena Dascalaki, Simon Kontoyiannidis)
• Schilperoort, R. P. and Clemens, F. 2009 Fibre-optic distributed
temperature sensing in combined sewer systems. Water Science &
Technology, 60. (5), 1127-1134.
• Hoes, O. A., Schilperoort, R. P., Luxemburg, W., Clemens, F. and Van
de Giesen, N. 2009. Locating illicit connections in storm water
sewers using fiber-optic distributed temperature sensing. Water
Research, 43, 5187-5197.
21/10/2013
References
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21/10/2013
Thanks …
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[email protected]