Viability of Heat Recovery from Combined Sewers

Mohamad Abdel-Aal PhD Researcher at the University of Bradford M.Abdel-Aal@Bradford.ac.uk 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

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Structure

• What?

• Why?

• How? • Data

• Model

• Results

• Conclusions

• Future work

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What

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Heat loss

Distance = ?

Large temperature drop = Problems in WwTP

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Why

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• Over 500 wastewater heat pumps are in operation world-wide. Thermal ratings range from 10 kW to 20 MW.

• Sewage temperatures vary between 10 and 250C, in Europe, all year around

• In Switzerland; 6000 GWh of thermal energy are lost annually through the sewage system

UK Water Industry Energy

Breakdown

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5

How

Data

Analysis

Flow Rate

In-Sewer Temperatures Soil Temperatures

Model

Comparison to literature

Deterministic (Energy balance)

Daily variation pattern

Estimate distance required to compensate heat losses upstream

Predictive (Group method of data handling)

Pipe dimensions

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Data- Aquafin

• 6 Sites in Antwerp, Belgium

• Wastewater and in-sewer air temperatures, flow and soil measured every 20 minutes for 6 - 12 months

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Site

Average DWF Pipe length Pipe diameter

(m3/hour) (m) (m)

1 37 464 1.2

2 49 170 1.3

3 48 232 1.2

4 1000 1031 1.2

5 1100 1775 1.3

6 340 749 0.7

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Data- Analysis (July)

7 19.5

20.0

20.5

21.0

21.5

22.0

00:01 01:41 03:21 05:01 06:41 08:21 10:01 11:41 13:21 15:01 16:41 18:21 20:01 21:41 23:21

Tem

per

atu

re o

C

Time

Site 1 Upstream Site 2 Upstream Site 3 Upstream

Site 1 Downstream Site 2 Downstream Site 3 Downstream

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Data- Site 2

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Average WW temperature drop = 0.70 C or 40 C /km

1.50 C difference 21

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Data- Site 2

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Deterministic Model

10 𝒎𝒄𝒑 𝑻𝒋 − 𝑻𝒋+𝒏 = 𝒒𝒘𝒂 + 𝒒𝒘𝒔 + 𝒒𝒓𝒆𝒄𝒐𝒗𝒆𝒓𝒆𝒅

q= thermal energy w= wastewater, s= soil m=mass flow rate cp= thermal heat capacity

Tj Tj+n

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Deterministic Model

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𝒎𝒄𝒑 𝑻𝒋 − 𝑻𝒋+𝒏 = 𝒒𝒘𝒂 + 𝒒𝒘𝒔 + 𝒒𝒓𝒆𝒄𝒐𝒗𝒆𝒓𝒆𝒅

𝑞𝑤𝑎= 1

𝑅𝑤𝑎𝑇𝑤𝑎𝑡𝑒𝑟 − 𝑇𝑎𝑖𝑟 𝑞𝑤𝑠 =

1

𝑅𝑤𝑠𝑇𝑤𝑎𝑡𝑒𝑟 − 𝑇𝑠𝑜𝑖𝑙

Parameter Abbreviation Value in Feb Units

Thermal Resistivity between Wastewater and Air 𝑅𝑤𝑎 0.04 m2/0C

Thermal Resistivity between Wastewater and Soil 𝑅𝑤𝑠 0.5 m2/0C

Wastewater Temperature 𝑇𝑤𝑎𝑡𝑒𝑟 11.6 0C

In-Sewer Air Temperature 𝑇𝑎𝑖𝑟 9 0C

Soil Temperature 𝑇𝑠𝑜𝑖𝑙 9.5 0C

Mas flow rate m 14 Kg/s

Specific Heat Capacity for water cp 4.2 kJ/kg.0C

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Deterministic Model- Results

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y = 0.9248x + 1.45 R² = 0.998

y = 0.8619x + 2.77 R² = 0.992

y = 0.9555x + 0.93 R² = 0.989

y = 1.0478x - 0.22 R² = 0.998

y = 1.2014x - 1.94 R² = 0.995

y = 1.2734x - 2.57 R² = 0.997

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11

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15

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9 11 13 15 17 19 21

Mo

del

led d

ow

nst

ream

tem

per

atu

re 0

C

Measured downstream temperature 0 C

Site 1 Site 2 Site 3 Site 4 Site 5 Site 6

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Deterministic Model- Sensitivity Analysis

13 -80%

-30%

20%

70%

120%

170%

220%

270%

25% 50% 100% 200% 400%

Ch

ang

e in

do

wn

stre

am t

emp

erat

ure

Percentage of upstream temperature (100% = default value)

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Deterministic Model- Sensitivity Analysis

14 -0.8%

-0.4%

0.1%

0.5%

0.9%

1.3%

1.7%

2.1%

2.5%

25% 50% 100% 200% 400%

Ch

ang

e in

do

wn

stre

am t

emp

erat

ure

Percentage of default value (100% = default value)

In-sewer air temperature Ta

Pipe thermal conductivity, kp

Soil thermal conductivity , ks

Flow surface width, b

Soil depth, ds

Soil temperature, Ts

Wastewater flow rate, Q

Wastewater velocity, uw

Wetted perimeter, wet.p

Water soil thermal resistivity, Rws

Water air thermal resistivity, Rwa

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Predictive Model

• 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)

15 Error = 9.4%

Error = 3%

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Predictive Model- Results (DWF )

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y = 0.9962x + 0.0836

R² = 0.994

y = 0.9956x - 0.4502

R² = 0.996

8.8

10.8

12.8

14.8

16.8

18.8

20.8

8.8 10.8 12.8 14.8 16.8 18.8 20.8

Mo

del

led d

ow

nst

ream

tem

per

atu

re

Measured downstream temperatures

Deterministic model Predective

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Deterministic Model- Application

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0

2

4

6

8

10

12

14

0 1 2 3 4 5 6 7 8 9

Do

wn

stre

am T

emp

erat

ure

, 0C

Sewer Length, km

Business As Usual, Q= 37 m3/h Soil In-Sewer Air

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Deterministic Model- Application

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0

2

4

6

8

10

12

14

0 1 2 3 4 5 6 7 8 9

Do

wn

stre

am T

emp

erat

ure

, 0C

Sewer Length, km

Business As Usual, Q= 37 m3/h 20 kW Heat Recovered, Q= 37 m3/h Soil In-Sewer Air

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Deterministic Model- Application

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0

2

4

6

8

10

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0 1 2 3 4 5 6 7 8 9

Do

wn

stre

am T

emp

erat

ure

, 0C

Sewer Length, km

Business As Usual, Q= 37 m3/h 20 kW Heat Recovered, Q= 37 m3/h250 kW Heat Recovered, Q= 37 m3/h SoilIn-Sewer Air

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Deterministic Model- Application

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0

2

4

6

8

10

12

14

0 1 2 3 4 5 6 7 8 9

Do

wn

stre

am T

emp

erat

ure

, 0C

Sewer Length, km

Business As Usual, Q= 37 m3/h 20 kW Heat Recovered, Q= 37 m3/h250 kW Heat Recovered, Q= 37 m3/h 500 kW Heat Recovered, Q= 37 m3/hSoil In-Sewer Air

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Model- Application

• Heat exchanger operating all year around

• 170kWh/m2 annual heat demand

• 100% heat exchanger efficiency

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Heat Exchanger Capacity

kW

Energy Recovered

MWh/yr

m2 heated

1,000 9 50

250,000 2,190 13,000

500,000 4,380 26,000

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Conclusions

• 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

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Future Work

• Model temperature drop along a Belgian sewer network using deterministic model (energy balance)

• Investigate the impact of transient deterministic model on modelling accuracy

• Test the model the model on more data

• Investigate further predictive techniques that incoperate sewer length

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References

• 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.

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M.Abdel-Aal@Bradford.ac.uk 25

Thanks …

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