Evaluation of predictive accuracy of a (micro)pollutant influent generator Laura Snip, X....
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Evaluation of predictive accuracy of a (micro)pollutant influent generator
Laura Snip, X. Flores-Alsina, I. Aymerich, B.G. Plósz, S. Rodríguez-Mozaz, D. Barceló, I. Rodriguez-Roda, Ll. Corominas, U. Jeppsson and K.V. Gernaey
[email protected] webinar 27th of February
27/02/2015Influent [email protected]
2 DTU Chemical Engineering, Technical University of Denmark
Outline
• Introduction• Materials and Methods:
– Catchment– Compounds studied– Influent generator– Quantitative evaluation methods
• Results:– Calibration WWTP Puigcerdà
• Conclusion
27/02/2015Influent [email protected]
3 DTU Chemical Engineering, Technical University of Denmark
Introduction
• WWTP modelling studies need good quality influent data (Rieger et al., IWA STR no.22)
– Sampling campaigns: high work load and costs• Influent generators receive increased interest
(Martin & Vanrolleghem, 2014 Environ. Modell. Softw. 60, 188-120)
• ‘Traditional’ variables– Flow rate, ammonium etc.
• Micropollutants– Dynamics in influent and effluent– Concentrations affect reaction rate
27/02/2015Influent [email protected]
4 DTU Chemical Engineering, Technical University of Denmark
Outline
• Introduction• Materials:
– Catchment– Compounds studied– Influent generator– Quantitative evaluation methods
• Results:– Calibration WWTP Puigcerdà
• Conclusion• Further work
27/02/2015Influent [email protected]
5 DTU Chemical Engineering, Technical University of Denmark
Catchment - Puigcerdà
• Wastewater from Spain and France• Widespread catchment (area of 100 km2)• No industry present• Population equivalent 16.000 PE
– Fluctuating due to touristic activities– Average flow rate of 4100-8300 m3/day– Organic load of 595-1785 kg BOD/day – Nitrogen load of 123-349 kg N/day
• 60% from Puigcerdà
27/02/2015Influent [email protected]
6 DTU Chemical Engineering, Technical University of Denmark
• Pharmaceuticals– Ibuprofen (IBU) and metabolite 2-Hydroxyibuprofen (IBU-2OH)
• Non-steroidal anti-inflammatory compound• 6 hours body residence time• Excretion in urine, 15% IBU, 9% IBU-2OH
– Sulfamethoxazole (SMX) and metabolite N-Acetyl Sulfamethazine-d4 (SMX-N4)
• Antibiotic• 10 hours body residence time• Excretion in urine, 14% SMX, 44% SMX-N4
– Carbamazepine (CMZ) and metabolite 2-Hydroxy Carbamazepine (CMZ-2OH)
• Mood stabilising drug• 8-72 hours body residence time• Excretion in urine 1% and faeces 28 % as CMZ,
4% urine CMZ-2OH
Compounds studied
27/02/2015Influent [email protected]
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Influent generator
HOUSEHOLDS (HH)
INDUSTRIES (IndS)
SEASONAL CORRECTION
FACTOR
RAINFALL
HOUSEHOLDS (HH)
INDUSTRIES (IndS)
SOIL MODEL
FIRST FLUSH EFFECT
MODELASM FRACTIONATION
TEMPERATURE
FLOW RATE MODEL BLOCK
POLLUTANTS MODEL BLOCK
TEMPERATURE MODEL BLOCK
TRANSPORT MODEL BLOCK
100-aH
aH
SEWER SYSTEM
MODEL
infiltration
FIRST FLUSH EFFECT
MODEL
Gernaey et al., 2011
Environ. Modell. Softw. 26(11)
27/02/2015Influent [email protected]
8 DTU Chemical Engineering, Technical University of Denmark
MP model - Influent generator
HOUSEHOLDS (HH)
INDUSTRIES (IndS)
SEASONAL CORRECTION
FACTOR
RAINFALL
HOUSEHOLDS (HH)
INDUSTRIES (IndS)
SOIL MODEL
ASM-X FRACTIONATION
TEMPERATURE
FLOW RATE MODEL BLOCK
POLLUTANTS MODEL BLOCK
TEMPERATURE MODEL BLOCK
TRANSPORT MODEL BLOCK
100-aH
aH
infiltration
PHARMACEUTICALS
Snip et al., 2014, Environ. Model. Softw., 62, 112-127
SEWER SYSTEM
MODEL
FIRST FLUSH EFFECT
MODEL
27/02/2015Influent [email protected]
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MP DAILY PROFILE
t (hours)
0 5 10 15 200,0
0,5
1,0
1,5
2,0
2,5
3,0
3,5
Phenomenological approach of occurrence MPs
MP_mg_d1000PE
MP load =
78 mg/(day*1000PE)
X PE
PE = 80.000 inhabitants
Average of the daily profile is 1
Snip et al., 2014, Environ. Model. Softw., 62, 112-127
27/02/2015Influent [email protected]
10 DTU Chemical Engineering, Technical University of Denmark
• Peak evaluation– Magnitude of peak (PDIFF & PEP)– Timing of peak (MSDE)
• Absolute criteria– Bias of prediction (ME)– No cancelling out of errors (MAE)– Emphasis on large errors (RMSE)
• Relative criteria– Bias of prediction (MPE)– No cancelling out of errors (MARE)– Emphasis on large errors (MSRE)
• Other criteria– Index of Agreement (IoAd)– Correlation data with simulation (Corr.)
Quantitative evaluation methods
Dawson et al., 2007, Environ. Model. Softw., 22, 1034-1052
Hauduc et al., 2011, Watermatex, San Sebastian, Spain
27/02/2015Influent [email protected]
11 DTU Chemical Engineering, Technical University of Denmark
Outline
• Introduction• Materials:
– Catchment– Compounds studied– Influent generator– Quantitative evaluation methods
• Results:– Calibration WWTP Puigcerdà
• Conclusion• Further work
27/02/2015Influent [email protected]
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• Flow rate– Dry and wet weather– Blocks HH, Rainfall, Soil and Sewer system
• Soluble pollutant, ammonium – Block HH
• Particulate pollutant, COD particulate – Block HH and First flush effect
• Temperature– Block Temperature
• Automatic calibration procedure with Bootstrap (optimization of error)
Results of influent generator – Traditional compounds
27/02/2015Influent [email protected]
13 DTU Chemical Engineering, Technical University of Denmark
HOUSEHOLDS (HH)
SEASONAL CORRECTION
FACTOR
RAINFALL
SOIL MODEL
FLOW RATE MODEL BLOCK
TRANSPORT MODEL BLOCK
100-aH
aH
infiltration
SEWER SYSTEM
MODEL
FIRST FLUSH EFFECT
MODEL
Results of influent generator – Traditional compounds
• Calibrated parameters:– HH: flow per PE = 110 m3/d; PE = 16,000– Soil: area connected to sewer pipes = 27,916 m2
– Sewer system: HRT = 3 h; area of sewer pipe per tank = 853.17 m2
– Rainfall: flow per mm rainfall = 823 m3/mm
27/02/2015Influent [email protected]
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• Calibrated parameters:– HH: flow per PE = 110 m3/d; PE = 16,000– Soil: area connected to sewer pipes = 27,916 m2
– Sewer system: HRT = 3 h; area of sewer pipe per tank = 853.17 m2
– Rainfall: flow per mm rainfall = 823 m3/mm
October5 10 15 20 25 30F
low
rat
e (
m3/d
ay)
0
5000
10000
15000
20000
Results of influent generator – Traditional compounds
27/02/2015Influent [email protected]
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Results of influent generator – Traditional compounds• Ammonium and COD particulates• Calibrated parameters:
– HH: • Ammonium per day per PE = 5.95 mgN/(day.PE)• COD particulate per day per PE = 55 mg/(day.PE)
– First flush effect:• Trigger flow rate = 10.000 m3/day• Maximum accumulated mass = 700 kg/SS• Fraction of settling particles = 0.40
October
5 10 15 20 25 30NH
4+ l
oad
(kg
/d)
0
100
200
300
400
500
October
5 10 15 20 25 30CO
D l
oa
d (
kg/d
)
0100020003000400050006000
27/02/2015Influent [email protected]
16 DTU Chemical Engineering, Technical University of Denmark
Results of influent generator – Pharmaceuticals
• Correlation ‘traditional’ pollutants– IBU and IBU-2OH with ammonium 0.78 & 0.77 – SMX and SMX-N4 with ammonium 0.63 & 0.58– CMZ with TSS 0.82 and CMZ-2OH with ammonium 0.63
• Calibrated parameters:– IBU and IBU-2OH = 20.79 and 5.45 g/(day.PE)– SMX and SMX-N4 = 0.1227 and 0.08 g/(day.PE) – CMZ and CMZ-2OH = 8.86*10-2 and 0.1538 g/(day.PE)
MP DAILY PROFILE
t (hours)
0 5 10 15 200,0
0,5
1,0
1,5
2,0
2,5
3,0
3,5
27/02/2015Influent [email protected]
17 DTU Chemical Engineering, Technical University of Denmark
Date
11/10/2012 12/10/2012 13/10/2012
Po
lluta
nt
load
(g
/d)
0
200
400
600
800
1000
Results of influent generator – Pharmaceuticals - Ibuprofen
IBU
Date
11/10/2012 12/10/2012 13/10/2012 P
ollu
tan
t lo
ad (
g/d
)0
200
400
600
800
1000
IBU-2OH
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Results of influent generator – Pharmaceuticals - Sulfamethoxazole
• Considerably lower load than IBU and IBU-2OH
• Pattern less distinctive, decrease of HRT needed
• Missing of toilet flush/bad mixing
Date
11/10/2012 12/10/2012 13/10/2012
Po
lluta
nt
load
(g
/d)
0
2
4
6
8
10
Date
11/10/2012 12/10/2012 13/10/2012 P
ollu
tan
t lo
ad (
g/d
)
0
2
4
6
8
10
SMX-N4SMX
27/02/2015Influent [email protected]
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Results of influent generator – Pharmaceuticals - Carbamazepine
CMZ CMZ-2OH
• CMZ lower load than CMZ-2OH
• Different pattern due to different excretion paths
Date
11/10/2012 12/10/2012 13/10/2012
Po
lluta
nt
load
(g
/d)
0
2
4
6
8
10
Date
11/10/2012 12/10/2012 13/10/2012 P
ollu
tan
t lo
ad (
g/d
)
0
2
4
6
8
10
27/02/2015Influent [email protected]
20 DTU Chemical Engineering, Technical University of Denmark
Results of influent generator – Quantitative evaluation
Quantitative method
Peak evaluation
Compound evaluated
PDIFF PEP MSDE
Flow rate 623.34 3.42 1.46*106
Ibuprofen -0.0045 -11.75 0.0013
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Results of influent generator – Quantitative evaluation
Quantitative method
Absolute criteria Relative criteria
Compound evaluated
ME MAE RMSE MPE MARE MSRE
Flow rate 181.5 1.1*103 1.7*103 0.12 0.13 0.03Ibuprofen
0.0078 0.0245 0.031 4.93 0.42 0.39
27/02/2015Influent [email protected]
22 DTU Chemical Engineering, Technical University of Denmark
Results of influent generator – Quantitative evaluation
Quantitative method
Other criteria
Compound evaluatedIoAd Corr.
Flow rate 0.82 0.70
Ibuprofen 0.71 0.49
27/02/2015Influent [email protected]
23 DTU Chemical Engineering, Technical University of Denmark
• Influent generator is capable of generating the dynamic profile of both ‘traditional’ variables and pharmaceuticals
• According to the excretion patterns of the micropollutants, different user defined profiles should be used
• Quantitative evaluation methods can help with identifying points of concern in the calibration
Conclusions
27/02/2015Influent [email protected]
24 DTU Chemical Engineering, Technical University of Denmark
The research leading to these results has received funding from the People Program (Marie Curie Actions) of the European Union’s Seventh Framework Programme FP7/2007-2013 under REA agreement 289193.This presentation reflects only the author’s views and the European Union is not liable for any use that may be made of the information contained therein.
Thank you for your attention!
Questions?
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