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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.

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Presentation on theme: "Evaluation of predictive accuracy of a (micro)pollutant influent generator Laura Snip, X. Flores-Alsina, I. Aymerich, B.G. Plósz, S. Rodríguez-Mozaz, D."— Presentation transcript:

1 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 Sanitas webinar 27 th of February

2 27/02/2015Influent generator 2DTU Chemical Engineering, Technical University of Denmark Outline Introduction Materials and Methods: –Catchment –Compounds studied –Influent generator –Quantitative evaluation methods Results: –Calibration WWTP Puigcerdà Conclusion

3 27/02/2015Influent generator 3DTU 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, ) ‘Traditional’ variables –Flow rate, ammonium etc. Micropollutants –Dynamics in influent and effluent –Concentrations affect reaction rate

4 27/02/2015Influent generator 4DTU Chemical Engineering, Technical University of Denmark Outline Introduction Materials: –Catchment –Compounds studied –Influent generator –Quantitative evaluation methods Results: –Calibration WWTP Puigcerdà Conclusion Further work

5 27/02/2015Influent generator 5DTU Chemical Engineering, Technical University of Denmark Catchment - Puigcerdà Wastewater from Spain and France Widespread catchment (area of 100 km 2 ) No industry present Population equivalent PE –Fluctuating due to touristic activities –Average flow rate of m 3 /day –Organic load of kg BOD/day –Nitrogen load of kg N/day 60% from Puigcerdà

6 27/02/2015Influent generator 6DTU 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

7 27/02/2015Influent generator 7DTU Chemical Engineering, Technical University of Denmark Influent generator HOUSEHOLDS (HH) INDUSTRIES (IndS) SEASONAL CORRECTION FACTOR RAINFALL HOUSEHOLDS (HH) INDUSTRIES (IndS) SOIL MODEL FIRST FLUSH EFFECT MODEL ASM 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)

8 27/02/2015Influent generator 8DTU 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, SEWER SYSTEM MODEL FIRST FLUSH EFFECT MODEL

9 27/02/2015Influent generator 9DTU Chemical Engineering, Technical University of Denmark MP DAILY PROFILE Phenomenological approach of occurrence MPs MP_mg_d1000PE MP load = 78 mg/(day*1000PE) X PE PE = inhabitants Average of the daily profile is 1 Snip et al., 2014, Environ. Model. Softw., 62,

10 27/02/2015Influent generator 10DTU 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, Hauduc et al., 2011, Watermatex, San Sebastian, Spain

11 27/02/2015Influent generator 11DTU Chemical Engineering, Technical University of Denmark Outline Introduction Materials: –Catchment –Compounds studied –Influent generator –Quantitative evaluation methods Results: –Calibration WWTP Puigcerdà Conclusion Further work

12 27/02/2015Influent generator 12DTU Chemical Engineering, Technical University of Denmark 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

13 27/02/2015Influent generator 13DTU 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 m 3 /d; PE = 16,000 –Soil: area connected to sewer pipes = 27,916 m 2 –Sewer system: HRT = 3 h; area of sewer pipe per tank = m 2 –Rainfall: flow per mm rainfall = 823 m 3 /mm

14 27/02/2015Influent generator 14DTU Chemical Engineering, Technical University of Denmark Calibrated parameters: –HH: flow per PE = 110 m 3 /d; PE = 16,000 –Soil: area connected to sewer pipes = 27,916 m 2 –Sewer system: HRT = 3 h; area of sewer pipe per tank = m 2 –Rainfall: flow per mm rainfall = 823 m 3 /mm Results of influent generator – Traditional compounds

15 27/02/2015Influent generator 15DTU Chemical Engineering, Technical University of Denmark 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 = m 3 /day Maximum accumulated mass = 700 kg/SS Fraction of settling particles = 0.40

16 27/02/2015Influent generator 16DTU 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 = and 5.45 g/(day.PE) –SMX and SMX-N4 = and 0.08 g/(day.PE) –CMZ and CMZ-2OH = 8.86*10 -2 and g/(day.PE) MP DAILY PROFILE

17 27/02/2015Influent generator 17DTU Chemical Engineering, Technical University of Denmark Results of influent generator – Pharmaceuticals - Ibuprofen IBUIBU-2OH

18 27/02/2015Influent generator 18DTU Chemical Engineering, Technical University of Denmark 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 SMX-N4SMX

19 27/02/2015Influent generator 19DTU Chemical Engineering, Technical University of Denmark Results of influent generator – Pharmaceuticals - Carbamazepine CMZCMZ-2OH CMZ lower load than CMZ-2OH Different pattern due to different excretion paths

20 27/02/2015Influent generator 20DTU Chemical Engineering, Technical University of Denmark Results of influent generator – Quantitative evaluation Quantitative method Peak evaluation Compound evaluated PDIFFPEPMSDE Flow rate *10 6 Ibuprofen

21 27/02/2015Influent generator 21DTU Chemical Engineering, Technical University of Denmark Results of influent generator – Quantitative evaluation Quantitative method Absolute criteriaRelative criteria Compound evaluated MEMAERMSEMPEMAREMSRE Flow rate * * Ibuprofen

22 27/02/2015Influent generator 22DTU Chemical Engineering, Technical University of Denmark Results of influent generator – Quantitative evaluation Quantitative method Other criteria Compound evaluated IoAdCorr. Flow rate Ibuprofen

23 27/02/2015Influent generator 23DTU 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

24 27/02/2015Influent generator 24DTU 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/ under REA agreement 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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