Aymerich, I.

Corominas, L. I.

Flores-Alsina, X.

Gernaey, K. V.

Jeppsson, U.

Plósz, B. G.

Snip, L. J. P.

Summary of key findings: The Benchmark Simulation Model No. 2 influent generator upgraded with pharmaceutical occurrences is capable of reproducing both the long- and short-term dynamics of traditional variables as well as micropollutants. Several quantitative evaluation criteria are presented and used to assess the model’s predictive capabilities and all show satisfactory results except for COD particulates. Ongoing research aims at improving this remaining issue

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Downloaded from orbit.dtu.dk on: Nov 08, 2017Calibration and evaluation of predictive accuracy of a (micro)pollutant influentgeneratorSnip, Laura; Flores Alsina, Xavier; Aymerich, I.; Corominas, L. I.; Plósz, Benedek G.; Jeppsson, U.;Gernaey, Krist V.Publication date:2015Document VersionPeer reviewed versionLink back to DTU OrbitCitation (APA):Snip, L. J. P., Flores-Alsina, X., Aymerich, I., Corominas, L. I., Plósz, B. G., Jeppsson, U., & Gernaey, K. V.(2015). Calibration and evaluation of predictive accuracy of a (micro)pollutant influent generator. Abstract from9th IWA Symposium on Systems Analysis and Integrated Assessment (Watermatex 2015), Gold Coast,Queensland, Australia.9th IWA Symposium on Systems Analysis and Integrated Assessment 11‐14 June 2015, Gold Coast, Australia   Calibration and evaluation of predictive accuracy of a (micro)pollutant influent generator  Snip, L.J.P.*, Flores-Alsina, X.*, Aymerich, I.**, Corominas, Ll.**, Plósz, B.Gy.***, Jeppsson, U.**** and Gernaey, K.V.*  *CAPEC-PROCESS, Department of Chemical and Biochemical Engineering, Technical University of Denmark (DTU), Building 229, DK-2800 Kgs. Lyngby, Denmark. **ICRA, ICRA, Catalan Institute for Water Research, Scientific and Technological Park of the University of Girona, Emili Grahit, 101, E-17003 Girona, Spain. ***Urban Water Engineering (UWE) Section, Department of Environmental Engineering, Technical University of Denmark, Building 115, DK-2800 Kgs. Lyngby, Denmark. ****Division of Industrial Electrical Engineering and Automation (IEA), Department of Biomedical Engineering (BME), Lund University, Box 118, SE-221 00 Lund, Sweden  Keywords: Influent modelling; Quantitative evaluation; Xenobiotic trace chemicals  Summary of key findings The Benchmark Simulation Model No. 2 influent generator upgraded with pharmaceutical occurrences is capable of reproducing both the long- and short-term dynamics of traditional variables as well as micropollutants. Several quantitative evaluation criteria are presented and used to assess the model’s predictive capabilities and all show satisfactory results except for COD particulates. Ongoing research aims at improving this remaining issue. Background and relevance When performing wastewater treatment plant (WWTP) engineering studies, realistic data representing the influent dynamics are crucial (Rieger et al., 2012). This also applies when modelling micropollutants. However, due to the required time and costs of performing measuring campaigns, many simulation studies lack time series of adequate duration and quality. Therefore, influent generators are alternatives that have gained interest (Martin and Vanrolleghem, 2014). In this study, the BSM2 influent generator (Gernaey et al., 2011) upgraded with pharmaceuticals (Snip et al., 2014) is used to produce dynamic influent characteristics of both ‘traditional’ variables and pharmaceuticals. The calibration is performed manually with high frequency data of a WWTP loaded for 16.000 population equivalent (PE) (Figure A1). Different quantitative evaluation methods are compared. Results Qualitative evaluation of calibration  A stepwise manual calibration procedure (Flores-Alsina et al., 2014) is used to adjust the most sensitive parameters (Table A1). By evaluating the calibration visually, it can be assumed that the model is capable of capturing dry weather dynamics (Fig 1). The morning peak, evening peak and late night and mid-day minima are well described for flow rate (Fig 1a), COD (Fig 1b) and NH4+ (Fig 1c). Also for temperature, daily variations are well described (Fig 1d). However, during wet weather there was rain data missing for additional peaks shown in the data of the flow rate (black line). In order to capture that rainfall, synthetic rainfall data was created (Fig 1a; grey line). Regarding the first flush effect, the model was able of capturing most. However, not all increases in particulates seem to have followed an increase in the flow rate and therefore were not captured by the model.  The calibration results of the pharmaceuticals can be seen in Fig 2 and show that the generator can describe the daily variation. Even though ibuprofen (IBU) has an irregular administration pattern, a strong correlation with NH4+ was found, which indicates excretion via urine. The same correlation was found for sulfamethoxazole (SMX) and N-Acetyl Sulfamethazine-d4 (SMX-N4), however the length of the sewer had to be modified in order to describe the peaks. There are two possible explanations. Firstly, one must notice that SMX is considerably lower in load than IBU and therefore more sensitive to the error of sampling (Ort and Gujer, 2006). Secondly, we assume that the detected compound is consumed and excreted in an area close to the sampling point. Hence, the shorter residence time results in peak concentrations above the detection limits. Carbamazepine (CMZ) is correlated with 9th IWA Symposium on Systems Analysis and Integrated Assessment 11‐14 June 2015, Gold Coast, Australia  total suspended solids (TSS), due to the fact that CMZ is excreted only 1% in the urine and 28% in the faeces (Zhang et al., 2008). However, the metabolite 2-Hydroxy Carbamazepine (CMZ-2OH) is correlated with NH4+ and therefore two different profiles were used. The close link between CMZ and TSS dynamics could be associated with desorption in the sewer or filtering. Numerous studies report low sorption rates for CMZ in wastewater (e.g., Ternes et al., 2004). October5 10 15 20 25 30Flow rate (m3 /day)05000100001500020000 a b  October5 10 15 20 25 30COD part load (kg/d)02000400060008000c d Figure 1. Comparison of the simulation results (black line) of flow rate (a), temperature (b), ammonia (c) and COD (d) with the measurements (grey dots). Synthetic rainfall data were created for the additional simulation of the flow rate (grey line).  Date11/10/2012  12/10/2012  13/10/2012 Pollutant load (g/d)020406080100120140Date11/10/2012  12/10/2012  13/10/2012 Pollutant load (g/d)0246810Date11/10/2012  12/10/2012  13/10/2012 Pollutant load (g/d)0246810a b c Figure 2. Simulations of influent IBU (black line) and metabolite IBU-2OH (line and dot) (a), SMX (black line) and metabolite SMX-N4 (line and dot) (b), and CMZ (black line) and metabolite CMZ-2OH (line and dot) (c) and measurements with their standard deviation of the chemical analysis (dark grey dots. Quantitative evaluation of calibration  The results of the quantitative evaluation can be found in Table 1, where the best and worst performances are presented in italics (calculations of criteria can be found in Table A2). As PDIFF and MSDE are absolute criteria, the highest value for flow rate is expected. However, when comparing the relative criterion PEP, the magnitude of the simulated SMX-N4 peaks differs the most compared with the observed peaks. As mentioned before, the sewer length was reduced for SMX and SMX-N4 profiles and for SMX-N4 the high peaks were still not captured. The best performance can be found for CMZ, which is also the compound with the most constant occurrence. Regarding the absolute criteria, there seems to be little bias in the calibrations with respect to ME. However, it could be that negative and positive errors are cancelled out. Nevertheless, also MSE and RMSE show little bias.  About the relative criterion, the calibration of the temperature is the best, while the COD has the worst performance. One peculiarity (Table 1, bold) is the positive value for ME for NH4+, COD and SMX-October5 10 15 20 25 30050100150200250NH4+ load (kg/d)October5 10 15 20 25 30Temperature (oC)810121416189th IWA Symposium on Systems Analysis and Integrated Assessment 11‐14 June 2015, Gold Coast, Australia  N4 while the MPE is negative. The positive ME is due to large positive errors at peak values, while overall negative errors are found (negative MPE).  Table 1. Quantitative evaluations of the BSM2 influent generator with high frequency data.  Quantitative method Peak evaluation Absolute criteria Relative criterion   Compound evaluated PDIFF PEP MSDE ME MAE RMSE MPE IoAd Corr NH4+ 1.27 0.60 3.40 1.83 4.40 5.59 -2.31 0.60 0.71 CODpart 103.16 15.76 9.15·103 -21.64 59.20 101.60 107.86 -0.15 0.33 Flow rate 623.34 3.42 1.46·106 181.5 1.07·103 1.66·103 0.12 0.82 0.70 Temperature 0.16 0.94 0.37 -0.0084 0.4707 0.60 -0.15 0.78 0.16 IBU -7.76·10-4 -9.02 4.06·10-5 1.5·10-3 4.2·10-3 5.2·10-3 7.41 0.71 0.50 IBU-2OH -6.34·10-4 -11.85 1.32·10-5 7.48·10-4 2.1·10-3 2.7·10-3 9.47 0.77 0.62 SMX -2.27·10-5 -6.43 1.38·10-7 -5.27·10-5 1.57·10-4 2.24·10-4 -71.58 0.84 0.72 SMX-N4 2.23·10-4 -338.68 1.06·10-7 -1.03·10-4 1.89·10-4 2.91·10-4 -78.16 1.00 0.16 CMZ -3.87·10-4 -6.91 1.31·10-8 -1.46·10-5 6.39·10-5 9.06·10-5 0.63 0.69 0.44 CMZ-2OH -3.50·10-5 -8.38 1.68·10-7 -7.04·10-5 2.17·10-4 2.84·10-4 -3.17 0.51 0.26 The IoAd shows a fair fit only for flow rate, SMX and SMX-N4. Temperature performed the best for all relative criteria, however not for IoAd. The highest correlation between data and simulation is found in SMX-N4. It indicates that the trend of data was followed well, even though the magnitudes were not captured. The COD particulates calibration scored low on many criteria and therefore other parameter values are chosen to compare the fit (Table A3). The relative criteria seem to worsen with the new parameters while the peak evaluation is improved. Therefore, there seems to be a trade-off between the relative error and the peak performance.  References Bennett, N.D., Croke, B.F.W., Guariso, G., Guillaume, J.H.A., Hamilton, S.H., Jakeman, A.J., Marsili-Libelli, S., Newham, L.T.H., Norton, J.P., Perrin, C., Pierce, S.A., Robson, B., Seppelt, R., Voinov, A.A., Fath, B.D., Andreassian, V., 2013. Characterising performance of environmental models. Environ. Model. Softw. 40, 1–20.  Castiglioni, S., Bijlsma, L., Covaci, A., Emke, E., Hernández, F., Reid, M., Ort, C., Thomas, K.V., Van Nuijs, A.L.N., De Voogt, P., Zuccato, E., 2013. Evaluation of uncertainties associated with the determination of community drug use through the measurement of sewage drug biomarkers. Environ. Sci. Technol. 47, 1452–1460. Dawson, C.W., Abrahart, R.J., See, L.M., 2007. HydroTest: A web-based toolbox of evaluation metrics for the standardised assessment of hydrological forecasts. Environ. Model. Softw. 22, 1034–1052.  Flores-Alsina, X., Saagi, R., Lindblom, E., Thirsing, C., Thornberg, D., Gernaey, K.V., Jeppsson, U., 2014. Calibration and validation of a phenomenological influent pollutant disturbance scenario generator using full-scale data. Water Res. 51, 172–185.  Gernaey, K.V., Flores-Alsina, X., Rosen, C., Benedetti, L., Jeppsson, U., 2011. Dynamic influent pollutant disturbance scenario generation using a phenomenological modelling approach. Environ. Model. Softw. 26, 1255–1267.  Hauduc, H., Neumann, M., Muschalla, D., 2011. Towards quantitative quality criteria to evaluate simulation results in wastewater treatment–A critical review. In: 8th IWA Symposium on Systems Analysis and Integrated Assessment (Watermatex), San Sebastian, Spain, 19-22 June. Martin, C., Vanrolleghem, P.A., 2014. Analysing, completing, and generating influent data for WWTP modelling: A critical review. Environ. Model. Softw. 60, 188–201.  Ort, C., Gujer, W., 2006. Sampling for representative micropollutant loads in sewer systems. Water Sci. Technol. 54(6-7), 169–176.  Rieger, L., Gillot, S., Langergraber, G., Ohtsuki, T., Shaw, A., Takács, I., Winkler, S., 2012. Guidelines for Using Activated Sludge Models, Scientific and Technical Report No. 22, IWA Publishing, London, UK. Snip, L.J.P., Flores-Alsina, X., Plósz, B.G., Jeppsson, U., Gernaey, K.V., 2014. Modelling the occurrence, transport and fate of pharmaceuticals in wastewater systems. Environ. Model. Softw. 62, 112–127.  Ternes, T.A., Herrmann, N., Bonerz, M., Knacker, T., Siegrist, H., Joss, A., 2004. A rapid method to measure the solid-water distribution coefficient (Kd) for pharmaceuticals and musk fragrances in sewage sludge. Water Res. 38(19), 4075–4084.  Zhang, Y., Geissen, S.-U., Gal, C., 2008. Carbamazepine and diclofenac: removal in wastewater treatment plants and occurrence in water bodies. Chemosphere 73, 1151–1161.  9th IWA Symposium on Systems Analysis and Integrated Assessment 11‐14 June 2015, Gold Coast, Australia   Presenting Author Dr. Xavier Flores-Alsina Technical University of Denmark Is the presenting author an IWA Young Water Professional? N (i.e. an IWA member under 35 years of age) Bio: Xavier Flores-Alsina joined the CAPEC PROCESS center at DTU as a Marie Curie Fellow in May 2013. He did his PhD between Spain, UK and Denmark. He worked as post doc in Canada, Sweden, Australia and South Africa. His field of expertise is modelling, control and benchmarking wastewater treatment systems.  

Calibration and evaluation of predictive accuracy of a (micro)pollutant influent generator

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Calibration and evaluation of predictive accuracy of a (micro)pollutant influent generator

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