Empirical Sewer Water Quality Model for Generating Influent Data for WWTP Modelling
Abstract
:1. Introduction
2. Materials and Methods
2.1. System Description: The Dommel River IUWS
2.2. Monitoring Network and Data Validation
2.3. Data Analysis
2.4. Model Development
2.5. Model Calibration
3. Results and Discussion
3.1. Calibration Results
3.2. Model Results
3.3. Transferability of the Concept
3.4. Applications: Influent Generation, Surveillance of Monitoring Equipment and Gap Filling
4. Conclusions and Outlook
Acknowledgments
Author Contributions
Conflicts of Interest
References
- Benedetti, L.; De Baets, B.; Nopens, I.; Vanrolleghem, P.A. Multi-criteria analysis of wastewater treatment plant design and control scenarios under uncertainty. Environ. Model. Softw. 2010, 25, 616–621. [Google Scholar] [CrossRef]
- Rieger, L.; Gillot, S.; Langergraber, G.; Ohtsuki, T.; Shaw, A.; Takács, I.; Winkler, S. Guidelines for Using Activated Sludge Models. In IWA Scientific and Technical Report No. 22; IWA Publishing: London, UK, 2012. [Google Scholar]
- Rodriguez-Roda, I.; Sànchez-Marrè, M.; Comas, J.; Baeza, J.; Colprim, J.; Lafuente, J.; Cortes, U.; Poch, M. A hybrid supervisory system to support WWTP operation: Implementation and validation. Water Sci. Technol. 2002, 45, 289–297. [Google Scholar] [PubMed]
- Flores-Alsina, X.; Rodríguez-Roda, I.; Sin, G.; Gernaey, K. Multi-criteria evaluation of wastewater treatment plant control strategies under uncertainty. Water Res. 2008, 42, 4485–4497. [Google Scholar] [CrossRef] [PubMed]
- Nopens, I.; Benedetti, L.; Jeppsson, U.; Pons, M.N.; Alex, J.; Copp, J.B.; Gernaey, K.V.; Rosen, C.; Steyer, J.P.; Vanrolleghem, P.A. Benchmark simulation model No 2: Finalisation of plant layout and default control strategy. Water Sci. Technol. 2010, 62, 1967–1974. [Google Scholar] [CrossRef] [PubMed]
- Maruejouls, T.; Lessard, P.; Wipliez, B.; Pelletier, G.; Vanrolleghem, P.A. A phenomenological retention tank model using settling velocity distributions. Water Res. 2012, 46, 6857–6867. [Google Scholar] [CrossRef] [PubMed]
- Martin, C.; Vanrolleghem, P.A. Analysing, completing, and generating influent data for WWTP modelling: A critical review. Environ. Modell. Softw. 2014, 60, 188–201. [Google Scholar] [CrossRef]
- Gernaey, K.V.; Flores-Alsina, X.; Rosen, C.; Benedetti, L.; Jeppsson, U. Dynamic influent pollutant disturbance scenario generation using a phenomenological modelling approach. Environ. Model. Softw. 2011, 26, 1255–1267. [Google Scholar] [CrossRef]
- Flores-Alsina, X.; Saagi, R.; Lindblom, E.; Thirsing, C.; Thornberg, D.; Gernaey, K.V.; Jeppsson, U. Calibration and validation of a phenomenological influent pollutant disturbance scenario generator using full-scale data. Water Res. 2014, 51, 172–185. [Google Scholar] [CrossRef] [PubMed]
- De Keyser, W.; Gevaert, V.; Verdonck, F.; De Baets, B.; Benedetti, L. An emission time series generator for pollutant release modelling in urban areas. Environ. Model. Softw. 2010, 25, 554–561. [Google Scholar] [CrossRef]
- Devisscher, M.; Ciacci, G.; Fé, L.; Benedetti, L.; Bixio, D.; Thoeye, C.; De Gueldre, G.; Marsili-Libelli, S.; Vanrolleghem, P.A. Estimating costs and benefits of advanced control for wastewater treatment plants—The MAgIC methodology. Water Sci. Technol. 2006, 53, 215–223. [Google Scholar] [CrossRef] [PubMed]
- Talebizadeh, M.; Belia, E.; Vanrolleghem, P.A. Influent generator for probabilistic modeling of nutrient removal wastewater treatment plants. Environ. Model. Softw. 2016, 77, 32–49. [Google Scholar] [CrossRef]
- Bertrand-Krajewski, J.-L. Stormwater pollutant loads modelling: Epistemological aspects and case studies on the influence of field data sets on calibration and verification. Water Sci. Technol. 2007, 55, 1–17. [Google Scholar] [CrossRef] [PubMed]
- Bertrand-Krajewski, J.-L.; Briat, P.; Scrivener, O. Sewer sediment production and transport modelling: A literature review. J. Hydraul. Res. 1993, 31, 435–460. [Google Scholar] [CrossRef]
- Bertrand-Krajewski, J.-L.; Chebbo, G.; Saget, A. Distribution of pollutant mass vs volume in stormwater discharges and the first flush phenomenon. Water Res. 1998, 32, 2341–2356. [Google Scholar] [CrossRef]
- Ashley, R.M.; Bertrand-Krajewski, J.L.; Hvitved-Jacobsen, T.; Verbanck, M. Solids in-Sewers; IWA Publishing: London, UK, 2004. [Google Scholar]
- Ashley, R.M.; Hvitved-Jacobsen, T.; Bertrand-Krajewski, J.-L. Quo vadis sewer process modelling? Water Sci. Technol. 1999, 39, 9–22. [Google Scholar] [CrossRef]
- Willems, P. Random number generator or sewer water quality model? Water Sci. Technol. 2006, 54, 387–394. [Google Scholar] [CrossRef] [PubMed]
- Willems, P. Parsimonious model for combined sewer overflow pollution. J. Environ. Eng. 2010, 136, 316–325. [Google Scholar] [CrossRef]
- Dembélé, A.; Bertrand-Krajewski, J.-L.; Becouze, C.; Barillon, B. A new empirical model for stormwater TSS event mean concentrations (EMCs). Water Sci. Technol. 2011, 64, 1926–1934. [Google Scholar] [CrossRef] [PubMed]
- Rousseau, D.; Verdonck, F.; Moerman, O.; Carrette, R.; Thoeye, C.; Meirlaen, J.; Vanrolleghem, P.A. Development of a risk assessment based technique for design/retrofitting of WWTPs. Water Sci. Technol. 2001, 43, 287–294. [Google Scholar] [PubMed]
- Gruber, G.; Winkler, S.; Pressl, A. Quantification of pollution loads from CSOs into surface water bodies by means of online techniques. Water Sci. Technol. 2004, 50, 73–80. [Google Scholar] [PubMed]
- Schilperoort, R.P.S. Monitoring as a Tool for the Assessment of Wastewater Quality Dynamics. Ph.D. Thesis, Delft University of Technology, Delft, The Netherlands, 2011. [Google Scholar]
- Schilperoort, R.P.S.; Dirksen, J.; Langeveld, J.G.; Clemens, F.H.L.R. Assessing characteristic time and space scales of in-sewer processes by analysis of one year of continuous in-sewer monitoring data. Water Sci. Technol. 2012, 66, 1614–1620. [Google Scholar] [CrossRef] [PubMed]
- 2000/60/EC. Directive of the European Parliament and of the Council Establishing a Framework for Community Action in the Field of Water Policy (2000/60/EC). 23 October 2000. Available online: http://eur-lex.europa.eu (accessed on 3 July 2017).
- Weijers, S.R.; de Jonge, J.; van Zanten, O.; Benedetti, L.; Langeveld, J.; Menkveld, H.W.; van Nieuwenhuijzen, A.F. KALLISTO: Cost Effective and Integrated Optimization of the Urban Wastewater System Eindhoven. Water Pract. Technol. 2012, 7. [Google Scholar] [CrossRef]
- Benedetti, L.; Langeveld, J.; van Nieuwenhuijzen, A.F.; de Jonge, J.; de Klein, J.J.M.; de Flameling, T.; Nopens, I.; van Zanten, O.; Weijers, S. Cost-effective solutions for water quality improvement in the Dommel River supported by sewer-WWTP-river integrated modelling. Water Sci. Technol. 2013, 68, 965–973. [Google Scholar] [CrossRef] [PubMed]
- Langeveld, J.G.; Benedetti, L.; de Klein, J.J.M.; Nopens, I.; Amerlinck, Y.; van Nieuwenhuijzen, A.; Flameling, T.; van Zanten, O.; Weijers, S. Impact-based integrated real-time control for improvement of the Dommel River water quality. Urban Water J. 2013, 10, 312–329. [Google Scholar] [CrossRef]
- Daal-Rombouts, P.; van Benedetti, L.; de Jonge, J.; Weijers, S.; Langeveld, J. Performance evaluation of a smart buffer control at a wastewater treatment plant. Water Res. 2017. submit (under review). [Google Scholar]
- Vrugt, J.A.; Ter Braak, C.J.F.; Clark, M.P.; Hyman, J.M.; Robinson, B.A. Treatment of input uncertainty in hydrologic modeling: Doing hydrology backward with Markov chain Monte Carlo simulation. Water Resour. Res. 2008, 44, 1–15. [Google Scholar] [CrossRef]
- Vrugt, J.A.; Ter Braak, C.J.F.; Gupta, H.V.; Robinson, B.A. Equifinality of formal (DREAM) and informal (GLUE) Bayesian approaches in hydrologic modeling? Stoch. Environ. Res. Risk Assess. 2009, 23, 1011–1026. [Google Scholar] [CrossRef]
- Keating, E.H.; Doherty, J.; Vrugt, J.A.; Kang, Q. Optimization and uncertainty assessment of strongly nonlinear groundwater models with high parameter dimensionality. Water Resour. Res. 2010, 46, 1–8. [Google Scholar] [CrossRef]
- Leonhardt, G.; Sun, S.; Rauch, W.; Bertrand-Krajewski, J.-L. Comparison of two model based approaches for areal rainfall estimation in urban hydrology. J. Hydrol. 2014, 511, 880–890. [Google Scholar] [CrossRef]
- Van Daal-Rombouts, P.; Sun, S.; Langeveld, J.; Bertrand-Krajewski, J.-L.; Clemens, F. Design and performance evaluation of a simplified dynamic model for combined sewer overflows in pumped sewer systems. J. Hydrol. 2016, 538, 609–624. [Google Scholar] [CrossRef]
- Langeveld, J.G.; Clemens, F.H.L.R.; Van Der Graaf, J.H.J.M. Interactions within the wastewater system: Requirements for sewer processes modelling. Water Sci. Technol. 2003, 47, 101–108. [Google Scholar] [PubMed]
- Boogaard, F.; Lemmen, G. STOWA Stormwater Database: The Facts about the Quality of Stormwater Runoff; STOWA 2007-21; STOWA: Amersfoort, The Netherlands, 2007. (In Dutch) [Google Scholar]
- Krebs, P.; Merkel, K.; Kühn, V. Dynamic Changes in Wastewater Composition during Rain Runoff. In Proceedings of the 8ICUSD, Sydney, Australia, 30 August–3 September 1999; pp. 920–927. [Google Scholar]
- Bruns, J.; von Regenwasserbehandlung, D.K.; bei Mischwasserzufluss, A. Stuttgarter Berichte zur Siedlungswasserwirtschaft; Bd. 151. (Stuttgart, Univ. Diss., 1998); Oldenbourg: München, Germany, 1999. [Google Scholar]
- De Mulder, C.; Flameling, T.; Langeveld, J.; Amerlinck, Y.; Weijers, S.; Nopens, I. Automating the Raw Data to Model Input Process Using Flexible Open Source Tools. In Proceedings of the Frontiers International Conference on Wastewater Treatment, Palermo, Italy, 21–24 May 2017. [Google Scholar]
Model Parameter | Abbreviation | Unit | Search Range | Parameter |
---|---|---|---|---|
dilution factor | a1 | - | 0–2 | NH4, COD |
dilution delay factor | a2 | minutes | 0–600 | NH4, COD |
recovery factor | a3 | mg/(L·s) | 0–0.01 | NH4, COD |
peak first flush concentration | a4 | mg/l | 0–2000 | COD |
recovery factor first flush | a5 | mg/(L·s) | 0–10 | COD |
recovery factor small events | a6 | mg/(L·s) | 0–0.01 | NH4, COD |
Model Parameter | Abbreviation | NH4 Model | COD Model |
---|---|---|---|
dilution factor, large storms | a1,L | 0.95 (-) | 0.63 (-) |
dilution delay factor, large storms | a2,L | 123 (min) | 342 (min) |
dilution factor, medium storms | a1,M | 0.82 (-) | 0.47 (-) |
dilution delay factor, medium storms | a2,M | 115 (min) | 589 (min) |
recovery factor | a3 | 0.00025 (mg/(L·s)) | 0.00014 (mg/(L·s)) |
peak first flush concentration | a4 | Not applicable | 48 (mg COD/(L·s)) |
recovery factor first flush | a5 | Not applicable | 0.19 (mg COD/(L·s)) |
recovery factor small events | a6 | 0.00059 (mg/(L·s)) | 0.00017 (mg/(L·s)) |
Model Parameter | Abbreviation | NH4 Model | COD Model |
---|---|---|---|
dilution factor, large storms | a1,L | 0.96 (-) | 0.49 (-) |
dilution delay factor, large storms | a2,L | 373 (min) | 590 (min) |
dilution factor, medium storms | a1,M | 0.98 (-) | 0.49 (-) |
dilution delay factor, medium storms | a2,M | 427 (min) | 548 (min) |
recovery factor | a3 | 0.00033 (mg/(L·s)) | 0.00034 (mg/(L·s)) |
peak first flush concentration | a4 | Not applicable | 60 (mg COD/(L·s)) |
recovery factor first flush | a5 | Not applicable | 0.06 (mg COD/(L·s)) |
recovery factor small events | a6 | 0.00027 (mg/(L·s)) | 0.00002 (mg/(L·s)) |
© 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Langeveld, J.; Van Daal, P.; Schilperoort, R.; Nopens, I.; Flameling, T.; Weijers, S. Empirical Sewer Water Quality Model for Generating Influent Data for WWTP Modelling. Water 2017, 9, 491. https://doi.org/10.3390/w9070491
Langeveld J, Van Daal P, Schilperoort R, Nopens I, Flameling T, Weijers S. Empirical Sewer Water Quality Model for Generating Influent Data for WWTP Modelling. Water. 2017; 9(7):491. https://doi.org/10.3390/w9070491
Chicago/Turabian StyleLangeveld, Jeroen, Petra Van Daal, Remy Schilperoort, Ingmar Nopens, Tony Flameling, and Stefan Weijers. 2017. "Empirical Sewer Water Quality Model for Generating Influent Data for WWTP Modelling" Water 9, no. 7: 491. https://doi.org/10.3390/w9070491