Improved design and optimization of subsurface flow constructed wetlands and sand filters Alessandro Brovelli, Otoniel Carranza-Díaz, Luca Rossi and D. Andrew Barry
Abstract EGU2010-5797
Ecological Engineering Laboratory, Ecole Polytechnique Fédérale de Lausanne, Lausanne CH-1015, Switzerland, http://ecol.epfl.ch. Emails:
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1. Introduction
2. Goal and methodology of the work
Constructructed wetlands (CWs) and sand filters are wastewater treatment devices (see Kadlec and Wallace, 2009 for a review).
Motivations: 1. To study the effect of substrate heterogeneity on the biological processes involved in wastewater purification and on the treatment efficiency of horizontal subsurface flow constructed wetland; 2. To develop a process-based design methodology able to incorporate the effect of porous medium stochastic variability.
Three key advantages compared to traditional treatment plants: (i) cheaper, (ii) low energy consumption and (iii) fit well in natural landscapes (see Fig. 1).
However, treatment efficiency is often suboptimal, and CWs must be over-sized to guarantee good contaminant removal.
Approach: Numerical simulations using a reactive transport code were conducted to study the effect of heterogenity on inert tracers and oxidizable contaminants (benzene).
Figure 1. Example of CW in Estonia
Low efficiency has been attributed to flow short-circuiting, short residence time and ultimately to hydraulic conductivity heterogeneity (detected using tracer experiments Fig. 2).
Spatially correlated hydraulic conductivity random fields were used to simulate heterogeneity and flow short circuiting (Fig. 5).
Heterogeneous distribution of the porous substrate can be observed in Fig. 3 (image analysis from data reported in Suliman et al, 2007).
Four scenarios were studied, varying the aspect ratio of the conductivity field (Tab. 1).
Figure 2. Observed and theoretic BTCs
Figure 6. Mean HRT distributions in homogeneous (red) and heterogeneous conditions.
Figure 5. Examples of hydraulic conductivity random fields used to simulate the filtering medium of the simulated CW. Table 1. Properties of the five scenarios considered.
A Monte-Carlo algorithm was used:
1. for each scenario, 30 statistically equivalent realizations of the random field were generated
Figure 3. Porosity variation (%). Data from Suliman et al., 2007.
The proposed design methodology is made up of the following steps: Define a target water quality.
Figure 4. Hydraulic conductivity variation (%)
2. the a-posteriori distributions of hydraulic residence time (HRT) and degradation efficiency were studied
Figure 7. Vertically-averaged longitudinal distribution of benzene, oxygen and biomass with (black) and without (red) heterogeneity. The dashed line is the mean of the a-posteriory distribution, the area shaded in gray represents ±1 standard deviation.
The impact on benzene distribution is dramatic (Fig.7) Degradation is significantly reduced in heterogeneous conditions. Biomass development patterns (and degradation rates) reproduce the distribution of hydraulic conductivity (Fig. 8).
Figure 8. Example of biomass distribution in the heterogeneous case.
Numerical modelling was used to study and highlight the importance of substrate heterogeneity on the degradation performance. It was shown that contaminant degradation is very sensitive to the spatial correlation of the hydraulic properties (aspect ratio of the conductivity field). Careful construction and filling of the container can therefore reduce problems related to shortcircuiting. The biomass distribution patterns are intimately connected to the spatial distribution of hydraulic conductivity: this as important implication for both sampling and modelling. Further details and discussion can be found in Brovelli et al., 2010.
Estimate the expected heterogeneity, in terms of amount (variance of the PDF) and spatial correlation. The values are controlled by the grain size distribution of the porous material and the wetland’s filling strategy (Suliman et al., 2007).
Use the PoF to identify the optimal size of the system to be built.
Simulated tracer experiments were used to study HRT (Fig. 6): as heterogeneity increases, the mean HRT decreases and the HRT distribution becomes broader
5. Summary and conclusions
4. Design methodology
Estimate the target hydraulic residence time (THRT), the HRT required to achieve the necessary degradation. This can be done combining previous experience and numerical modelling (e.g. Langergraber et al., 2009).
Run a suite of Monte-Carlo simulations and compute the probability of failure (PoF) as a function of the distance from the inlet.
3. Effect of heterogeneity on residence time and biological transformations
6. References •
Figure 9. Definition of the target hydraulic residence time (THRT) for 2 water qualities: 90% removal of benzene and World Healt Organization benzene concentration limit for drinking water. The THRT is the minimum residence time required to achieve the target concentration of the contaminant.
Figure 10. Computed probability of failure of the system based on the results of the Monte-Carlo simulations. With the current wetland dimensions, 90% removal can be achieved in nearly all conditions, while the WHO limit can only be achived in homogeneous conditions or with very mild heterogeneity.
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Brovelli, A., Carranza-Diaz, O., Rossi, L., Barry D.A. 2010. Design methodology accounting for the effects of porous medium heterogeneity on hydraulic residence time and biodegradation in horizontal subsurface flow constructed wetlands. Submitted to Ecological Engineering. Kadlec, R.H., Wallace, S.D., 2009. Treatment Wetlands, 2 nd ed. CRC Press, Boca Raton, FL, USA, p. 1016. Langergraber, G., Giraldi, D., Mena, J., Meyer, D., Peña, M., Toscano, A., Brovelli, A., Korkusuz, E.A., 2009a. Recent developments in numerical modelling of subsurface flow constructed wetlands. Science of the Total Environment, 407 (13), 3931–3943. Suliman, F., Futsaether, C., Oxaal, U., 2007. Hydraulic performance of horizontal subsurface flow constructed wetlands for different strategies of filling the filter medium into the filter basin. Ecological Engineering, 29 (1), 45–55.
