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Potential of using mass-volume curve prediction for water quality-based real time control
Duy Ly, Thibaud Maruéjouls, Guillaume Binet, Jean-Luc Bertrand-Krajewski
To cite this version:
Duy Ly, Thibaud Maruéjouls, Guillaume Binet, Jean-Luc Bertrand-Krajewski. Potential of using mass-volume curve prediction for water quality-based real time control. 11th UDM – International Conference on Urban Drainage Modelling, Sep 2018, Palermo, Italy. pp.700-704. �hal-01885463�
Potential of using mass-volume curve prediction for water quality-based real time control
*Duy Khiem Ly 1, Thibaud Maruéjouls 2, Guillaume Binet 2, Jean-Luc Bertrand-Krajewski 1
1University of Lyon, INSA Lyon, DEEP, EA 7429, F-69621 Villeurbanne cedex, France
2Suez Eau France, LyRE, 43 rue Pierre Noailles, 33400 Talence, France
Abstract: This study implements two real time control (RTC) strategies on a small sewer network: water quality-based real time control (QBR) using mass-volume (MV) curves versus hydraulics-based RTC (HBR) and then compares their efficiencies. The network consists of a retention tank, a combined sewer overflow (CSO) structure and several actuators (valves and weir) for regulation. Initial results from the first demonstration on a rain event of 17.8 mm depth and 4-hour rainfall duration reveal the potential of the QBR. It offers a 10.7 % reduction in CSO load while increasing CSO volume by 9.1 %, when compared to the HBR. The next step is being planned to characterize the types of rain events for which QBR offers the highest efficiency.
Keywords: mass-volume curve; real time control; combined sewer overflow
1. INTRODUCTION
Real time control (RTC) is considered as a cost-efficient technology to optimize the available capacity of an urban drainage system during wet weather and thereby alleviate the impact of combined sewer overflows (CSOs). The majority of RTC implementations are hydraulics-based RTC (HBR), which aims at minimizing CSO volume through the use of online hydraulic measurements (Schutze et al., 1999, Pleau et al., 2005). Water quality-based RTC (QBR) was initiated more recently and has received increasing attention (Lacour et al., 2011; Vezzaro et al., 2014) due to advancements in wastewater quality sensors, modelling tools, and more rigorous adoptions of legislations to preserve receiving water bodies’ ecological status, e.g. European Water Framework Directive and French Decree dated 21 July 2015 on the compliance of sewer networks. This study proposes the application of mass-volume (MV) curves (Bertrand-Krajewski et al., 1998) as a potential new approach for QBR and presents the results from its first demonstration on a small sewer network, in comparison to HBR.
2. MATERIALS AND METHODS
2.1 Study area and modelling tool
The urban catchment in this study is adapted from the Perinot catchment to facilitate quick and realistic RTC demonstration (Figure 1). In reality, Perinot is part of the Louis Fargue catchment, covering most of the urban areas in Bordeaux, France. Sewer hydraulics and water quality processes models are developed for control strategies using SWMM-TSS software, which contains an improved SWMM5.1.11 (USEPA) model library (Maruéjouls et al., 2012; Montserrat et al., 2017). The model applied for control strategies is extracted from a full-scale model developed for the whole 7700 ha Louis Fargue catchment, which is already calibrated and validated with long-term measurement data of turbidity, flow, and water level at four main catchment outlets (Montserrat et al., 2017). Total suspended solids (TSS) is used as the quality state variable since it is the primary source of pollution transfer within the sewer pipes. TSS time series are obtained from turbidity time series by means of their correlation derived from several sampling campaigns during rain events (as presented in Maruéjouls et al., 2017).
Rainfall data are extracted from a rain gauge located inside the catchment.
* Proceedings of the 11th UDM – International Conference on Urban Drainage Modelling, Palermo, Italy, 23-26 Sept., 700-704.
2.2 Control elements
Global control (see Figure 1) is based on regulating the weir F to fill the tank or the valve C to allow discharge through the CSO structure. Inlet offset of valve C is at 0.54 m, three times the maximum dry weather (DW) water depth at junction node D1. Valve P settings depend on the water depth at junction node D2; once D2 depth reaches the threshold of 0.31 m, opening of the valve should be minimal to ensure that the flow to waste water treatment plant (WWTP) remains always below 0.3 m3/s, i.e. three times the maximum DW flow. Emptying of the tank is done after the rain event by adjusting valve E according to D2 depth too. All possible operational positions of the actuators are described below:
weir F: 0 – no fill, 1 – fill.
valve C: 0 – no CSO, 1 – CSO.
valve P: 0.5 – D2 depth < 0.31 m, 0.033 – otherwise.
valve E: 0 – no empty, 0.05 – gradual empty if D2 depth > 0.31 m, 1 – fast empty if equal to or lower than 0.31 m.
Figure 1. Adapted Perinot catchment and control elements.
2.3 Control methods and performance measure
Two control strategies are performed in this study: the QBR using MV curve versus the HBR. Both share the primary objective of avoid flooding in the network. The first strategy additionally targets reduction of CSO load while the second one focuses on reduction of CSO volume.
The diagram in Figure 2a illustrates the closed-loop simulation scheme implemented in both strategies.
It involves using a controller to predict processes and solve the objective function within the incoming control time interval (CTI). The solution is then applied to the real system by means of actuator positions, letting the system evolves till the end of the CTI and measuring its states to set initial conditions for the next CTI. SWMM-TSS is applied to build both the model representing the virtual reality that provides measurements to the controller and the model for predictions in the controller.
Strategy for QBR. The MV curve is a dimensionless way of representing the variation of the cumulative pollutant mass with respect to the cumulative volume during a rain event. Figure 2b displays the upstream MV curve of the rain event selected for this study. It is derived from the simulated flow and TSS arriving at junction nodes U1 and U2. The SWMM-TSS model in the controller is run for the period between the beginning of the incoming CTI and the end of the rain event. Modelling outputs are used to predict the upstream MV curve. The rules for QBR are set as follows: should D1 depth be greater than 0.54 m during the incoming CTI, upstream MV slopes of this CTI and all further CTIs with D1 depth also greater than 0.54 m are picked and then sorted from largest to smallest one to determine the rank of the incoming CTI (e.g. rank k). Exceeding volume in each CTI from ranks 1 to k-1 can be obtained based on the difference between the simulated discharge upstream and three times its maximum DW discharge. If the sum of these volumes is less than the current tank capacity, exceeding volume of the incoming CTI needs to be stored in the tank. Otherwise, this volume is discharged through CSO.
Strategy for HBR. During the incoming CTI, the tank is fed if D1 depth rises above 0.54 m. If the tank is full, exceeding stormwater is spilled via CSO.
Tank
weir F valve E
CSO valve C D2
D1
V = 2250
U1
valve P
U2
Rain gauge
Catchment area: 205 ha Population: 2931 Pipe length: 2054 m
Performance measure. Total overflow loads and volumes in each strategy are estimated to evaluate their performances.
Figure 2. a) Closed-loop simulation scheme b) MV curve for the rain event on 23 April 2016
3. RESULTS AND DISCUSSION
The rain event used for demonstration has a return period of three months, with total depth of 17.8 mm and rainfall duration of approximately 4 hours. Figure 3 shows that QBR allows CSO discharges in the beginning and fills the tank mostly at the latter part of the event. This is in agreement with the evolution of the upstream MV curve in Figure 2b and significantly different from the behaviour of HBR. The sharpest increase in the curve slope, representing the period containing the most polluted stormwater, is detected near the end of the event. On the other hand, HBR stores all the exceeding stormwater from the start to avoid CSO. Subsequently when the tank reaches full capacity, exceeding stormwater with higher concentration has to be spilled out. In total, the CSO load spilled by QBR during the rain event is 10.7 % lower than by HBR although the CSO volume is 9.1 % higher.
Furthermore, it takes longer time for exceeding stormwater to fill the tank through weir K than to spill through valve C. The depth at node D1 is thus usually higher in the case of HBR. There are times of fully pressurized flows and D1 depth increases rapidly, but still remains lower than the ground level. It is also observed that the downstream flow to WWTP is well maintained below the limit of 0.3 m3/s.
Figure 3. Differences in results between the two control strategies.
Another rain event is utilized to compare the two strategies but both provide identical actuator positions and performances. This is because the highest gain of MV slope occurs at the beginning of this rain event and along with the largest discharge of stormwater. The next step is to identify the types of rain events for which QBR offers the highest efficiency. This is planned to be presented in the conference later.
Measurements
Reality
Controller
Actuator positions
0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1
0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1
Accumulated Load /Total Load
Accumulated Volume /Total Volume
0 20 40 60
8:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00
Rain intensity (mm/h)
0 30 60 90 120 150
8:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00
CSO Volume (m3/5mins)
QBR HBR
0 15 30 45 60
8:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00
CSO Load (kg/5mins)
QBR HBR
QBR: total volume = 4435 m3 HBR: total volume = 4065 m3
QBR: total load = 760 kg HBR: total load = 851 kg
0 700 1400 2100 2800
8:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00
Tank Volume (m3)
QBR HBR
0 1,5 3 4,5 6
8:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00
Depth at D1 (m)
QBR HBR 3 times max DW depth
Ground level
0 0,1 0,2 0,3
8:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00
Flow to WWTP (m3/s)
QBR HBR
CONCLUSIONS
The results obtained from this study indicate the potential of using MV curves for QBR. Given the selected rain event, QBR using MV curves offers an obvious advantage in terms of CSO load reduction when compared to HBR. More detailed investigations are being carried out to further improve this newly proposed strategy and to characterize the types of rain events that can benefit from this approach.
Acknowledgements
The authors wish to thank the EU funding for LIFE EFFIDRAIN LIFE14 ENV/ES/00080 and the great technical and financial supports from Bordeaux Metropole and SGAC.
References
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