OVERFLOW COMPLIANCE THROUGH REAL TIME CONTROL - SEWERFLEX SEWER NETWORK OPTIMISATION

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1 OVERFLOW COMPLIANCE THROUGH REAL TIME CONTROL - SEWERFLEX SEWER NETWORK OPTIMISATION Hestbæk, T. K. 1, Sørud, M. 1, Poulsen, T. S. 1, Grum, M. 1, Moelbye, N. and Joergensen, A. T. 1 1 Krüger A/S, Denmark Corresponding Author tina.kunnerup@kruger.dk Abstract Sewerflex focuses on advanced Real Time Control (RTC) of sewer networks. The main focus is to reduce Combined Sewer Overflow (CSO) events by utilising the existing storage volume in an optimised way and to reduce capital investment within new sewer infrastructure. The core of Sewerflex is a Dynamic Overflow Risk Assessment that uses a Generic Algorithm to minimise the total expected impact on the receiving waters. Setpoints for pumps and gates are communicated through the existing SCADA system. The system works on radar-based flow forecast, online measurements from the sewer system and an advanced algorithm. Two different case studies are presented. One case is regarding the city of Copenhagen, capital of Denmark, where the number of CSO events was reduced by 75% by utilisation of the existing sewer system. The other case is from Kolding, a town of about 60,000 citizens, where capital investments were reduced by 22% by implementation of RTC, which brought the City in compliance with the CSO requirements. This paper also lends focus to a review on how advanced RTC can be approached in at step-by-step way giving important value to the city along the way. Sewerflex is part of the product suite STAR Utility Solutions, an intelligent software for wastewater facilities. Keywords Overflow compliance, sewer network optimisation, Real Time Control (RTC), radar-based flow forecast, overview, Sewerflex, SewerView, PREACT, STAR Utility Solutions, reduce CSO events. Introduction In most old cities, rainwater is handled in combined sewer systems that are vulnerable to overflow events. A Combined Sewer Overflow (CSO) will occur when the rain intensity/volume exceeds a given threshold. 50 years ago it was not considered a problem discharging untreated sewage water to the surrounding water bodies. These days things are different, the utilities have to comply with increasing restrictions on discharge to receiving waters, e.g. the annual number of CSOs or annual discharge volume. These new demands regarding handling of sewage water has inspired utilities to look into alternative solutions on how to handle sewage water in the most optimal way. For most utilities there are two main reasons for looking into global, real time wastewater control. A desire to optimise the utilisation of the existing wastewater system and network

2 A desire to minimise the needed expansion (capital investments) of the existing wastewater system in complying with the environmental requirements. Conventional solutions Faced with an increasing focus on water quality in the receiving waters, resulting in more extensive restrictions on permitted overflow from combined sewer systems, the conventional solution is: BUILD BIGGER. To reduce the number of overflows and/or the overflow volume, a well-tested and effective method is to build storage volume in the sewage system. This approach will reduce the overflow, but it is a rather costly solution. Advanced Real Time Control - Sewerflex An alternative solution for reducing the number or volume of overflows could be using intelligent real time control to optimise the function of the sewer system. Sewerflex is part of Krüger s STAR Utility Solutions and is a highly advanced approach of RTC for sewer systems. Advanced Real time control is developed as an alternative to conventional storage volume building. The purpose is to utilise the existing volume in the sewer system in an optimised way before building additional storage volume. This is achieved by: Online real time control Radar-based flow forecast Optimisation routine and algorithms The Sewerflex control is a system wide control. It is not replacing the existing SCADA system, but is instead put on top of it. The local controls, which are often set to minimise risk of local flooding, are kept in place to handle those issues. The purpose of the overall control is to utilise the volume in the system in a way that minimises the CSOs, not to minimise local flooding. Online real time control Online real time controls in STAR Utility Solutions are based on holistic control and overview; all products are based on an integrated platform. The platform includes well-tested functionality regarding communication, fall-back strategies, data management and more. (Thomsen and Önnerth 2009) Radar-based flow forecast (PREACT) A very important part of the network optimisation concept is the ability to predict the state of the system some time into the future. Radar-based flow forecast can predict how much water the system has to manage and how the spatial variations are. A high-resolution radar is used to measure and predict rain for the coming 2 hours. Based on the expected rain, grey-box models are used to calculate flows and volumes in selected locations in the sewer system. These grey-box models get feedback information from sensors in the system, and uses this information auto-calibrate the model.

3 Optimisation routine (Dynamic Overflow Risk Assessment) The Dynamic Overflow Risk Assessment is a global optimisation strategy which uses a simplified representation of a sewer system. The Strategy tries to minimise the expected overflow risk by considering: The water volume presently stored in the sewer network The expected runoff volume (radar-based flow forecast) The estimated uncertainty of the runoff forecast The capacity in the network and treatment plant The cost of an overflow A cost is associated to each node (control point) of the sewer network. The cost reflects the sensitivity of the receiving water to CSO discharge. The control strategy tries to minimise the total cost associated with CSO discharge by reducing the CSO volume as much as possible, and if it cannot be avoided then to discharge in locations where the cost is low. (Vezzaro and Grum 2012) Step-by-step approach It is a quite comprehensive task to start from scratch and build up an advanced, real time control Sewerflex system with a Dynamic Overflow Risk Assessment control. However, the process can be broken down to steps, which individually, give value to the city. A step-by-step approach can be: 1. SewerView (overview and basic holistic understanding of the related reactions in the wastewater network/system ) 2. PREACT (Radar-based flow forecast) 3. Sewerflex (Advanced real time control) These three steps can all be combined with Hydraulic models running in parallel. SewerView (overview) The first step is getting an overview of how the system is performing. This is done using SewerView. In this part of the application there is no control. SewerView is monitoring the system and is showing selected information. This information can for example be: Storage tank fillings Pump status on/off Gate positions Selected water levels CSO active/not active Rainfall depth This information is shown real time on the user interface. The user also has the opportunity to go back in time and see the state of the system for a given time, or to go back and replay the state of the system for a given time period. From the user interface, it is possible to generate reports based on the data from the system. (See Figure 1). SewerView can be used to improve the understanding of how the system is working and to detect abnormal conditions with pumps, infiltrations etc.

4 Figure 1: SewerView user interface, example from Hjørring, Denmark. Hydraulic model operating in parallel A hydraulic model such as MIKE URBAN or INFOWORKS can be run either in parallel (real-time) or once per day, using measured precipitation as input. A model can be run whether you are just starting up with SewerView or doing the grand solution with Sewerflex. Comparison between the real world and the model can be used to improve the description of the system in the model. But the comparison can also be used to catch problems in the real world earlier than usual. For example, a comparison of runtime for pumps or levels in the system indicates that there is a blockage somewhere upstream in the system. A problem that otherwise perhaps would not have been realised until a citizen phones with sewage water in the basement.

5 PREACT (Radar-based flow forecast) The second step would be to implement the radar-based flow forecast, called PREACT. As described above, PREACT is a real-time operating forecast software for estimating future flows or levels in selected parts of the sewer systems, e.g. pumping stations, basins or inlet to wastewater treatment plants for early adjustment to wet weather mode. The forecast is based on radar information/weather forecast and is transferred into flow forecast for the system. The early warning system can be connected to existing control strategies or may be applied as input data for a new, general control strategy for wastewater systems. The early warning helps the operators to react and take the necessary actions. Sewerflex (advance real-time control) After getting the overview and flow forecast it is time to start optimising the system. This can be done either by rule-based RTC or by implementing dynamic overflow risk assessment Sewerflex. The rule-based strategy is based on analyses made when the RTC system was configured and then the control system is reacting to online measurement in the sewer network. Dynamic overflow risk assessment is based on genetic algorithms. For every time step, it tries to optimise the function of the system. The advanced real-time control Sewerflex is described above. Case study Copenhagen The Sewerflex system was demonstrated in Copenhagen, the capital of Denmark, in an ambitious development project. Over the past 15 years, Copenhagen has transformed its former industrial harbour into an area with bathing water and water sports. In that process, Copenhagen has modernised its combined sewer system to minimise CSOs. The modernisation has been performed both structurally as well as operationally. Between the mid-1990s and 2008, Copenhagen invested heavily in storage tank volume to protect bathing water by minimising CSO events. This has been a success and today, the system meets a service level demanding no more than 1 CSO per year near bathing water. However, during wet weather the number of overflows to the sea (not near bathing water) still remains near 100 per year. The aim of the project was to reduce that number without risking the bathing water in the harbour area and without further extension of the storage capacity. Figure 2 shows location and key figures for the Copenhagen catchment.

6 Lynetten Catchment WWTP capacity: 750,000 Storage volume: 115,000 m 3 Control points: 7 Catchment area: 7,600 ha Figure 2: Location and key figures for the Copenhagen case study. In the Copenhagen case, the Sewer system and the WWTP is considered as one unit. That means that the actual capacity at the WWTP at any time is used as a boundary condition when optimising the functionality of the sewer system. Also, the radar-based flow forecast is used to activate wet weather operation at the WWTP and in that way increase the flow through the WWTP. This, in return, will decrease the CSO volume in the sewer system. Figure 3: Simplified system configuration for the Copenhagen case.

7 The project was run in three phases. 1. Offline modelling 2. Online modelling 3. Live test Offline modelling After configuration of the system, the initial test was run using a MIKE URBAN (MU) model of the real system. In this phase, the input to Sewerflex came from a model of the real world and the calculated setpoints were sent back to that model to evaluate the effect of Sewerflex. This was done to verify how the system would react without risking the operation of the real sewer system. Also, the model setup was used to simulate long time series, to see the statistical effect of the chosen setup. Online modelling After the offline setup had proven effective in simulations, the next step was online modelling. In this phase, the input to Sewerflex was measured values from the SCADA system. From that input optimal flows for the system were calculated and setpoints to the actuators in the system were generated. However, the setpoints were not sent to the real system but to a MU-model running in parallel with the real world. During and after the rain events, it was then possible to evaluate the calculated setpoints to see whether they were as expected. The evaluation was performed in order to get familiar with the Sewerflex before it took over the control of the sewer system. Later in the project, two semi-online models were run to evaluate the effect of Sewerflex. One model was run with the pre-sewerflex RTC and one was run with Sewerflex. Both models received precipitation data from the radar. The model running with Sewerflex was also receiving the same flow forecasts as the actual system. This was considered the best way to evaluate whether the Sewerflex control had improved the function of the sewer system for a given rain event. Live test After a considerable amount of offline and online modelling, the function of Sewerflex was trusted and the control of the sewer system was handed over to Sewerflex for a rain event. In the beginning, Sewerflex was only turned on during daytime (8:00 to 16:00) so that an operator would always be able to turn off the system should unwanted situations occur. In this phase, two online models of the system were running in parallel with the real world as mentioned previously. After a given rain event, the effect of Sewerflex was evaluated based on the online data from the STAR system as well as the results from the two online models and all data are saved on the common STAR platform.

8 Figure 4: Optimal flows calculated by Sewerflex during a rain event, shown on the STAR user interface Results Two rain events are never identical, so it will not be possible to compare the exact results from two different control strategies. Therefore, the best way to evaluate the effect of using Sewerflex is to make evaluations based on model simulations. Simulation studies have shown that by using Sewerflex, Copenhagen can reduce the number of overflow per year from around 100 to around 10. This reduction is achieved by retaining sewage water in the basins upstream in the system during smaller/normal rain events. In this way, the discharges downstream from the sewer network are avoided during the events without risking the bathing water. All discharge events with a CSO volume up to 10,000 m 3 were documented eliminated, without any further capital investment. Overall, the result can be summarised to: 94 per cent saving on capital investments (compared to building storage volume to reduce the CSO number equally). Number of overflows reduced by 75 per cent More than 20 per cent reduction in overflow volume. Evaluation of general potential for Sewerflex In a recent study (Joergensen et al. 2014), the potential of Sewerflex was evaluated. The Copenhagen Case was one out of 6 different urban catchments, which was evaluated using hydraulic models. The effect of Sewerflex was evaluated against existing rule-based RTC in the catchments. Figure 5 shows the calculated CSO discharge volume for all events in the 6 catchments. The x-axis shows the discharge volume for a baseline calculation without Sewerflex and the y-axis shows the discharge volume for the same event, but WITH Sewerflex. For all events located below the dark line, Sewerflex has reduced the discharge volume. As the figure shows, the discharge volume for almost all the events is reduced due to Sewerflex. The catchment named Lynetten in that survey is the Copenhagen catchment, presented in the case study above.

9 Figure 5: Reduction of CSO discharge volume from baseline Scenario to DORA Scenario. (Joergensen et al. 2014) The result for all six catchments showed reduced CSO discharge volume. Figure 6 shows the reduction in CSO volume for all six catchments. The potential for reducing CSO volume using Sewerflex will be very different form catchment to catchment. The most important parameter being the existing storage volume. It is difficult to optimise the utilisation of storage volume, if there is no volume.

10 Figure 6: Reduction of CSO discharge volume from baseline Scenario to DORA Scenario. (Joergensen et al. 2014) Case study Kolding Kolding, a city in Denmark, with 60,000 inhabitants, had issues meeting the requirements regarding the number of CSO events per year. Analyses indicated that if the conventional solution was chosen, they would need an additional storage volume of 6,000 m 3. However, it was difficult to find the space, and it was expensive to build the storage in the middle of the city. From that perspective, Blue Kolding a/s (the utility company in Kolding) looked into alternative solutions. The City decided to go for an optimised utilisation of the existing storage volume. A lot of effort was put into designing an RTC-strategy for 15 selected storage locations in the sewer system. The RTC-strategy was designed using a MIKE URBAN model of the sewer system to analyse the consequences of different alternatives. At the end the RTC-strategy was based on water levels measured real time in the sewer system. The model simulations were used to verify that the chosen RTC-strategy did not increase the water level in the system during rain which was an essential requirement from the City. As part of the implementation of the RTC-strategy in Kolding, the City installed SewerView (see Figure 7) in order to have a place to monitor the performance of the RTC-strategy. But it also had the added benefit of improving the knowledge of the functionality of the system.

11 Figure 7: SewerView in Kolding providing an overview of the current state in the sewer system. (Moelbye et al. 2011) Implementation of the RTC-strategy reduced the needed storage volume from 6,000 m 3 to 2,900 m 3. Still meeting the required demand less than 10 CSO events per year. Table 1 shows the key figures from the project. Table 1: Required investment to meet goal (10 CSO events per year) CSO [m 3 /year] CSO [number] Installation Price [million ] Before 148, Scenario 1 75,000 < 10 6,000 m 3 storage volume 2.7 Scenario 2 75,000 < 10 2,900 m 3 storage volume and RTC 2.1 By implementation of Scenario 2, Blue Kolding a/s managed to comply with the demand of max. overflow events and to reduce the capital investment by 22% as well. The costs related to Scenario 2 include the costs related to building additional storage volume (2,900 m 3 ), development and implementing the control strategy, purchase and instalment of 6 controllable gates to manage flow in the sewer system. As an extra feature, the MIKE URBAN model of the sewer system runs every night using precipitation data from 5 rain gauges in the catchment from the previous day. The results can be used to increase the knowledge of the system and to validate the quality of the Hydraulic model of the sewer system. This is important if the model is to be used for future design/optimisation projects.

12 Conclusion Advanced Real Time Control of sewer network (Sewerflex) focuses not only on the reduction of CSO and the environmental surroundings, but also on the capital investment. The use of advanced RTC to optimise the functionality of an existing sewer system can generate significant savings compared to conventional solutions, such as extending storage volume, and still secure overflow compliance. The environmental savings (reductions in CSO) are achieved by using the existing sewer infrastructure and using algorithms to give priority to the best (less damaging) overflow point in the specific situation. The increased capacity in the system obtained by the Sewerflex reduces overflow/bypass and increases the flow through the existing wastewater treatment plant. In the Copenhagen case, advanced real-time control was found to have the same effect as an additional storage volume of 10,000 m 3, with a reduction in the numbers of overflows of 75%. In Kolding, the storage volume needed to comply with regulations was reduced from 6,000 m 3 to 2,900 m 3 by implementing advanced real-time control in the sewer system, and reduced the capital investment by 22%. References Grum, M.; Thornberg, D.; Christensen, M. L.; Shididi, S. A. and Thirsing, C. (2011) Full-Scale Real Time Control Demonstration Project in Copenhagen s Largest Urban Drainage Catchments. 12 th International Conference on Urban Drainage, Porto Alegre, Brazil, September Joergensen, A. T.; Grum, M.; Vazzaro, L. and Kunnerup, T. (2014) Evaluating Potential of Forecast- Based Global Real-Time Control Strategy on Six Urban Catchments. 13 th International Conference on Urban Drainage, Sarawak, Malaysia, September Moelbye, N.; Pouelsen, T. S.; Ravn. C. and Geilager, J. (2011) Mere intelligens mindre beton bedre miljø (in Danish, More intelligence - less concrete - better environment ). Spildevandsteknisk tidsskrift (Danish journal). Thomsen, H. R. and Önnerth, T. B. (2009) Results and benefits from practical application of ICA on more than 50 wastewater systems over a period of 15 years. 10 th IWA Conference on INSTRUMENTATION, CONTROL & AUTOMATION, Cairns, Australia, June Vezzaro, L. and Grum, M. (2012) A generalized Dynamic Overflow Risk Assessment (DORA) for urban drainage RTC. 9 th International Conference on Urban Drainage Modelling, Belgrade, 2012.