The Use of a Decentralized Wireless Sensor Network for CSO Abatement and Control L. A. Montestruque1 and T. P. Ruggaber2 1 EmNet, LLC, 12441 Beckley St., Suite 6, Granger, IN 46530; PH (574) 360-1093; FAX (574) 968-0269; email: firstname.lastname@example.org 2 A.M. ASCE, EmNet, LLC, 12441 Beckley St., Suite 6, Granger, IN 46530; PH (574) 303-3031; FAX (574) 968-0269; email: email@example.com Abstract EmNet, LLC, the City of South Bend, IN, the University of Notre Dame, and Purdue University have developed CSOnet, an embedded sensor network that provides decentralized real-time control (RTC) for CSO abatement. Unlike conventional centralized RTC systems, CSOnet implements optimal control strategies using a distributed network of microprocessors rather than a massive central computer, which results in a system that is robust, incrementally implementable, cost- effective, and easily integrated into any existing infrastructure. CSOnet was demonstrated during a pilot demonstration, which increased the storage ability of a retention basin by up to 110% and cost 55% less than conventional solutions. Furthermore, in a series of computer simulations, CSOnet was able to reduce the amount of overflow by up to 84% through interceptor optimization, to significantly reduce the number of CSO events, and to prevent dry weather overflows. Introduction Each year in the United States, combined sewer overflow (CSO) events result in the release of 3,217 billion liters (850 billion gallons) of untreated wastewater into lakes and rivers, causing drinking water contamination, human illness, animal and fish kills, and eutrophication (USEPA 2004). The United States Environmental Protection Agency (USEPA) estimates that reducing this problem by 85% over the next 15 years will cost over $50.6 billion using traditional abatement methods (sewer separation, treatment plant expansion, etc.) An alternative to construction intensive solutions such as sewer separation projects or waste water treatment plant (WWTP) expansions is the use of real time control (RTC) systems. RTC solutions can make use of underutilized in-line and off-line storage areas to precisely control the flows within the sewer system. This allows the optimization of the transport system and delivery to the WWTP effectively reducing overflows at a reduced cost. These systems tend to be centrally controlled, requiring a powerful central computer to solve thousands of complex equations every few minutes (Heinz and Schultz 2006, Colas et al. 2001). Unfortunately, RTC systems have not been widely used due to the cost of the components and the complexity of the control system. Furthermore, existing RTC systems do not scale linearly, meaning that the system is proportionally more expensive for smaller municipalities than large cities (Pleau et al. 2005). Many of these obstacles can be overcome through the use of a decentralized RTC scheme. In this system, control decisions are made and implemented by a network of microprocessors, which gather data from the local area and communicate with each other to determine the optimal control strategy (see Figure 1). Such a system is inherently redundant, robust, and easily installed (either incrementally or at one time) into any existing infrastructure (Ruggaber et al. 2006). EmNet, LLC, the City of South Bend, The University of Notre Dame, and Purdue University have collaborated extensively during the past two years to create and implement just such a system, called CSOnet. CSOnet Description CSOnet is composed of a number of embedded real-time processors that communicate over a multi-hop wireless communication network. Each node in CSOnet consists of at least three basic subsystems; the sensor/actuation subsystem, the processor module, and the wireless communication subsystem. The processor module is either a micro-processor or embedded single board computer that is interfaced to sensors and actuators through the sensor/actuation subsystem. The processor module can store sensor information and can make control decisions based on information it receives over its wireless communication subsystem. The novelty of CSOnet rests in two main aspects; information dissemination using low power radio systems and a decentralized control for regulating in-line and off-line water storage. The first aspect uses multi-hop mesh networking techniques that allow the devices to communicate over large distances by relaying data through other intermediary devices. This differs from traditional radio modem approaches where each device communicates directly with a central controller, usually located at the WWTP. For a large number of sensors (>50), these centralized data-gathering systems require considerable bandwidth since one single central location must communicate with each device simultaneously at each sampling time. Radio modem installations can be expensive (around $10,000 per location) whereas a CSOnet device costs approximately $2,000 per location. The second novelty of CSOnet rests in its use of decentralized control strategies. Typically, a central computer gathers the information from all system sensors using the radio modem connections. After all the information is received, the central computer calculates a control strategy using a complex model of the sewer system and an optimization algorithm. Traditionally, receding-horizon predictive global optimization control techniques are used over a SCADA system. The weakness of this centralized method is that it relies heavily on a-priori models of the existing sewer system. Additionally, it requires powerful computational infrastructure to obtain adequate set-points for the flow diverting structures in a timely manner, especially if the amount of real-time information on the sewer network comes from a limited number of field sensors. CSOnet, on the other hand, processes information in a decentralized manner. Each processor in CSOnet sits in a manhole or retention basin of the sewer system. Each CSOnet processor only gathers data from its adjacent neighbors to make control decisions that asymptotically approach the optimal control achieved using centralized methods. Moreover, CSOnet’s use of recursive feedback mechanisms in controlling wastewater flows endows it with added robustness to model uncertainty. CSOnet, therefore, has the ability to adapt to unforeseen changes in the sewer infrastructure, something which is entirely impossible for traditional centralized SCADA-based solutions. The technology implications of the use of CSOnet or centralized SCADA-based solutions are multiple and have orthogonal results (see Figure 2). The wireless nature of CSOnet allows its implementation into existing distribution systems with only minor infrastructure modifications. No hardwired communication media such as optic fiber, coaxial cable, or twisted pair is needed. If such networks already exist, the nodes can easily access them. Some roadwork and sewer work is required for the placement of Smart Valves (actuated valves) into the sewer lines and retention basins, but this localized work can be done with minimal inconvenience to the public. Smart Valves do require electricity, but battery or solar- powered options are possible. The concluding differences between the use of the CSOnet solution or the SCADA based solution are summarized in Table 1. These are powerful motivators for municipalities with restricted economic and engineering resources that seek a technologically efficient solution that will bring both EPA compliance and environmental enhancement. TABLE 1. Comparison between CSOnet control solution and a SCADA- based control solution. CSOnet decentralized control SCADA-based centralized solution control solution Inexpensive: reusable and Expensive: customization Control Algorithm homogeneous components. required for each Implementation Cost municipality. Inherent robustness due to Highly dependent on: localized decision, massive hydraulic model, Robustness spatial and temporal feedback, communication and communication infrastructure, and central redundancy computer. No communication Expensive communication infrastructure required, no infrastructure and Hardware Cost central computing element computing systems due to required. large bandwidth requirements. Fast and inexpensive, battery Slow and expensive, or solar operated devices form requires implementation of Implementation ad-hoc network and perform computing center, design of decentralized control. control architecture, electrical installations. The chief technological components in CSOnet were developed by EmNet LLC, Purdue University, and the University of Notre Dame. The original concept for CSOnet was developed by Drs. Jeff Talley and Michael Lemmon (University of Notre Dame) as a method for monitoring and controlling CSO events. The realization of that concept relied on hardware designs and middleware services developed at EmNet LLC, as well as a novel antenna design developed by Dr. W. Chappell of Purdue University. The remainder of this section briefly describes the existing CSOnet technology. CSOnet consists of three main types of embedded nodes (see Figure 3), which are refered to as the gateway (Gnode), the instrument node (Inode), and the routing node (Rnode). The Inode and Rnode are both based on the Chasqui processor module which was developed by EmNet LLC for this project. The difference between these two node types is that the Inode has a sensor/actuation subsystem, whereas the Rnode (which is only used to relay data between nodes) requires no such subsystem. The Gnode is a single board embedded computer that can connect to the Internet and which often has an actuator interface for controlling valves in the sewer network. In practice, Inodes gather data about head level and flow rates within the sewer system, Rnodes relay that sensor data to a Gnode, and the Gnode then computes the required control scheme and controls valves within the network to adjust local flow rates within the sewer system. Note that connectivity between the Gnodes do not necessarily require accessibility to the internet or other network, although scalability is guaranteed if the network can be partitioned in subnets with Gnodes connecting with each other through a faster media. The main piece of technological hardware is the Chasqui processor module. The Chasqui Wireless Sensor Node was developed by EmNet LLC to address a number of real-life issues that were found in existing embedded network nodes such as Crossbow’s MICA2 processor module. These issues concerned the limited radio range of the MICA2 module and the need for specialized sensor/actuator interfaces required in this application. To answer these questions, EmNet LLC started with the original embedded node designs developed by U.C. Berkeley and modified the radio subsystem and sensor/actuator interface subsystems. The Chasqui node uses a MaxStream radio operating at 900MHz that uses Frequency Hopping Spread Spectrum (FHSS) signaling to reduce interference and allow large range (TX power = 100mW with a range >700m in urban environments and >5km in line of sight). The radio complies with the limits specified by the FCC for the use of license free ISM spectrum. The Chasqui’s microprocessor is an ATmega128 running at 8MHz. This microprocessor and support hardware enables the use of Berkeley’s TinyOS for firmware coding. TinyOS is a programming tool widely used by academic institutions for research in the area of sensor networks. The Chasqui node uses a highly efficient switching power supply that generates 3.5V for the microprocessor, 5V for the radio, and 12V for the sensor. The power supply can work with an input voltage between 4V and 7V allowing extended operation on battery power. The sensor interface allows the connection of off-the-shelf sensors to the Chasqui Node. MOSFET switches enable the microprocessor to turn off the sensors when not needed. The Gnodes are the main control nodes in CSOnet. These are Linux embedded single board computers. These nodes collect data from Inodes and Rnodes and then use that data to adjust Smart Valves to meet changing conditions. To perform this, an actuator interface was designed providing two Pulse Width Modulated outputs with 1A capacity and two 4mA-to-20mA current outputs. In addition to controlling Smart Valves, Gnodes are responsible for keeping all of the nodes in CSOnet synchronized. Synchronization ensures that all of the nodes are awake and operating at the same time, allowing for real-time communication while conserving power through a hibernation cycle. Gnodes also contain PC card slot and an Ethernet connection. The PC card allows, for example, the use of a cellular card to connect to the Internet via a wireless cellular connection to exchange information. The wireless connection allows the Gnodes to post current conditions of the system and the Smart Valves on the Internet for the WWTP. These functions require considerable power, which requires the Gnodes to be linked into the AC power supply for the Smart Valves. Because they have a continual source of power, Gnodes do not undergo a power conservation cycle. The communication structure is a core part of CSOnet. It enables the use of low power radios powered by batteries to cover large distances by multi-hopping connections. Data is disseminated by means of an advanced routing algorithm called Persistent Stateless Gradient-Based Routing. This algorithm was developed by EmNet LLC and it enables the network to maintain connectivity in spite of poor node-to-node reception while requiring low computational power. The result is robust data communication over the network. The Inodes are usually placed within manholes and they communicate up to street level. Initial tests showed that the Chasqui radio could only broadcast about 10 meters outside of a manhole. This distance could be greatly enhanced if the manhole is converted into an antenna. The design of the manhole antenna was done by Dr. W. Chappell (Purdue University). With this new antenna design the broadcast distance increased to over 75 meters if the receiving node was placed 4.5 meters above the ground. The Ireland-Miami Pilot CSOnet Pilot Site Background A pilot CSOnet was deployed and implemented in the CSO 22 service area in South Bend, IN. South Bend currently has 36 CSO locations and a CSS that spans 50.1 km2 (Greeley and Hansen 2003). Each year, South Bend receives an average of 91.71 cm of rainfall from an average of 122 storms. A majority of the storms are rather small, resulting from the climatic impact of nearby Lake Michigan (Greeley and Hansen 1994). The CSO 22 service area spans 15.3 km2 (see Figure 4a) and is responsible for 17% of the city’s total overflow volume (Greeley and Hansen 1994). Due to its vast size and the relatively small size of the interceptor line at that location, an overflow will occur at the CSO 22 control structure if more than 2.5mm of rain falls in less than 7 hrs (Greeley and Hansen 2003). The diameter of the trunkline at the outfall is 2.3 m (90 in), and the weir height is set at 1.42 m (56 in) above the trunkline invert. Within the CSO 22 service area, there is a 1.09 km2 section (65% commercial and 35% residential) of separated sewers. The sanitary sewers are directly connected to the CSS, but the storm sewers empty into a 47,540,000L retention basin, which then drains into the CSS. Before CSOnet, the basin outflow was controlled by a 25 cm outlet pipe and a manual valve, which had been stuck half-open for several years. Even prior to the valve being stuck, the basin was shown to be ineffective at storing rainwater runoff during most storm events. The City of South Bend replaced the manual valve with an actuated valve, which served as the prototype of the Smart Valve. CSOnet Components This pilot CSOnet consists of 1 Gnode, 7 Rnodes, and 3 Inodes (see Figure 4b). The Gnode is directly connected to the Smart Valve at the basin, drawing its power from the same source as the valve. It is mounted to a nearby antenna pole, and from this position, the Gnode is able to have direct RF communication with the surrounding nodes and connect to the Internet via a cellular connection. After every time step, the Gnode uploads the data from the Inodes and the valve position via the Internet to a specified website for the network administrator. Two Inodes are deployed in the basin itself, and both use pressure transducers to determine the depth in the basin. The reason for this Inode redundancy is to further minimize the risk of flooding in the areas surrounding the basin by providing a failsafe. Both Inodes communicate directly with the Gnode via RF radios. Should the two depth measurements ever differ beyond a given threshold, the Gnode selects the higher measurement and notifies the administrator. The last Inode is deployed at the CSO 22 outfall, approximately 5.3 km away from the Gnode. It monitors the depth of flow at the outfall using an existing level sensor. Because distance between this Inode and the Gnode is too great for direct communication, the Inode communicates with the Gnode via a series of Rnodes. The Rnodes are mounted about 6m above ground on traffic signals along main roads, which allows for very clear lines-of-sight. These clear lines-of-sight not only allow the Rnodes to be placed farther apart, but they also enable a signal to make two hops at once should one Rnode fail. When the outfall Inode tranmits a message, it is then relayed by the Rnodes downgradient until the Gnode receives the message. Control Scheme The overall goal of this pilot CSOnet is to maximize the storage ability of the basin, thereby minimizing the amount of overflow at the outfall. Once the runoff is stored in this basin, it must then be drained as quickly as possible once there is no longer a threat of a CSO event in preparation for the next storm event and to prevent anoxic conditions from occurring in the basin (USEPA 1999). CSOnet must also ensure that depth in the basin does not exceed a predetermined level, which could result in the flooding of the surrounding area. Should the depth in the basin ever reach this level, the Gnode would begin draining the basin even though such an action could result in a CSO event. A permitted overflow is deemed preferable to flooding or property damage in the area surrounding the basin. Under normal (non-flooding) conditions, the Gnode begins releasing water into the CSS at a constant rate of 56 L/s once the depth at the outfall falls below 0.76m. A constant, controlled discharge rate minimizes the impact of the additional flow on the CSS and downstream structures (USEPA 1999) and allows the Gnode to predict the effect of the additional flow on the flow depth at the outfall. To keep this rate constant, the Gnode must continue opening the valve further throughout the basin drainage process to account for the decreasing head in the basin. If the depth at the outfall exceeds 1.02m, the Gnode closes the valve until the depth at the basin falls to safe levels again. This usually happens when a rain event begins while the basin is draining. Once the basin is empty, the Gnode closes the gate in preparation for the next storm event. Each morning (if a storm event is not occurring), the Gnode opens and closes the gate as part of a systems check. Results and Discussion This pilot CSOnet was deployed during the summer of 2005 and has functioned accurately during a number of storm events. Each time, water was stored and discharged at the appropriate time in a controlled manner. The total cost of implementing CSOnet, including the purchase and installation of a new actuated valve, was approximately 55% less (US$26,000 compared to US$58,000) than the estimated cost of providing the same control using a more traditional Programmable Logic Controller. The long term maintenance cost of CSOnet has not been able to be determined yet, but it is expected to be less than or equal to that of existing, more traditional solutions. Figure 5 demonstrates how CSOnet functions during a typical storm. The data shown is from a storm that occurred in South Bend on September 22, 2005, in which CSOnet was responsible for storing 1,890,000L of runoff. However, a better illustration of CSOnet’s storage improvement when compared with the passively controlled system occurred during a November 1, 2005 storm. During this storm event, 2.01 cm of rain fell during the span of 9.5 hours. In the CSOnet-controlled basin, the depth in the basin reached 1.38m (see Figure 6), resulting in the storage of 6,020,000L of runoff. Moreover, no runoff entered the CSS while a CSO event was occurring. A computer simulation was then run in which the same storm hit this basin without CSOnet. In this scenario, the depth in the basin only reached 0.77m, for a total storage of 2,870,000L. CSOnet increased the storage ability of the basin by 3,160,000L, or 110%. Furthermore, of the 3,160,000L that the passively controlled basin released into the CSS, 3,060,000L was released while a CSO event was occurring, causing an equal amount of combined wastewater to overflow at the outfall. CSOnet prevented this additional overflow. The City of South Bend estimates that a liter of long-term storage potential is worth US$0.80. With this in mind, CSOnet’s ability to improve a basin’s storage potential more than pays for itself. Interceptor Optimization Description Most sewer systems are composed by a number of basins and one or a few interceptors. Each basin serves a specific area of the city. The flows in each basin are aggregated into bigger pipes that connect each other in a tree structure. Typically, the aggregated flow terminates in a single large trunk pipe that connects to the interceptor line. The interceptor line collects the flows of all the basins as it runs towards the wastewater treatment plant (WWTP). In the specific case of South Bend, the interceptor pipe runs along the St. Joseph River. South Bend has 36 CSO outfalls, located at the points where each basin connects to the interceptor (see Figure 7). The outfall structure is usually simple, consisting of a weir structure and a throttle line that connects the basin with the interceptor. The throttle line and weir maximum level have been designed so to allow a fixed maximum flow into the interceptor line. If the flow is greater than this maximum flow per basin, the hydraulic head level at the weir will be higher than the weir itself causing a CSO overflow event. Since all the weir levels and throttle line diameters are fixed, they had to be designed assuming the worst case scenario of all CSO points at maximum flow. That is, rain is assumed to fall uniformly over the entire city exactly at the same time. This is seldom the case. Typically, rain storms sweep the city and therefore the different service areas are affected at different times. Not only is precipitation time varying, but it is also non- uniform. That is, the intensity of the storm varies greatly depending on the area. As a result, extra capacity in the interceptor line is available for those basins more affected by the storm event. The city has identified that the first step in reducing CSO occurrences is to balance the load into the interceptor. For example, in March 2005 a heavy rain event that greatly affected the south part of South Bend generated a flow that was slightly higher than the threshold value for a period of 2 days in CSO22 (the storm event had duration of 4 hours). Even though the interceptor line had capacity to convey the excess flow (the rain event had stopped several hours earlier), CSO22 was still discharging into the river. If the sensors downstream from CSO22 had determined that the interceptor had extra capacity, CSOnet would have allowed the extra flow from CSO22 to enter the interceptor instead of releasing it into the river. Currently, only 5 outfalls have some kind of monitoring device to measure the amount of CSO flows directed to the St. Joseph River. A more effective system than the current weir diversion system is to dynamically adjust the flow into the interceptor at each outfall using CSOnet so that the flow in the interceptor is optimized (i.e., when the flow in the interceptor is equal to the maximum capacity of the WWTP). For such a system to be utilized, each throttle line must be replaced with a larger line fitted with a Smart Valve. A Gnode controls each Smart Valve and gathers flow depth data from the outfall and the interceptor (see Figure 8). It then uses this data, as well as data from other Gnodes, to determine the existing capacity in the interceptor line and then how much flow should enter the interceptor at that outfall to optimize the overall interceptor performance. At this point, CSOnet sets the maximum depth at each manhole equal to the highest crown height of the pipes that enter the manhole. Soon, CSOnet will be able to adjust this maximum height (i.e., allowing for surcharge) if this is deemed advantageous and safe. Currently, the goal of control algorithm is only to minimize the total amount of overflow during a given storm, without taking into account where the overflows occur. However, CSOnet does possess the ability to control where the overflows occur for most storms, should that be necessary to minimize the environmental impact of the CSO events. Results and Discussion A series of computer simulations have been run using the South Bend CSS to determine the impact of CSOnet on minimizing the amount of overflow. Table 2 shows a list of the South Bend design storms used in this study. During the simulations, it was assumed that the storms moved across the city from west to east at 33 km/hr. TABLE 2. Descriptions of the South Bend design storms used in this study Storm Total Rainfall Storm Percentage of Storms with Total Number (cm) Duration (hr) Rainfall Equal to or Less(%) 1 0.61 11 65 2 1.24 11 80 3 2.03 13 90 The results for some key simulations are shown in Table 3. In addition to decreasing the amount of overflow by 84%, CSOnet also reduced the number of CS0 events from 13 (which would occur with the existing system) to 1 in Simulation A. In Simulation B, CSOnet reduced the number of CSO events from 11 to 1. CSOnet is also very effective for larger storms, decreasing the amount of overflow by nearly half and preventing 21,690,000L of untreated combined wastewater from overflowing for a storm in the 90th percentile (Simulation C), and storms that affect the entire city (as in Simulation D). This study focused on the impact of CSOnet on larger storms, and based on this data, it is reasonable to assume that CSOnet will virtually eliminate CSO events for smaller storms. TABLE 3. Results for the computer simulations. Existing Controlled Overflow System System Volume Overflow Simulation Storm Description Overflow Overflow Decrease Decrease Designation 6 6 6 (%) (L x 10 ) (L x 10 ) (L x 10 ) Storm 1 falls on A areas north and 4.43 0.70 3.732 84 west of river Storm 2 falls on the B western half of the 18.28 7.32 10.96 60 city Storm 3 falls on the C 47.21 25.52 21.69 46 western half of city Storm 2 falls on the western half of the D city and Storm 1 40.63 29.39 11.24 28 falls on the eastern half of the city In addition to reducing the amount of overflow, CSOnet also fulfills five of the EPA’s Nine Minimum Controls. In the process of maximizing flow to the WWTP for treatment, CSOnet monitors each outfall to effectively characterize CSO impacts and the efficacy of CSO controls. This also enables the City to properly and accurately inform the public of CSO events. CSOnet automatically performs preventative maintenance through the self-flushing effect of daily Smart Valve tests. Lastly, CSOnet effectively prevents dry weather overflows by automatically detecting and compensating for unexpected additional dry weather flows and by preventing the throttle lines from clogging. Although interceptor optimization alone may not fulfill all of the EPA’s standards (i.e., decreasing the amount of overflow by 85%) for most municipalities, it can be an integral part of any Long Term Control Plan. In addition to interceptor optimization, CSOnet can be used to maximize the storage capability of in-line and off-line storage areas, thereby minimizing the flow in the trunklines. With the data CSOnet provides, the municipality is also able to determine where additional storage must be added or where sewer separation must occur with greater certainty, eliminating any unnecessary construction and minimizing CSO abatement costs. Conclusion EmNet, LLC, the City of South Bend, the University of Notre Dame, and Purdue University have developed an embedded sensor network solution to the CSO problem called CSOnet. CSOnet was proven to be an effective, robust, and cost- effective RTC system during the pilot demonstration, which increased the storage ability of a retention basin by up to 110% and cost 55% less than conventional solutions. In a series of computer simulations, CSOnet was able to reduce the amount of overflow by up to 84% through interceptor optimization and significantly reduce the number of CSO events. The implementation of CSOnet at each outfall also fulfills five of the NMCs, including the prevention of dry weather overflows. CSOnet is designed to dovetail into any existing infrastructure and to work in conjunction with other abatement measures to fulfill all of the EPA’s CSO standards in an effective, timely, and inexpensive manner. Acknowledgements We wish to thank the Indiana 21st Century Fund, which funded the research and development of CSOnet. We also wish to thank the City of South Bend, IN, in particular Mayor Steve Luecke, Gary Gilot, Jack Dillon, and Patrick Henthorn, for their continual support and assistance. We thank Drs. Jeffrey Talley and Michael Lemmon of the University of Notre Dame and Drs. William Chappell and Saurabh Bagchi for their technical contributions. Finally, we thank Greeley & Hansen, LLC for providing us with the trunkline flow data for the design storms used in the simulations. Works Cited Colas, H., Jolicoeur, N., Pleau, M., Marcoux, C. E., Fields, R., andStinson, M. (2001). The choice of a real time control strategy for combined sewer overflow control.” Proceedings of Novatech'2001, 4th International Conference on Innovative Technologies, Lyon-villeurbanne, France, June 25-27, 2001. Greeley and Hansen, LLC. (1994). Combined sewer overflow control study, South Bend Department of Public Works, Division of Environmental Services, South Bend, IN. Greeley and Hansen, LLC. (2003). Stream reach characterization and evaluation report, South Bend Department of Public Works, Division of Environmental Services, South Bend, IN. Heinz, S., and Schultz, N. (2006). “Milwaukee case study in example evolution of sewer controls.” World Environmental and Water Resources Congress, Omaha, NE, May 21-25, 2006. Lawson-Fisher Associates P.C. (2003). Stormwater management master plan, South Bend Department of Public Works, Division of Environmental Services, South Bend, IN. Pleau, M., Colas, H., Lavallee, P., Pelletier, G., and Bonin, R. (2005). “Global optimal real-time control of the Quebec urban drainage system.” Env. Modeling and Software, 20(4), 401-413. Ruggaber, T. P., Talley, J. W., and Montestruque, L. A. (2006). “Using embedded sensor networks to monitor, control, and reduce CSO events: A pilot study.” Environmental Engineering Science. Accepted. United States Environmental Protection Agency (USEPA). (1999). Combined sewer overflow technology fact sheet: Retention basins, Office of Water, Washington, D.C. United States Environmental Protection Agency (USEPA). (2004). Report to congress on impacts and control of combined sewer overflows and sanitary sewer overflows, Office of Water, Washington, D.C. Figure 1. CSOnet uses a widely distributed embedded network with specific control points to control the CSS. CSOnet Decentralized Technology SCADA-Based Centralized Technology Multi-hop No communication Robust Centralized Dependent on comm. Robust mesh network infrastructure decentalized network infrastructure decentalized control control Multi-hop Large central mesh network Dense computer Model to Low power temporal Long range compensate for radio spatial radio SCADA lack of field Low cost Expensive feedback RTU data hardware hardware Battery Low cost Large External power Expensive Small operated installation deployments supply installation deployments Figure 2. Technology implications of the decentralized CSOnet control system and the centralized SCADA-based control system. Figure 3. CSOnet components (a) (b) Figure 4. CSOnet pilot deployment September 22, 2005 Storm Event Results Depth at outfall Flow in trunkline (MGD) (from two sensors) 1 2 3 4 Depth in basin (ft) Valve position (%) Rainfall (in) Figure 5. Results from a typical storm event in the CSO 22 service area. In box 1, the Gnode opens the valve to release water stored during a prior storm. In box 2, a storm event began during this draining, raining 0.41 cm in one hour. This storm caused the flow depth at the outfall to increase. The Inode at the outfall signaled the Gnode of this increase, and the Gnode closed the valve before the CSO event began. In box 3, the storm event ended, and then the CSO event ended. The Gnode then began releasing the stored water at a constant rate of 56 L/s, gradually opening the valve further to account for the decreased head in the basin. In box 4, the Gnode opens and closes the gate as part of a system check. Figure 6. Comparison of basin depth vs. time for a CSOnet controlled basin and passively controlled basin. Figure 7. South Bend’s CSO basins and outfalls. GNode Cabinet or Traffic Signal Manhole Cover Antenna Conduit INode Overflow Stilling Line Actuated Valve Well Stilling Combined Well Enlarged Weir Sewer Throttle Line Trunkline Sensor Sensor Interceptor Line Figure 8. Configuration of a CSOnet controlled outfall.
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