On-Line Computer Models. Off-Line Computer Models. Spatial Visualization of Network Components. Supervisory Control and Data Acquisition (SCADA)

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1 PROJECT DESCRIPTION The United States Department of Homeland Security (DHS) has established 18 sectors of infrastructure and resource areas that comprise a network of critical physical, cyber, and human assets. One of these sectors is the Water Sector. The Water Sector Research and Development Working Group has stated that water utilities would benefit from a clearer and more consistent understanding of their system flow dynamics. Understanding flow dynamics is important to interpreting water quality measurements and to inform basic operational decision making of the water utility. Such capabilities are critical for utilities to be able to identify when a possible attack has occurred as well as knowing how to respond in the event of such an attack. As a result, DHS contracted with the National Institute for Hometown Security (NIHS) to administer a project to address this identified need. NIHS subsequently contracted with the Kentucky Water Resources Research Institute (KWRRI) to perform the research. The research team assembled by the KWRRI included faculty and staff from the University of Kentucky, the University of Missouri, the University of Cincinnati, and Western Kentucky University. This research project was implemented to better understand the impact of water distribution system flow dynamics in addressing such issues. In particular, the project: (1) tested the efficacy and resiliency of the real-time hydraulic/water quality model using stored SCADA data in order to understand the potential accuracy of such models, and understand the relationship between observed water quality changes and network flow dynamics, and (2) developed a toolkit for use by water utilities to select the appropriate level of operational tools in support of their operational needs. The toolkit was developed with the following functionality: (a) a graphical flow dynamic model, (b) guidance with regard to SCADA systems including sensor placement, and (c) guidance with regard to the appropriate level of technology needed to support operational needs of the utility. The results of the research may be accessed either through The Water Distribution System Operational Decision Support Website (a self directed decision tree) or through the Water Distribution System Operational Toolkit (an expert system developed to guide the user through a series of operational questions in support of their system operations). 1

2 THE WATER DISTRIBUTION SYSTEM OPERATIONAL DECEISION SUPPORT WEBSITE The Water Distribution System Operational Decision Support Website has been developed to assist water utilities in designing a monitoring/control system for their water distribution system that will provide water distribution system data (WDSD) for use in support of various system operations. Such data could include both general operational data as determined from either real time telemetry or offline computer models, or on-line data (including data from both hydraulic and water quality sensors). Operational applications could include 1) normal operations, 2) emergency response management, 3) water quality management, 4) energy management and 5) event detection. Operational support for water distribution systems can be obtained by utilizing a series of different hardware and software components. These can be arranged in an operational hierarchy that can be visualized in a ladder of components, in which each rung on the ladder will be dependent upon the previous rung. The four basic rungs are illustrated in Figure 1 below. These include 1) a Supervisory Control and Data Acquisition System (SCADA), 2) spatial visualization of network components, 3) an off-line computer model of the water distribution system, 4) an on-line computer model of the water distribution system. Additional information in support of these four operational components can be obtained by clicking on the associated box below. On-Line Computer Models Off-Line Computer Models Spatial Visualization of Network Components Supervisory Control and Data Acquisition (SCADA) Figure 1. Hierarchy of Operational Components for a Water Distribution System [Christie - Please flip the order in figure 1, with the arrows going down] 2

3 SCADA SYSTEMS A water distribution system is made up of many different operational components, including sensors, meters, pumps, and control valves, that can be monitored or controlled onsite or from a central location. In the past, these operations were normally accomplished through the use of onsite instrument and control panels. These panels typically consist of a series of electro mechanical devices such as indicators, push buttons, lights switches, relays and analog control instruments. In recent years many utilities have made the transition to computerized SCADA systems in which commands are entered through a keyboard, mouse, or touch screen instead of through the use of switches or push buttons. Supervisory Control Schemes SCADA systems are usually built around a central computer and operator s station, which communicate with intelligent Remote Terminal Units (RTUs) or Programmable Logic Controllers (PLCs) via an integrated communication network. The exact composition of the system will be dependent upon the selected control scheme. Two different control schemes are available: hierarchical control or distributed control (Clingenpeel and Rice, 1990). Hierarchical Control In a hierarchical control environment there is normally a single control computer which is linked to several remote control units via the communication network. RTUs are normally used for the remote control units. Each RTU contains a point database for all field input/output (I/O) points and calculated values like flow totals, tank volumes, etc. The central computer contains a mirror image database which includes points for all RTUs in the system. Under normal operation the RTU continuously scans all input points and updates the calculated values. The central computer is then used to poll each RTU to update the central database. Any control decisions are then sent back to the selected RTU for control of a particular network component (Riddle, 1989). Distributed Control In a distributed control environment more of the control is distributed to the remote stations. Although a distributed control system can also contain a central host computer, it is typically used more to manage and monitor the control units as opposed to controlling the actual field instrumentation. PLCs are normally used as the control units for this type of system. Since the PLCs are connected to a central host computer, the computer can still be used to monitor all the control data. However, in this case the central computer can be used to change selected set points of the associated remote control units or even download complete control schemes (Christie, 1989). Advantages and Disadvantages The principal advantage of the hierarchical approach as opposed to the distributed approach is the lower cost of the system. This is primarily due to the cost of the RTUs as compared to the PLCs. The chief disadvantage of the hierarchical approach is the inability of the system to operate in the event of a failure of the communication network. Other problems can arise due to differences in the telemetry data transmission rate and the computer scan rate (Clingenpeel and Rice, 1990). 3

4 The primary advantage of the distributed approach is that normal operations can still be maintained despite a loss of the telemetry link or even the central computer. In addition, since the control hardware is located at the control point, potential differences in the transmission and scan rates can be more readily minimized (Clingenpeel and Rice, 1990). Potential SCADA Uses A SCADA system is a widely distributed computerized system primarily used to remotely control and monitor the condition of field-based assets from a central location. Field-based assets include wells, pump stations, valves, treatment plants, tanks, and reservoirs (Bentley, 2004). Generic uses of SCADA in distribution systems include: Security monitoring Energy management Monitor equipment operations to forecast maintenance, repair, and replacement Sub-metering utility usage Identifying alarm conditions For water distribution, the operational and managerial uses of a SCADA system include the following: Monitor the system Exercise control over the system and ensure that required performance is continuously achieved Reduce operational staffing levels through automation or by operating the system from a central location Monitor and store data of a system s behavior, and use the data to achieve full compliance with regulatory reporting requirements Obtain information on the performance of the system and establish effective asset management procedures for the system Establish efficient operation of the system by minimizing the need for routine visits to remote sites Potentially reduce power consumption during pumping operations through operational optimization Provide a control system that will enable operating objectives to be set and achieved Provide an alarm system that will allow faults to be diagnosed from a central location, thus allowing field repair trips to be made by suitably qualified staff to correct the given fault condition and to avoid incidents that may be damaging to the environment. Monitor system operations to identify excursions of operating equipment from normal operating conditions/ranges Monitor equipment operations to forecast maintenance, repair and replacement. Use SCADA data to verify hydraulic and water quality models. Use SCADA data to identify intrusions, leakages, and other variations from normal system, operations. Use of SCADA in support of real-time modeling SC ADA Survey As part of this research effort, 22 utilities were surveyed with regard to their use of SCADA. Additional information about the survey can be obtained below: 4

5 SCADA Survey Report [27.1pdf] Appendix A: SCADA Survey Questions [27.2pdf] Appendix B: SCADA Survey Results [27.3pdf] SCADA Functions SCADA Function categories are: 1. Data Acquisition (Collection) 2. Data Communication (Monitoring) 3. Data Presentation 4. Equipment Control Note that these SCADA function categories are in a specific order; in other words the second, third and fourth functions all build on the prior functions. The first three SCADA function categories deal with data acquisition. Many water systems, especially smaller systems, do not elect to use supervisory control aspects the SCADA in the daily management of their system. All SCADA systems must have data acquisition and communication before any supervisory controls can be implemented; therefore, many industry professionals believe that the DA of SCADA, or data acquisition portion of SCADA, is the most important part of the system. Closely tied to the data acquisition, is the communications and data logging/presentation aspects of the SCADA SCADA Components All of the SCADA Functions are carried out by SCADA Equipment. There are four SCADA equipment categories that are illustrated in Figure 2. Sensors/Controls RTUs, PLCs or IEDs Communications Network SCADA Master Water Levels Hydraulic Pressure Motor Speed/Temp Pump Station Ambient Humidity / Temperature Remote Terminal Unit (RTU), Programmable Logic Controller (PLC) or Intelligent End Device (IED) SCADA Master with User Interface Motion Detection Pump on/off Control Figure 2. Illustration of four SCADA Equipment Categories 5

6 1. Sensors and Controllers - Sensors (either digital or analog) and control relays directly interface with the managed system. Additional information on both types of components can be found below: [21.1a] Sensor Equipment [21.1b] Control Equipment 2. SCADA Interface Units - Most SCADA system associated with water distribution systems typically employ one or more types of SCADA interface units. These include: the remote terminal unit (RTU), the programmable logic controller (PLC), or the intelligent end device (IED). Remote Terminal Units (RTUs), Programmable Logic Controllers (PLCs), and Intelligent End Devices (IEDs) are small computerized units deployed in the field at the specific sites and locations where sensors and equipment controllers are utilized. RTUs, PLCs, and IEDs serve as local collection points for gathering status from sensors and delivering commands to control relays. More information on each of these components can be found below: RTUs [22.1] PLCs [22.2] IEDs [22.3] 3. Communications Network - the communication network is used to connect the control master of the SCADA system to the SCADA interface units. There are two primary types of systems to consider when designing telemetry and communication systems. In a unidirectional system, remote terminal units report data to a central location, but do not accept remote command and control instructions. Remote telemetry is truly bi-directional in nature, reporting both statistics and accepting instructions from a central computer or controller. In most cases, it is worth remembering that in a uni-directional system, terminal units only send data, the managing system requires a bi-directional link; the link itself can usually both send and receive information. Additional information [23]. 4. SCADA Master - These are larger computer consoles that serve as the central processor for the SCADA system. Master units provide a human interface to the system and automatically regulate the managed system in response to sensor inputs. Additional information [24]. SCADA System Implementation Process When considering a SCADA system, there are traditionally two methods that are utilized for implementation. These methods are: Design-Bid-Build Method [26.1] EFI (Engineer, Furnish and Install) Method [26.2] 6

7 SCADA Sensor Location One of the critical elements in designing a SCADA system is to know where to place the various sensors that will supply critical information to the system. Determining locations where either hydraulic or water quality sensors should be installed should be driven by exactly what information is needed about the distribution system. For example, if all that is needed is the water level in an elevated storage tank, then obviously placing a pressure sensor at or near the base of the tower is probably the logical choice. However, the placement choices become very complicated when the most logical option is not economical or in a physically optimal location. Remoteness and access to such facilities sometimes pose questions that are hard to answer. The use of computer modeling for sensor placement is increasing. The USEPA model Threat Ensemble Vulnerability Assessment-Sensor Placement Optimization Tool (TEVA-SPOT) is one such computer tool available to assist in sensor location decisions. Hart and Murray (2010) reported on three sensor placement strategies that are being applied for deployment of a contaminant warning system (CWS): expert opinion, ranking methods, and optimization (computer modeling). Berry, et al, (2005) reports that tests of the use of sensor placement modeling and the decisions made by local experts in a water supply system suggest that a collaboration between modelers and those with practical water system expertise can improve the effectiveness of sensor placement decisions. Programs like TEVA-SPOT can assist in finding optimal locations for each sensor in a distribution system for use as a component of a drinking water system, while the operators and/or managers need to decide which among the potential alternative locations are optimal to place the sensor for their purposes. The placement choices can become very complicated if the purpose of the sensor is to determine model inputs for a hydraulic model, or if the goal is to monitor for maliciously injected contaminants, which is termed a contaminant warning system (CWS) or detect pipe bursts and other leakage. (Mounce, S. et. al., 2003 and 2006), (Berry, J. et. al., 2005) Hydraulic sensors are very useful for modeling water quality in a distribution system, by monitoring flows, and thus residence times in the system. This use of hydraulic data also has direct application to regulatory compliance. Berry, J, et al (2005) identified placement cost budget, contamination public health impacts, and potential attack scenarios as considerations in sensor placement. The Public Health Security and Bioterrorism Preparedness and Response Act of 2002 identified physical security of water supply distribution systems as a major priority for water utilities. So, in addition to combining hydraulic and water quality sensors in the same location, consideration of the value of data from these sensors at locations of security sensing (e.g. water storage towers, basins, and pump stations) may be worthwhile. SCADA sensor placement decisions made without the use of computer models are frequently based on the experience of the decision-maker and his/her support group s experiences. Following is a list of sensor placement design issues provided in a sequence used by the authors (Mounce, S. et. al., 2003 and 2006), (Berry, J. et. al., 2005) in the design of SCADA systems. SCADA Sensor Placement Decision-Making Sequence 1. Intended uses of data A. hydraulic and water quality monitoring B. security monitoring C. energy management D. equipment management including repair and replacement forecasting E. sub-metering utility usage F. identifying alarm conditions 7

8 G. verifying hydraulic or water quality modeling H. provide data as a component of a CWS 2. Parameters of interest to be monitored 3. Locations/areas for which data are desired A. areas or location of major public health impacts from contamination (schools, health care facilities, food preparation, elderly living centers, etc.) B. potential contamination attack scenarios and locations based on physical characteristics of distribution system C. areas of known poor water quality in distribution system based on complaints D. locations of regulatory agency required compliance sampling 4. Locations of other types of sensors currently in distribution system 5. Potential locations of other (security, water quality, etc.) sensors to be added to the system 6. Potential locations based on the above 7. Security & accessibility of potential sensor sites 8. Cost components (relative costs) by location 9. Sensor station design & construction budget available 10. Final sensor placement locations If a water system SCADA is being planned, sensor placement computer programs (e.g., TEVA SPOT) are available to optimize the locations of the sensors to minimize the impacts of a contamination incident. To use one of these programs, complete, accurate and calibrated hydraulic and water quality models of the distribution system are necessary to provide inputs to the sensor placement program. Following is a list of the information/data needed from the system operators and/or managers for input to these programs. SCADA CWS Sensor Placement Optimization Program Inputs 1. Complete and calibrated hydraulic and water quality distribution system models 2. Simulation time the length of time (number of hours) the program run should simulate 3. Time of release the length of time a contaminant is injected into the distribution system 4. Mass injection rate (mg/min) the amount of contaminant that is injected into the distribution system per unit time 5. Contaminant the contaminant(s) that may be injected into the system 6. Response time delay the time between initial detection of a contaminant in the distribution system and when public warnings are issued 7. Detection limit some level of contaminant concentration below the health impact level of an injected contaminant; this must be in the range between the upper and lower detection limits of the sensors 8. Impact metrics any of a variety of impacts can be used: the number of people ingesting the contaminant, length of distribution system piping that is contaminated, number of people with health effects from the contaminant 9. Sensor placement objective - any of a variety of objectives for the placement of sensors can be used: minimize the number of public health impacts, minimize the extent of distribution system contamination SCADA Sensor Placement Guidance As part of the current research program, general guidelines for the placement of SCADA sensors have been developed for use by small utilities. 8

9 [25.1pdf] Sensor Placement Guidelines for Small Utilities SCADA Sensor Placement Software Drinking water utilities have recognized the importance of online sensors in minimizing distribution system vulnerability to accidental or intentional contamination. Online sensors with real-time data acquisition and transmission capability can be quite expensive forcing small and medium water utilities to limit the number of sensors to just one or a few. Placement of sensors is not a trivial task, considering the limitations on number of affordable sensors and the need for limiting the spread of contamination within the distribution system. Though TEVA-SPOT (Threat Ensemble Vulnerability Assessment and Sensor Placement Optimization Tool) can provide guidance on placement of water quality sensors for water distribution networks, it requires the use of complex water quality models of distribution networks coupled with sophisticated optimization methods for efficient sensor placement guidance. Most small and medium water utilities may not have technical or financial resources required for meaningful use of TEVA-SPOT and may not be able take full advantage of water quality sensor technology and may continue to face higher vulnerability risk. KYPIPE-WQSensor is a simplified guidance tool for optimal placement of sensors on small and medium water distribution networks. The input for this tool is a good hydraulic model setup for extended period simulation (EPS). Optimal water quality sensor placement analysis (to minimize average detection time) can be performed with little or no additional input from water utilities. Using the default data built into the KYPIPE-WQSensor tool, the Pipe2012 users can determine optimal locations for water quality sensors for their extended period simulation model with just three simple key strokes! In addition to generating detailed report on sensor placement study, the optimal locations for water quality sensors will be automatically highlighted on the network model. The KYPIPE-WQSensor features have been embedded in to the Graphical Flow Model, which can be downloaded here. < Additional resources for KYPIPE-WQSensor are provided below: [25.2 pdf] Instructions for using KYPIPE-WQSensor [25.3 pdf] Comparison of performance of KYPIPE-WQSensor and TEVA-SPOT SCADA in Support of Energy Management One of the most effective uses of SCADA systems is in managing the energy usage of the water pumping and distribution systems. For additional ways to use SCADA data in improving the energy efficiency of such systems, check out the additional information below: [28] Improving Energy Efficiency of Water Distribution Systems 9

10 SPATIAL VISUALIZATION OF NETWORK COMPONENTS It has become possible for many small utilities to develop or utilize hydraulic network models in their day-to-day planning and operational activities. In addition to determining pressures and flows within particular systems, such models can be used to address the issues of fire-flow capacity, system reliability, rehabilitation scheduling, emergency response, and energy management. In most cases, the costs associated with network modeling will be more than offset by better operation and management decisions that will result from a more comprehensive understanding of the network system. Despite such advantages, many utilities do not feel like they have the technical background, staff or budget to develop and use a water distribution system computer model on a regular basis. As a result, an intermediate tool, called the Graphical Flow Model has been developed to help them get started up this ladder of improved operational control. Graphical Flow Model The Graphical Flow Model (GFM) has been developed for water utility managers as a first step toward utilizing a computer model of their distribution system. The GFM is not a comprehensive water system model, modeling all possible aspects of operations; however, for systems that have little to no computer based representations of their system, the GFM is a great asset. First, it provides a graphical representation of a system within an interactive interface, capable of storing and manipulating data pertaining to many components of the system. Secondly, given certain hydraulic operational control inputs, the GFM is capable of returning output for many important system questions such as flow directions and magnitudes, pressures, hydraulic grade line contours, and more. The GFM has been designed to allow the use of pre-developed geographic information system (GIS) datasets for use in constructing graphical representations of water distribution systems. In particular, the model has been developed to allow use of the Kentucky Infrastructure Authority (KIA) water distribution system database. Finally, the GFM has an option that will allow the user to export any model developed within the GFM graphical user interface (GUI) for possible subsequent use with comprehensively functional water distribution network analysis software (i.e. KYPIPE). Data Sources One of the unique challenges of developing computer models for small utilities is the ability to generate and assemble the required basic network data. The Kentucky Infrastructure Authority has assembled extensive GIS data sets for all of the water distribution systems within the state ( This information includes data on pipelines, water tanks, water treatment plants, water meters, and pump stations. The GFM model includes functionality to upload such data via a graphical user interface to permit the creation of a schematic of the water distribution system. The existing infrastructure can be displayed in graphical form superimposed on a topographic map. The topographic map can display the physical relationships that the existing system infrastructure has with its surroundings in terms of elevations and natural and urban geographical features. 10

11 Downloading and installing the Graphical Flow Model The Graphical Flow Model and associated user's manual can be accessed below. The user should first download the user's manual and read the appendix that describes how to download the computer program to the user's computer. Graphical Flow User's Manual < Graphical Flow Model < Downloading and installing the Network Decontamination Model In addition to the Graphical Flow Model, a Network Decontamination Model has also been developed for use by water utilities. This model can be used to help determine which valves should be shut to isolate part of the water distribution system in response to a pipe break or a contamination event. The program has been developed to use the same data files generated using the Graphical Flow Model. The user should first download the user's manual and read the appendix that describes how to download the computer program to the user's computer. Network Decontamination Model User's Manual < Network Decontamination Model < 11

12 OFF-LINE COMPUTER MODELS Computer models for use in the analysis and design of water distribution systems have been available since the mid 1960's. Such models are typically composed of three parts: a database (composed of both graphical and physical data), a computer program (which solves both hydraulic and water quality equations), and a graphical user interface (see Figure 3). Distribution Network Graphical User Interface Graphical Database Physical Database Network Simulation Program Water Quality Program Database Computer Program Figure 3. Network Computer Model The physical database will contain will contain information that describes the physical infrastructure of the network, system demands, and the operational characteristics of the system. The actual physical characteristics of the network are typically represented in the computer using a node-link representation, where each pipe segment is represented by a line that is joined at both ends by a node (Figure 4). Observed or assumed water demands along the pipe line are normally averaged and then assigned to the nodes (see Figure 5). Additional components, such as tanks, valves and pumps are then represented in the model by treating them either as a special node or pipe element. The graphical database will contain information on the spatial location of the physical components of the system (see Figure 6). The Graphical Flow Model has been especially designed to take advantage of the Kentucky Infrastructure Authority graphical and physical databases for use in building a water distribution system model. 12

13 Figure 4. Pipe (link), Junction (node) Conceptualization Figure 5. Demand Load Simplification 13

14 Most network computer programs today, include both a hydraulic model and a water quality model. The hydraulic computer program solves a set of equations that are used to determine the flows in each of the pipes, the pressures at each of the nodes, and the operational status of various components in the system (e.g. tanks, valves, pumps, etc.). The water quality computer program, uses the results of the hydraulic analysis to solve an additional set of equations that are used to determine the movement and concentration of selected water quality parameters (e.g. chlorine) throughout the system. Network Analysis Figure 6. Example of Graphical User Interface (i.e. KYPIPE) The application of a network model in the analysis of a water distribution system will normally involve three basic steps: 1) model development, 2) model calibration, and 3) model applications. Each of these steps is discussed in detail in the AWWA Manual M32 (AWWA, 2005). A brief summary of each step is provided below: Model Development Developing a network model will normally involve three basic steps: 1) Data gathering 2) Map development and 3) Computer coding. In developing a network model two different kinds of data must be collected:1) Physical data and 2)Operational data. Physical data includes such things as geometric data (i.e. node-pipe linkage), pipe and node data, and initial estimates for pipe roughness and nodal demands. Operational data includes such things as the daily pump operating schedule, storage usage, 14

15 and valve operations. The physical and operational data required for the development of a network model can normally be obtained from maps of the water system, as-built drawings, operational records, and conversations with plant operators and city or utility engineers. To a certain degree the exact data requirements for a particular computer model will be somewhat dependent upon the options of the program. In general, all programs require a junction label, elevation, and water demand value for each junction node. Similarly, most programs will require a pipe label, beginning and ending junction node labels, pipe length, diameter, and roughness of each pipe. Most programs allow the user to characterize the pipe roughness using either the Hazen-Williams equation or the Darcy-Weisbach equation. Some program also allow the user to assign a minor loss to each pipe and to characterize various types of control valves (i.e. check valves, pressure regulating valves, pressure sustaining valves, etc.). Pumps may be simulated using a network model in a variety of ways. For the situation involving a source with a constant head pump, the pump may be simulated as a constant tank. For the situation involving a source with a constant flow pump, the pump may be simulated by a junction node with negative demand. Variable head pumps can either be simulated using a constant head characterization or by mathematically approximating the pump characteristics curve using some type of curve fit. Some models have a pump curve fit option as a program feature. The required network data are normally recorded on a working map of the system. In order to develop a working map, a complete system is first developed. The system map is typically at a scale of 1:1000 and includes street names and topographic contours (Walski 1984). A skeletal map may then be developed by eliminating pipes of minor influence. Guidelines for skeletonization are provided in the AWWA Manual M32 (1989). In general, small lines which are perpendicular to major trunk lines may be considered for removal. However, all small lines may be necessary if they are near a large supply point ( such as a tank) or a large demand center or if they provide a connection between major mains. A working map may be constructed by overlaying the skeletal map with a sheet of tracing paper or mylar and creating the node -pipe linkage required by the computer program. Each junction node and pipe are then numbered and the data associated with each component added to the map. In adding the additional descriptive data to the map it is best to use some type of symbolic index so that numbers associated with different types of data can be identified. Model Calibration It is imperative that a network model be calibrated prior to use. A detailed discussion of model calibration is provided in the AWWA M32 Manual (1989) and by Walski (1984). Model calibration involves the adjustment and/or modification of the mathematical model so that the predicted model results match previously observed conditions. The steps required to properly calibrate a network model are: 1) Develop the network model, 2) Initialize the model parameters, 3) Collect the required calibration data, 4) Perform a micro-level calibration, and if necessary 5) perform a macro-level calibration. Once the initial network model has been developed, initial estimates for the pipe roughness and nodal demands should be obtained. Although preliminary estimates of pipe roughness may be obtained from literature values, it is always best to obtain values directly from field tests of the actual system. Guidelines for field tests are provided by Walski (1984) and McEnroe et. al. (1990). 15

16 The two model parameters that are normally adjusted in model calibration are the nodal demands and the pipe roughness. In some instances it may be necessary to adjust the model boundary conditions as well (i.e. tank levels, pump heads, PRV settings etc.), however, the values associated with the boundary conditions should normally be determined explicitly from field measurements (such as from recorded charts). The spatial distribution of the nodal demands can be initialized using one of two approaches: a disaggregation approach or an aggregation approach. In the disaggregation approach the total residual demand (i.e. the total demand minus all known major users) is distributed among the junction nodes on the basis of the ratio of the service area of each node to the total service area. In the aggregation approach the residual demand is assigned based on the total number of consumption units associated with each node and the unit demand associated with each consumption unit. Once initial estimates of the pipe roughness and nodal demands are obtained, a micro-level calibration should be performed. The micro-level calibration involves adjustment of the pipe roughness values and nodal demands until the model accurately predicts flows and pressures in the system for a wide range of operating and loading conditions. In general, two different levels of calibration should be performed: a steady-state calibration followed by a dynamic calibration. In the steady-state calibration the model parameters are adjusted to simultaneously match observed flows and pressures for several different steady-state loading conditions. The observed flows and pressures are normally obtained from fire flow tests. When performing such tests, it is important to make sure that the system is sufficiently stressed to cause a significant drop in pressure; otherwise the fire flow data may be insufficient to allow for proper model calibration (Walski, 1984). The dynamic calibration involves the fine tuning of the model parameters to match observed tank level or flow meter fluctuations over a 24 hour period. Data for this level of calibration can hopefully be obtained from existing telemetry records. Alternatively the data may have to be collected directly, say, by placing a pressure recorder at the base of a tank. In general, the steady state calibration is more sensitive to adjustments in pipe roughness while the dynamic calibration tends to be more sensitive to nodal demand. A good calibration model will be one in which the predictive values are within 5-10 percent of the observed values. If the micro-level calibration is unable to produce such results, it may be that the model topology or assumed operating conditions are incorrect. In that case it may be necessary to perform a macro-level calibration. That is, it may be necessary to review the underlying assumptions of the original model to determine if any errors are present. An excellent discussion of some of the issues associated with macro-level calibration are provided by Walski (1990). In recent years, several authors have proposed techniques or algorithms for use in calibrating hydraulic network models. These include analytical algorithms (Walski 1983), the use of explicit (simulation) algorithms (Ormsbee and Wood 1986), and the use of implicit (optimization) algorithms (Ormsbee 1989 and Lansey and Basnet 1991). Despite the availability of such techniques, the trial - and - error method remains the dominant method. Model Application A calibrated computer model of the water distribution system can be used to assess the performance of the existing system and/or identify and locate system inadequacies. If system inadequacies are identified, the model can be used to evaluate the impact of any proposed design improvements. Most available computer network models may be applied in either a steady state or an extended period 16

17 simulation. In most models, the extended period simulation is automatically performed by linking or integrating a series of discrete steady state analyses. Steady state simulations provide a snapshot analysis for determining pressures and flows for specified demands, tank levels, and pump operations. Extended period simulations allow for an evaluation of the performance of the system over time. Such an analysis may be important in evaluating performance of tanks in the system in response to extended fire demands, specific pump operating policies. or a basic 24 hours diurnal demand pattern. Model Applications Water distribution computer models can be used in support of a wide range of applications. These include; Planning Capital improvement planning Conservation impact studies Water main rehabilitation evaluation Siting of future storage tanks Engineering Design Fire flow studies Valve sizing Reservoir sizing Pump station sizing Pipe sizing Calculation of pressures and flows at particular locations Pressure zone boundary identification System Operations Personnel training Troubleshooting Water loss calculations Emergency operation scenarios Source management Main flushing Energy cost management Water Quality Improvement Constituent tracking Water source/age tracking Chlorine levels Water quality monitoring location 17

18 Real Time Operations Scenario forecasting Optimal pump scheduling Event detection Model Selection Computer models for water distribution analysis have been available since the late sixties (Ormsbee, 2006). Over the last fifteen years, such models have been augmented with very sophisticated pre and post processing software including GIS interfaces and customized graphical user interfaces (GUIs). When selecting a potential computer program the buyer should consider several things. Among the more important issues are: 1) ease of data input, 2) geometric data requirements, 3) program options, 4) numerical stability and reliability, 5) graphical interface capabilities, 6) program history, 7) cost, and 8) user support. Two of the more widely used computer programs for small to medium sized utilities are EPANET and KYPIPE. EPANET was developed by researchers at the EPA Risk Management Laboratory in Cincinnati primarily as a water quality model. While EPANET has emerged as the model of choice for academicians (primarily due to its open source architecture), its application to actual water utility systems has been somewhat limited. This is due to several factors including the lack of any formal support structure or training programs. Secondly, EPA does not have a formal program to provide continual upgrades to its user interface, which hinders its sustained use in the water utility arena. Free copies of EPANET may be downloaded off the internet at the EPANET website: EPANET website < KYPIPE was originally developed at the University of Kentucky in the early seventies, making it the oldest hydraulic software that is still commercially available. Technical support for the software is provided free, making it an especially attractive software for small utilities. Free demo versions of the program are available at the KYPIPE website. For larger applications, the cost of the software is related to the size of the system. The KPIPE website is: KYPIPE website < Model Calibration In order for a water distribution model to be truly useful, it needs to accurately reflect real operating conditions. That is, it should accurately reflect current pressures and flows throughout the system, the time it takes to fill or drain a tank and the travel time from one point to another. It should also be able to predict the pressures and status of different components in the system (e.g. tank levels) at some future point in time. In order for a model to be able to do this, it needs to be first calibrated. Model calibration simply involves the process where the modeler inputs initial estimates for different model parameters such as pipe roughness, nodal demands, pump heads, etc. into the model, executes the model, and then compares the results of the model to actual field observations. If the model results and the field observations do not match, then the modeler will need to go back and adjust the model parameters, rerun the model, and then compare the model results again with the field results. This process may require several iterations until the model is properly calibrated. Specific instructions on 18

19 how to properly calibrate a hydraulic model are provided below. [41pdf] Ormsbee, L., and Lingireddy, S., (1999) Calibrating Hydraulic Network Models, Journal of the AWWA, 89 (2) pp [42pdf] U.S. EPA (2005) Water Distribution System Analysis: Field Studies, Modeling and Management (A Reference Guide for Utilities) EPA/600/R-06/028 [43pdf] Maslia, M., Sautner, J., (2005) "Use of Continuous Recording Water-Quality Monitoring Equipment for Conducting Water Distribution System Tracer Tests: The Good, the Bad, and the Ugly." Proceedings of the ASCE Water Congress [44pdf] Walski, Thomas, (1990) "Sherlock Homes meets Hardy Cross", Journal of the AWWA, pp [45pdf] Grayman, W., Morris, M., and Sautner, J., (2006) Calibrating Distribution System Models with Fire-Flow Tests, Opflow, April, pp Laboratory Model Calibration Case Study Use of computer models (both hydraulic and water quality) to aid in the operations of water distribution systems in a real-time environment requires that the models represent the actual system as accurately as possible. In the past, models have frequently incorporated basic physical assumptions and/or approximations, that while adequate for most static applications (e.g. network design, etc.), may introduce levels of errors that could prove to be significant when applied in support of real time operations and in particular in support of contamination detection. As an example, Wood et al. (1993, 1994) have shown that the use of a constant minor loss coefficient for pipe junctions and fittings may introduce a significant error in network modeling results, especially in the case of large diameter pipes with short lengths. The impact of such errors on water quality predictions is largely unknown. As part of their research, Wood et al. (1993) developed flow dependent loss relationships for several different standard fitting configurations that could be incorporated into most standard hydraulic software. In addition to explicit hydraulic considerations, several researchers (Lee and Buchberger, 2001; Tzatchkov et al., and Lee; 2004) have shown that dispersion can have a significant effect on concentration profiles, especially in the cases of intermittent laminar flow. Further, Buchberger et al., (2003) have shown that the aggregation and concentration of distributed system demands at junction nodes can also impact water quality predictions. Finally, Van Bloemen Waanders et al. (2005) have shown that the normal assumption of complete mixing at junctions nodes, such as made in EPANET2, is not totally accurate, especially at four pipe intersections. In order to evaluate the impacts of such assumptions on the ability of hydraulic (and by dependence water quality) models to accurate forecast future state conditions, a laboratory scale model of a water distribution network was constructed and its performance compared to that of hydraulic and water quality computer models. The laboratory model for use in this study was based on an actual water distribution system in Kentucky. Following the identification of the utility, a site visit to the utility was conducted and relevant system data collected, including data associated with the hydraulic model for the system, as well as operational field data for use in developing and calibrating a water quality model for the system. Using the collected data, a scale representation of the system was constructed in the hydraulics laboratory of the University of Kentucky, such that both network hydraulics and fate and transport 19

20 processes were represented as accurately as possible. The developed model was instrumented with basic flow and pressure sensors as well as the capability to evaluate surrogate water quality parameters (e.g. food grade calcium chloride). The model was also instrumented with inline valves for use in adjusting the relative head-loss associated with each pipe segment. Hydraulic and water quality models of the system were developed and calibrated using data collected from the physical laboratory model using standard EPA protocols (2005) as modified for laboratory applications. Once calibrated, the computer models were used to forecast future hydraulic and water quality conditions based on the known boundary conditions of the physical model. The observations from the physical model were compared with the predictions of the computer models in order to assess the reliability of the computer models to accurately predict the observed conditions. The sensitivity of the measured water quality parameters to system hydraulics were determined by varying the boundary and loading conditions of the physical model and then determining the resulting impacts on the water quality results. The ability of the computer models to replicate these results were evaluated. Following these analyses, the laboratory model of the actual utility water distribution network was be calibrated by adjusting the pipe roughness and system demands (of the laboratory physical model) using inline roughness values and demand outlet values respectively. A dimensional analysis of the critical network parameters was be performed in order to provide guidance with regard to scaling factors for application to the laboratory model. The ability of the computer models to replicate the hydraulic and water quality performance of the calibrated laboratory model was then be explored. It is anticipated that these analyses may yield useful insights on the relative impact of flow dynamics on water quality and the ability of computer models to accurate predict such impacts. A copy of the final report which documents the construction and application of the model is provided through the links below: [47pdf] Physical Model Design Report [48pdf]Physical Model Analysis Report Field Calibration Calibration of a water distribution model requires field data, such as pressures, flows, travel times, water quality concentrations, etc. Before performing the data collection, one should develop a Standard Operating Procedures (SOP) for the data collection as well as a Quality Assurance Project Plan (QAPP). Examples of both are provided below: [53pdf] Water Distribution System Analysis: Field Studies, Modeling and Management [54pdf] Quality Assurance Project Plan for Calibration Data Collection Actual System Calibration Case Studies As part of the research project, hydraulic simulation models were developed for a small and moderate sized water utility in Kentucky. A water quality model was also developed for the moderate sized utility. Both of the hydraulic models were first calibrated prior to use. The water quality model was also calibrated. In addition, a large water utility was also calibrated as part of the real-time model study of the research project. Reports on each of these calibration studies can be obtained from the links 20

21 below: [49pdf] Hydraulic Calibration of Small Water Utility [50pdf] Hydraulic Calibration of Moderate Sized Water Utility [51pdf] Water Quality Calibration of Moderate Sized Water Utility [52pdf] Water Quality Calibration of Large Sized Water Utility In addition to these studies, the CDC recently completed a water quality study of the Camp Lejeune Marine Base. Results of this study can be accessed below: [55pdf] Camp Lejeune Water Quality Study [56pdf] Camp Lejeune Water Quality Study Presentation 21

22 ON-LINE COMPUTER MODELS Since measurement of hydraulic and water quality dynamics in pipe networks is performed at relatively few locations, network models are important tools for understanding network-wide flow dynamics, and their impact on the evolution of water quality. Many water utilities have developed computer models of water distribution systems as a tool for system design and analysis. Although great effort has been invested in such models, most of them are limited to off line analysis. These models are calibrated with data sampled during certain periods of time (usually several days) and only simulate the hydraulic and water quality conditions under specific water demand and operational scenarios. Despite the relatively wide application of SCADA systems, and the presumably vast amount of data stored within these archives, water distribution system models are not updated frequently, nor compared to historical SCADA records. Off-line models may not accurately represent the hydraulic or water quality behavior of water distribution systems under typical variability in water demands and operational response. Water utility operations will greatly benefit from the development of real-time network models since such models will provide a powerful mechanism to synthesize SCADA data and analyze/predict the network system performance under different operational scenarios. Such analysis will likely lead to better water quality predictions and improved anomaly/event detection, as well as dual-benefits such as improved energy efficiency or disinfectant residual management, because operators would have a consistently reliable tool to determine how system operational changes affect current and forecasted hydraulic and water quality behavior. While off-line computer models (both hydraulic and water quality) can be used to improve the overall operations of a water distribution systems, further improvement may be obtained through the use of models in a real-time or on-line environment. Water distribution systems are designed and operated to satisfy a range of objectives, including hydraulic performance. Metrics of hydraulic performance include pressure levels, fire protection, water quality, and various measures of system reliability. Real time monitoring can be used to identify the status of a water distribution system at a particular point in time. By archiving such data, subsequent statistical analysis can be performed to identify various performance trends under different conditions. Use of an on-line or (real time) hydraulic model can be further used to evaluate the future performance of the system under different conditions. Water utilities are frequently faced with the challenge of having to respond to a range of different emergency situations. Such emergencies can included pipe breaks, component failures (e.g. pumps), low pressure issues, cross connections, and contamination events (either accidental or intentional). Real-time hydraulic models can be used to evaluate the impact of such emergencies on the future status of the system as well as for use in evaluating possible response actions. Water quality management continues to be a significant challenge for water distribution system operators, especially in light of increasing water quality regulations. The use of real time water quality models provides the opportunity for system operators to improve the quality of their delivered product by having the capability to model the water quality impacts of different operational changes in the system as well as to explore the impact of the location of regional chlorine booster stations. Hydraulic/water quality simulation models can be applied in a real-time environment in three different levels: archived simulation, real-time forecasting, and real-time control. At the heart of each level is a SCADA system that collects and logs data, monitors the operational status of the system, and transmits operator's directives to various control devices in the field (see Figure 7). In archived simulation (see Figure 8), the simulation model is integrated with the SCADA system database for use in evaluating 22

23 previous historical data sets for the purpose of evaluating general operating rules or for use in predicting the possible performance of the system for similar scenarios. The next level of real-time simulation, shown in Figure 9, is the computer-assisted command structure sometimes called real-time forecasting. This control structure provides operators with an interactive environment incorporating a SCADA system linked with software capable of predicting the state of the hydraulic system. The software will typically involved two basic components: 1) a demand forecast model and 2) a hydraulic/water quality model of the distribution system. The demand forecast model will typically be integrated with the SCADA system so as to provide real-time forecasts of system demand which are then fed as boundary conditions to the hydraulic/water quality simulation program. The final level of real time simulation is the computer-directed command structure shown in Figure 10. The computerdirected structure is often called real-time control because the structure is able to provide an optimal operating policy based on prescribed operational goals. As a result, this level of operations involves three different software components: 1) a demand forecast model, 2) a hydraulic/water quality model, and 3) an optimal control model. Distribution Network SCADA System Figure 7. Heart of Real Time Operating Environment With respect to the minimization of operational costs, the purpose of an optimal control system is to provide the operator with the least-cost operation policy for control units (e.g. pump stations, booster chlorinators, etc.) in the water-supply system. The operation policy for a system is simply a set of rules or a schedule that indicates when a particular control unit or group of control units should be turned on or off over a specified time period. The optimal policy should result in the lowest total operating cost for a given set of boundary conditions and system constraints. 23

24 Distribution Network Real Time SCADA Data SCADA System Scenario Analysis SCADA Database Archived Scenarios Network Simulation Model Figure 8. Archived Simulation Distribution Network Real Time SCADA Data SCADA System Scenario Analysis SCADA Database Archived Scenarios Network Simulation Model Figure 9. Real Time Forecasting 24

25 Distribution Network Real Time SCADA Data SCADA System Scenario Analysis SCADA Database Archived Scenarios Network Simulation Model Model/SCADA Integration/Calibration Figure 10. Real Time Control While two of the major network analysis software vendors (i.e. MWH Soft and Haestad Methods) have developed SCADA interfaces for their distribution models, neither have seen much commercial application. To date, virtually no studies have looked at the practical benefits and limitations of such technologies to real systems. In particular, it is currently unclear how system flow dynamics might affect the performance of such systems (e.g. on accurate water quality predictions) both for normal operations and for detection of possible incursions to the system. Part of the reason for the limited use of real-time modeling is because of the challenges of making the interface connection between the model and the SCADA database. The subsequent discussion assumes that EPANET will be used for the software model, however similar steps would be required for other models as well. Typical steps involved in integrating a model with a SCADA system include: A general software system has to be developed for linking the hydraulic and water quality models, with the utility SCADA database. As part of this research, software was developed for use in linking EPANET with an actual utility SCADA system. Next, the software system has to be configured to link real time data on tank and clearwell water levels to boundary conditions of the network model, and to link real time data on pump operation to pump status in the network model. In addition, data on water source flow rates and tank water levels has to be processed to estimate zoned water demand variations, so that the real-time model can reflect those demand variations through adjustments of the demand multipliers. A comprehensive statistical comparison of hydraulic model predictions and distribution system hydraulic measurements has to be performed for an extended historical analysis period (at least 25

26 one year). This will require identification of SCADA tags associated with pressure and flow measurements with associated network model objects. A comprehensive statistical comparison of water quality model predictions and distribution system water quality measurements then needs to be performed for an extended historical analysis period (at least one year). This will require identification of SCADA tags associated with water quality measurements with associated network model objects. It will also require the development of appropriate water quality reaction dynamics models to predict the water quality sensor signals; these water quality models will be developed and integrated into EPANET- MSX, the multi-species water quality network modeling extension to EPANET Verification studies will need to be performed by selecting locations for additional pressure measurements, and installing integrated pressure transducers and data loggers at fire hydrants. These will collect data for a shorter period of 1-3 months, at which time these additional data will be harvested and integrated with the telemetered SCADA database, and a similar statistical analysis performed on this verification data set. The pressure monitoring will be conducted at locations that are deemed to be the least reliable, or to represent the greatest variability, so as to test the model accuracy under the most stressed conditions. A comprehensive data analysis will need to be performed to understand the variability of network hydraulics and water quality in this large system over an extended time of actual operation. Specifically, since water quality evolution is dependent on the flow paths through the network and the velocity of those paths, an analysis will be performed to quantify the variability in flow paths as predicted by the real-time calibrated hydraulic model. These analyses will be based on previous work by the research team to quantify network flow paths using particle backtracking, and associated software. Finally, the ability of the real-time models to provide realistic predictions of future states and deviations from such states (i.e. as a result of a water quality contamination event) will need to be determined. This assessment and results of previous tasks will support recommendations about how real-time hydraulic and water quality models can best be used to support improved real-time event detection, and at what cost. Potential Applications of Real-Time Simulation Water distribution system hydraulic/water quality models can be used in a range of real-time operations. These include the following: [74] Real Time Network Hydraulic Modeling: Data Transformation, Model Calibration, and Simulation Accuracy [75] Accuracy of Real-Time Network Water Quality Models [73] Optimal Control for Energy Minimization [72] Real Time Even Detection 26