Water System Design by Optimization: Colorado Springs Utilities Case Studies Abstract Istvan Lippai, Ph.D., P.E. 1 Traditional water system master planning procedures have included designing water distribution networks to meet minimum system pressures, velocities and fire flow requirements. With the population of Colorado Springs projected to increase to nearly 520,000 by 2030, the Colorado Springs Utilities (Utility) has adopted designby-optimization to reduce costs and streamline design procedures. WinPipes is an EPANET-based water distribution system solver, optimizer, and output interpreter. It was modified for the specific requirements of the Utility. The optimization program was designed to evaluate multiple performance criteria for maximum and minimum pressure, maximum velocity, flow from a single hydrant, simultaneous flow from multiple hydrants, pump capacity and energy, and component failure based reliability. Development of the optimizer as well as the design-by- optimization approach of both a small and a large development is presented. The master planning of these developments represented unique challenges for the design and operation of the Utilities distribution system. Design-by- optimization allowed the Utility to meet specific design criteria at near minimum cost without compromising the integrity or reliability of the distribution system. Introduction The Utility currently serves a population of about 370,000 persons with the population projected to increase to about 520,000 persons by year 2030. Ground elevations within the Utility service area range from about 5,900 feet (1,800 m), USGS datum, along the southern edge of Colorado Springs to 7,800 feet (2,380 m) in the western regions of the city along the foothills. Water is supplied to the primary service levels (pressure zones) mainly by gravity through a complex system of large mains, pressure reducing valves (PRV's), and storage reservoirs. 1 Water Resources Consultant, Castle Rock, Colorado, bpgclm@yahoo.com 1
The Utility service area is divided into five major service levels including Briargate, Templeton, Northfield, Highline, and Lowline. Ten secondary service levels are served within each primary service level. Four water treatment plants provide a delivery capacity of 230 MGD (10,100 l/s). Water treatment plants are McCullough at 75 MGD (3,300 l/s), Pine Valley at 92 MGD (4,000 l/s), Mesa at 50 MGD (2,200 l/s), and Fountain Valley Authority (FVA) at 13 MGD (600 l/s). A future SDS water treatment plant (150 MGD) is proposed in the eastern vicinity of the City to serve future growth. Figure 1 shows the City of Colorado Springs current and future water system. FLYING HORSE RANCH McCullough WTP (75 MGD) Pine Valley WTP (92 MGD) WOLF-CORDERA RANCH WOODMEN HEIGHTS RANCH BANNING LEWIS RANCH Mesa WTP (50 MGD) SDS WTP (150 MGD) FVA (13 MGD) SDS (150 MGD) Figure 1 Colorado Springs Utilities Current and Future Water System Growth within the City is limited to the northern and eastern boundaries of the water system. Dark lines in Figure 1 represent the existing water system. Gray lines represent expected future developments. Several master planned developments within the City require system optimization to determine the best engineering solution in meeting established design criteria while at the same time minimizing capital costs. A hydraulic water model in conjunction with WinPipes was used to optimize the water system design for a small development and a large development. The area of the small development, the Wolf-Codera Ranch, is 2,400 acres (9.7 km2) with an estimated average daily demand (ADD) of 3.7 MGD (160 l/s). In contrast, the Banning Lewis development is 28,000 acres (113.3 km2) with an ADD of 60 MGD (2630 l/s). Water service to the Wolf-Codera Ranch is provided from a 10 MGD (440 l/s) pump station which pumps to a 5 MG (19,000 m3) reservoir. The Banning Lewis 2
development will be served from the proposed SDS water treatment plant and a series of reservoirs and pump stations. Water Model The Utility is using H2Onet for water system modeling. The model currently contains 39 storage reservoirs, 258 pressure reducing valves, over 19,000 nodes, and over 23,000 pipes. The model is expected to grow with new developments. The current ADD is 84 MGD (3,700 l/s). The projected 2030 ADD is 113 MGD (5,000 l/s). The maximum day to average day peaking factor used for system planning is 2.5. The water models for both the small and large developments optimized in this study were easily separated from the overall system model. Fire Flows H2ONet s Fireflow command is used to simulate fire flows from individual hydrants. All construction permits for new development must be approved by Colorado Springs Fire Department (Fire Department). The Fire Department requires a report by the Utility stating the available pressure and fire flow for the proposed construction meets minimum criteria. Expected post-development pressures and fire flows are computed using the Utility s water model. The method of modeling fire flows was established in close cooperation with the Fire Department to ensure their confidence in the modeled results. The headloss for a typical hydrant with 5-1/4 inch Main Valve is computed based on a 4-1/2 inch Nozzle. Hydrant nozzle headloss coefficients were computed from discharge-headloss curves supplied by hydrant manufacturers. The total headloss is computed as the sum of minor headloss and hydrant lateral friction headloss to simulate fire flows. A typical hydrant configuration and associated minor headloss coefficients are depicted in Figure 2. 4-1/2" NOZZLE HYDRANT 6" GATE VALVE Minor Loss Coefficients Item Description K Line to Branch Tee 0.8 8"x6" Reducer 0.2 6" Gate Valve 0.2 Hydrant Nozzle 2.8 Total 4.0 5-1/4" VALVE 8" MAIN 6" HYDRANT LATERAL Figure 2 Typical New Hydrant Installation 3
The available hydrant discharge is simulated with a residual 20 psi (14.1 m) of pressure remaining at the hydrant and in the system. This is consistent with a Fire Department requirement as well as the method used by commercial software to model fire flows. Minor headloss, is added to the friction headloss through the 6 inch (150 mm) hydrant lateral to model fire flows. It is important to estimate the available fire flow as accurately as possible because underestimating the available fire flow would be uneconomical while overestimating the available fire flow could result in denial of occupancy permit. Alternative Method of Determining Fire Flows No service connections are allowed between the 6 inch (150 mm) gate valve and the hydrant for developments within the Colorado Springs service area. With 20 psi (14.1 m) residual pressure at the left side of the valve and everywhere else in the system, fire flow can also be computed by assuming zero pressure at the hydrant discharge nozzle. This method may be more realistic and WinPipes could be used to compute fire flows with this alternate method. It was not used for the design-byoptimization of Utility projects because commercial software (e.g. H2ONet) does not currently support this alternative. Simultaneous Fire Flows Another simulation scenario used to estimate system performance involved more than one hydrant open at the same time. Several hydrants are opened at the same time to produce large fire flows for this scenario. H2ONet s Simultaneous Fire command is not used because it does not simulate the Fire Department requirement of minimum 20 psi (14.1 m) residual at the hydrants. Instead, each fire node was replaced with a tank of water surface elevation equal to the elevation of fire node plus residual pressure of 20 psi or 46.2 feet (14.1 m). This method has worked better for modeling simultaneous fire flows under large fire flow demand. This configuration is shown in Figure 3. Figure 3 Simultaneous Flow from 3 Hydrants 4
The impact of large simultaneous flows on the pressure gradient is more pronounced than the impact of individual hydrants. It is very important to verify the minimum residual pressure is maintained in the system and velocities do not exceed the maximum allowable velocity during simultaneous flows. Planning and Design of New Developments Colorado Springs is experiencing rapid growth. Most of the growth is occurring on the northern and eastern side of Colorado Springs on large sections of land. During the past two years, development plans were submitted for Flying Horse Ranch (FHR), Wolf-Cordera Ranch (WCR), Woodmen Heights Ranch (WHR) and Banning Lewis Ranch (BLR). There was prior knowledge about the nature and requirements for FHR and WCR, but very little prior information was available for WHR and BLR. Design-by- optimization was used by a relatively small Utility staff to design large extensions to the existing system on short notice. WCR and BLR were selected for presentation in this paper because WCR and BLR represent the challenges of small and large systems, respectively. Wolf-Cordera Ranch Model WCR model contains1981 pipes, 372 hydrants and 5 pumps. The pipe to area ratio is 0.825 pipes per acre (204 pipes per km2). The system consists mostly of 8 inch (200 mm) pipes. The system configuration is given in Figure 4 with fire flow requirements shown. CORDERA RANCH WOLF RANCH RC WOLF RANCH RESERVOIR OVERFLOW = 7315' PRESSURE ZONE OVERFLOW = 7435' RC RC FIRE DEMAND : 8000 GPM MU1: 8000 GPM MU2: 7250 GPM ES1: 5250 GPM MS1: 5250 GPM RC: 2750 GPM RW: 2500 GPM BRIARGATE RESERVOIR OVERFLOW=7135' RC ES1 RC RW MU2 MU1 RW MS1 Figure 4 Wolf-Cordera Ranch Model Utility s standards require that 8 inch (200 mm) minimum diameter pipes are used for distribution lines and 6 inch (150 mm) minimum diameter pipes be used for cul-de- 5
sacs. Small systems that are designed for consumptive demand with little irrigation requirements may only need minimum pipe sizes. A summary of optimized WCR pipe diameters is given in Table 1. Table 1 Wolf-Cordera Ranch Pipe Summary Diameter Length Volume in mm ft m % gallons liters % 6 150 17,954 5,472 4.68 26,371 99,824 1.67 8 200 281,133 85,689 73.33 734,093 2,778,844 46.44 12 300 60,500 18,440 15.78 355,449 1,345,520 22.48 16 400 7,193 2,192 1.88 75,129 284,395 4.75 24 600 16,589 5,056 4.33 389,854 1,475,757 24.66 Total 383,369 116,851 100.00 1,580,895 5,984,341 100.00 Based on Utility experience with construction in undisturbed soil of $8/Diameter/ft ($0.315/Diameter/mm), the optimized cost of WCR water distribution system is $28.8M or $12,000/acre ($3.0M/km2). Banning Lewis Ranch Model BLR model is a planning skeletal model of 396 pipes representing supply mains. The pipe-to-area ratio is 0.014 pipes per acre (3.5 pipes per km2). The system consists of 12 inch (300 mm) and larger pipes. Large system consumptive demands combined with fire demands dictates the required pipe sizes. See Table 2 for summary of optimized BLR pipe diameters. Table 2 Banning Lewis Ranch Pipe Summary Diameter Length Volume in mm ft m % gallons liters % 12 300 651,341 198,529 62.04 3,826,750 14,485,824 22.65 16 400 194,400 59,253 18.52 2,030,464 7,686,144 12.02 24 600 80,425 24,514 7.66 1,890,047 7,154,608 11.19 36 900 66,985 20,417 6.38 3,541,944 13,407,718 20.96 42 1050 5,862 1,787 0.56 421,894 1,597,043 2.50 48 1200 35,066 10,688 3.34 3,296,309 12,477,886 19.51 54 1350 15,878 4,840 1.51 1,889,046 7,150,816 11.18 Total ####### 320,027 100.00 16,896,454 63,960,040 100.00 Based on $8/Diameter Inch/Foot, the optimized cost of BLR water mains is $144.4M or $5,200/acre ($1.3M/km2). A meaningful comparison cannot be made between WCR and BLR costs because only the larger supply mains are included in the BLR model, while all the pipes are included in the WCR model. WCR and BLR are both designed for 8,000 GPM (500 l/s) maximum fire flow demand. The differences in pipe sizes are due to differences in consumptive system demand. The relative impact of fire flow on pipe sizes depends on system demand. (Lippai and Heaney 2000). 6
Multi-objective Optimization with WinPipes Multi-objective optimization may be used to meet all design criteria while satisfying all physical constraints. These include, at a minimum, node pressures, fire flows and system performance with any one of the critical components out of service. The process is easy enough to allow designers to apply optimization to the design of new systems, expansion and rehabilitation of existing systems. Undergraduate students at the University of Colorado at Boulder learned to use WinPipes to optimize simple water systems after an introductory lecture followed with some phone and e-mail assistance. Combining third party optimizers with the simulator and evaluator is well suited for a variety of applications because the heuristic search engine is independent of the simulation model. The optimizer may be a genetic algorithm, a tabu search, or any suitable intelligent search engine. With the introduction of commercial optimizers, the project may be separated into several parts (e.g., simulation, evaluation and optimization). Individuals who are experienced in their specific area develop procedures for each part and the components combined in the appropriate environment. Several commercial and public domain optimizers were linked with WinPipes to determine which optimizer works best with water distribution system optimization. Evolver was found to work best with water system optimization (Lippai, Heaney and Laguna, 1999). WinPipes is linked to Evolver to design water systems by defining constants, constraints, variables and objectives, and manipulating the variables so that all the objectives are satisfied at optimal cost. See Figure 5. Evolver-WinPipes Interaction Spreadsheet Environment Initialize 1. Constants 2. Constraints 3. Objectives Evolver Updates water system variables using Genetic Algorithms WinPipes.EXE 1. Reads water system variables 2. Modifies EPANet data file 3. Solves modified EPANet data file 4. Computes Penalties Figure 5 Application of WinPipes for Optimization Constants: Constraints: Variables: Pipe and Energy Unit Cost Data, Node Demands and MD/MH Ratio. Maximum Velocity, Maximum Pressure and Rules. Pipe Diameters, Pipe Roughness Coefficients, Tank Elevations and Link Settings. 7
Objectives: Node Pressures, Node Pressures with Pipe Closures, Fire Flows, Simultaneous Fire Flows, Pump Station Discharge and Pumping Life Cycle Costs. Link settings, simultaneous fire flows, pump station discharge and pumping life cycle costs were implemented for WCR water system. WinPipes evaluates every component for each event and computes an objective value based on costs and penalties. Penalties are set intentionally high to drive the optimization process in the direction of a system that satisfies every design objective. Link Settings can be used to open and close pipes, valves and pumps and to change the setting of valves and pumps. Link Settings can be used to explore pressure zone boundary changes, reservoir locations and operation of pumps. Rules are used to guide the optimization process. Rules are used to modify Evolver generated variables prior to model simulation and evaluation by WinPipes. For example, rules may be used to force the diameters of a subset of pipes to a specific value or to remain within a range of values. A rule may state that diameters must always increase or remain the same. Wolf-Cordera Ranch Multi -Objective Optimization The WCR water system was evaluated for pressure at 544 nodes, fire flow at 544 hydrants, simultaneous fire flow for 33 commercial locations and 45 residential locations, pump discharge capacity and energy life cycle costs for average day and maximum day demands. The initial 625 objective evaluations were reduced to 40 objective evaluations by eliminating hydrants that were found to meet design criteria with minimum pipe sizes. Pressure for Node 12238 Pressure (psi) 66.0 64.0 62.0 60.0 58.0 56.0 54.0 52.0 50.0 48.0 46.0 44.0 42.0 40.0 38.0 36.0 34.0 32.0 30.0 28.0 26.0 24.0 22.0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 Time (hours) Figure 6 Pressure at Node 12238 with Maximum Day Demand, Fire Flow, Simultaneous Fire Flow and Pump Operation 8
A large number of checks were performed for each set of variables to verify that each simulation met the rules and constraints. See Figure 6 for an example time history of pressure at Node 12238. The Time (hours) represents a sequence of events occurring in response to multiple design criteria with each time period representing the response of the model to the design conditions imposed. The normal pressure at zero hour is 58 psi (40.8 m). The fire demand and pressure at 2 hours is 1,750 GPM (110 l/s) and 22 psi (15.5 m), respectively. The pump station discharge of 7,320 GPM (462 l/s) at 4 hours increases the pressure to 67 psi (47.1 m). The residential simultaneous discharge of 2,780 GPM (175 l/s) at 6 hours from two hydrants near Node 12238 reduces the pressure to 32 psi (22.5 m). The commercial simultaneous discharge of 8,590 GPM (542 l/s) at 11 hours from four hydrants reduces the pressure to 27 psi (19.0 m). Similar graphs can be generated for any node or link, and be inspected to evaluate system performance for possible inconsistencies. Optimizing WCR based on single objective of pressure alone would have been meaningless because the pipe sizes produced could not provide fire flows. Component failure based reliability is modeled with pipe closures. Utility maintenance first responds to pipe failure by isolating the broken pipe. It is done by closing the valves on both sides of the break. Developers often argue that the requirement of three valves for every tee and four valves for every cross is excessive but having adequate isolation valves play a critical role during of pipe failure. No pipe closures were included during the optimization process but a post-optimization reliability check was performed. The 8,300 GPM (524 l/s) demand distributed at 1123 nodes was converted to dynamic demand constructs (Lippai 1996) for realistic system response to pipe closures. Dynamic demand modeling replaces the fixed demand with a combination of flow control valve, a check valve and a reservoir to estimate the fraction of demand that is delivered. See Figure 7. Da y 1, 1 2 :0 0 AM Figure 7 Static Demands Replaced with Dynamic Demand Constructs 9
A detailed explanation of dynamic demand modeling can be found in Pipeline 2005 conference proceedings (Lippai and Wright 2005), Criticality Analysis Case Study: Zone 7 Water Distribution System. A post-optimization closure of 599 pipes revealed there was no serious degradation of service for any of the closed pipe scenarios, as seen in Figure 8. Pressure for Node 12238 58.0 Pressure (psi) 57.0 56.0 55.0 0 50 100 150 200 250 300 Tim e (hours) 350 400 450 500 550 Figure 8 Pressure at Node 12238 in Response to Pipe Closures Closure of the 24 inch (600 mm) pipe between the pump station and Wolf ranch reservoir requires pump activation to maintain service. Banning Lewis Ranch Multi-Objective Optimization The 28,000-acre (113 km2) BLR service area is divided into eight pressure zones. See Figure 9. Each pressure zone has its own independent supply reservoir with the following exceptions. Northfield supplies Reduced Northfield and Lowline supplies Reduced Lowline. Table 3 shows the pressure zones within the BLR, overflow or hydraulic grades for each zone and maximum day demands for ultimate build out of the BLR, which is expected by 2057. 10
Reduced Briargate Templeton Reduced Templeton Northfield Reduced Northfield Highline Lowline Reduced Lowline Figure 9 Banning Lewis Ranch Model Table 3 Banning Lewis Ranch Pressure zones Section Pressure Zone Overflow ElevationMaximum Daily Demand Number Description ft m MGD l/s % 1 Reduced Briargate 7135 2175 16.3 714 11.52 2 Templeton 6900 2103 32.2 1411 22.76 3 Reduced Templeton 6725 2050 3.5 153 2.47 4a Northfield 6688 2039 5.8 254 4.10 4b Reduced Northfield 6600 2012 5.0 219 3.53 5 Highline 6413 1955 27.6 1209 19.51 6a Lowline 6225 1897 28.3 1240 20.00 6b Reduced Lowline 5850 1783 22.8 999 16.11 Total Demand 141.5 6199 100.00 Optimization of independent sections of the water system was performed in parts to control the solution space (and therefore limit run times) but the complete BLR model was used for optimization of sections to take advantage of normally closed PRV s between pressure zones for fire flows. An 8,800 GPM (555 l/s) fire demand just above the normally closed PRV separating Reduced Briargate (RBRGT) and Templeton (TMPL) zones would allow PRV flow reversal from TMPL to RBRGT to assist with the RBRGT fire demand. See Table 4 for summary of BLR variables and constraints. 11
Table 4 Banning Lewis Ranch Variables and Constraints Section Pressure Zone Pipe Diameter Node Pressure Fire Flow Number Description Variables Constraints Constraints 1 Reduced Briargate 50 61 51.0 2 Templeton 44 51 51.0 3 Reduced Templeton 17 12 15.0 4 Northfield 22 27 31.0 5 Highline 59 39 39.0 6 Lowline 76 55 55.0 Total Variables and Constraint 268 245 242 The reduction of variables and constraints for large systems is not surprising. The optimization problem is greatly simplified because the model is less detailed. See Figure 10 for pressure at Reduced Briargate Node 501053. Pressure (psi) 74.0 72.0 70.0 68.0 66.0 64.0 62.0 60.0 58.0 56.0 54.0 52.0 50.0 48.0 46.0 44.0 42.0 40.0 38.0 Pressure for Node 501053 0 1 2 3 4 5 6 7 8 9 1011 1213 1415 161718 1920 2122 2324 2526 2728 2930 3132 333435 3637 3839 4041 4243 4445 4647 4849 5051 Time (hours) Figure 10 - Pressure at Node 501053 with Normal Demand and Fire Flow The normal pressure at zero hour is 74 psi (52.1 m). The fire demand and pressure at 31 hours is 8,800 GPM and 37.2 psi (555 l/s and 26.2 m), respectively. The required 8,000 GPM fire flow was increased by 10% because the skeletal system has no hydrants and therefore no simultaneous flow scenarios could be modeled. Conclusions and Recommendations Significant savings were realized from the designs submitted to the Utility for WCR. The $28.8M for WCR and $144.4M for BLR is the optimized cost that meets every specified design constraint. Equally important as the savings were the finding that the original designs submitted by the developer did not meet the design constraints and would have produced unacceptable pressures in the system during large simultaneous flows. The design-by- optimization of BLR did not identify savings. Initial diameters were deliberately overstated to produce an initial design to meet all 12
constraints. When design by optimization becomes a routine part of the design process the optimized project cost is going to be the project cost and claims of savings will be replaced with the knowledge that the optimal design meets all design objectives at the lowest possible cost. A major advantage of in-house design-by- optimization is the invaluable input from informed engineering management and design professionals. Design objectives were re-evaluated in consultation with management and design professionals to determine if some objectives should be modified after initial optimization and results presentation. Acknowledgements Len Wright, Ph.D., P.E., Water Resources Engineer, Carollo Engineers, edited the paper. Lisa Barbato, P.E, Managing Engineer, Colorado Springs Utilities, contributed the description of Colorado Springs Utilities water system in the Introduction section and edited the paper. Lisa Hagerman, Project Engineer, and Keta Donegan, Engineering Support Specialist, Colorado Springs Utilities, developed demand estimates for Banning Lewis Ranch. References Evolver (2001). The Genetic Algorithm Solver for Microsoft Excel, Palisade Corporation, Newfield, NY. H2ONet v3.5 (2001). Graphical Water Distribution Modeling and Management Package, MW Soft, Inc., Pasadena, CA. Lippai, I and Wright, L. (2005). Criticality Analysis Case Study: Zone 7 Water Distribution System, Pipeline 2005, Houston, Texas (accepted). Lippai, I. and Heaney, J.P. (2000). Efficient and Equitable Impact Fees for Urban Water Systems, J. Water Resources Planning and Management, pp. 75-84. Lippai, I, (1999). Robust Urban Water Distribution System Design. Doctor of Philosophy Dissertation, University of Colorado, Boulder, CO. Lippai, I., Heaney, J.P., and Laguna, M. (1999). Robust water system design with commercial intelligent search optimizers. J. Computing in Civil Engineering, pp. 135-143. Lippai, I. (1996). Introduction to demand based reliability of water distribution systems. In: Conference on Municipal and rural water supply and water quality, Poznan, Poland, pp. 437-447. Rossman, L. A. (1994). EPANET Users Manual. Drinking Water Research Division, Risk Reduction Eng. Laboratory, U. S. Environmental. Protection Agency, Cincinnati, OH. 13