Genetic Algorithm Optimization for Accurate Hydraulic and Water Quality Analysis of Water Systems
Z. Y. Wu, T. Walski, R. Mankowski, G. Herrin and R. Gurrieri Bentley Systems, Incorporated, USA
E. F. Arniella, E. Gianellaand, Envirosoft Eng. & Sci., Inc., USA
C. Clark, City of Sidney, Ohio, USA
P. Sage, United Utilities PLC, UK
Outline
• Needs for innovative technology• Water system analysis in a
nutshell• Long-standing and emerging
challenges• Competitive solution methods• Practical applications
Sustainable Needs
• Water systems are vital role in mankind history
• Systems are deteriorated over years• Must be upgraded systematically• Need comprehensive and accurate
analysis• Hydraulics throughout system• Water quality characteristics• Water security
• Cannot be achieved without innovation
WDS Analysis Overview
• Hydraulic model since 1960’s• Flow conservation law• Energy conservation law
• Water quality model• Reynold’s Transport Theorem (RTT)• Mass balance• Chemical reactions
• Hundreds of millions invested in modeling
WDS Model Examples• Model tens or hundreds of
thousands of elements (pipes, pumps, valves, tanks and reservoirs)
• Model a few pipes
Challenges• Long-standing build accurate and
robust model• Identify roughness coefficients for all pipes• Identify demand amount of water out of
system• Identify pump and valve operating settings
• Emerging challenges• Water security• Tougher regulations for water quality • Higher customer expectation• Tighter financial budget
Challenges (cont.)• Customers are expected to save
water• Water companies are losing more
water than the saved• In average, water loss > 15%
Call for the best innovations!!
Innovation Since 1960’s
• Fight for model accuracy via calibration• Adjust model parameters• Minimize the model predicted and the
observed
• Hundreds of papers published• Lack of robustness for handling growing
complexity• Mixed continuous and discrete parameters• Static and dynamic parameters• Large model size• Astronomically large solution space
Generalized FormulationSearch for:Minimize:Subject to:
.,...,1;,...,1;,...,1),,( ,, NKkNJjNIismfX tktji
)(XF
iii fff
tjtjtj mmm ,,,
}1,0{, tks
Where: fi is the roughness coefficient for pipe i
mj,t is the demand factor for node j at time t
Sk,t is operating setting for element k at time t
is defined in four distance functions)(XF
fmGA Optimizer
• Multi-era and two-loop evolution
• Start with short strings
• Enable partial solutions for a large system optimization
Era = 1
Initialization
Building Block Filtering
Generation = 1
Selection
Cut SpliceMutation
Generation++
Era++
Gen
era
tion
Loop
Era
Loop
Core Method Darwin Calibrator
Integrated into WaterCAD and WaterGEMS standalone version and multiple platforms of MicroStation, AutoCAD and ArcGIS
Handle Parameter Dynamics
• Snapshot dataset system wide data at one time step
• Allow multiple snapshots• Optimization for all snapshots
Application Guidance• Make parameter sensitive grouping• Decompose system into subsystems• Progressive calibration/optimization
in multiple inherited runs
Competitive Case I
• Water system model for city of Guayaquil, Ecuador
• Supply 2.3 million people• Water loss > 50%
• Optimize model parameters• Improve project productivity
• 40 man-hours with the innovative tool
• At least four times as long (160 man-hours) with conventional modeling method
Competitive Case I: Benefits• Forge non-revenue water reduction plan• Simulate water loss in low pressure zones• Identify pipes to be replaced or rehabilitated• Analyze effects of future system expansions• Produce informed 30-year master plan for
City
Competitive Case II
• Identify system demand and pipe roughness coefficient
• Enable informed system analysis
• Water system model for city of Sidney, Ohio
• More than 150 miles water mains
Competitive Case II: Benefits• Model new City
subdivisions• Report annexation
assessment• Model new
industrial users• Provide fire flow
data to developers, engineers, architects and fire fighters
• Develop a new Hydrant Tagging System
Blue hydrant >1500 gpm Green hydrant 1000 – 1500 gpm
Orange hydrant 500 – 1,000gpm Red hydrant < 500 gpm
Competitive Case III• Oberlin zone of Harrisburg, PA• Water quality benchmark funded
by AWWARF
• Excel benchmark results• More robust and effective at handling
all types of chemical reactions
Items
GA Solution
one
GA Solution
two
GA Solution
three
Vasconcelos et al (1997)-
Benchmark
Sum of absolute mean differences 1.3090 1.3106 1.3119 2.4670
Average absolute mean difference (mg/L) 0.0450 0.0450 0.0450 0.0860
Competitive Case IV• No technique for both water loss
detection and model calibration• Apply Darwin Calibrator to a District
Meter Area (DMA) in UK
• Optimize nodal demand
• Locate actual demand differences
• Predict leakage hotspots
• Minimize leak detection uncertainty
• Facilitate a better detection rate
Conclusions• Solve the indisputable difficult problem of model
calibration (HUMIES criteria G)
• Better the methods for the long-standing difficult problem of hydraulic and water quality model calibration (HUMIES criteria E)
• Produce better results than the research project supported by America Water Works Research Foundation (HUMIES criteria F)
• Calibrated modeling results have been published and also used in practice (HUMIES criteria D)
Conclusions (cont.)• Outperform the previously published methods in
robustness, flexibility and effectiveness (HUMIES criteria B & C)
• Provide the new method for water loss/leakage detection (HUMIES criteria D)
• Excel human-competitive criteria• Generalize the human-competitive results for practical
applications• Integrate as a off-shelf modeling tool in multiple CAD and
GIS platforms• Develop the application guidelines for industry applications• Bring the benefit of the technology advancement to water
industry• The technology has been applied around the world
Full Citations[1] Wu, Z. Y. (2006) "Optimal Calibration Method for Water Distribution Water Quality
Model.", Journal of Environmental Science and Health Part A, Vol. 41, No. 7, pp1363-1378.
[2] Wu, Z. Y. and Sage P. (2006) “Water Loss Detection via Genetic Algorithm
Optimization-based Model Calibration” ASCE 8th Annual International Symposium on Water Distribution Systems Analysis, Cincinnati, Ohio, August 27-30, 2006.
[3] Clark, C. and Wu, Z. Y. (2006) "Integrated Hydraulic Model and Genetic Algorithm Optimization for Informed Analysis of a real system" ASCE 8th Annual International Symposium on Water Distribution Systems Analysis, Cincinnati, Ohio, August 27-30, 2006.
[4] Wu Z. Y. and Walski T. (2005) “Diagnosing error prone application of optimal model calibration.” International Conference of Computing and Control in the Water Industry, Sept. 5-7 2005, Exeter, UK.
[5] Wu, Z. Y., Elio F. A. and Ernesto G. (2004) "Darwin Calibrator--Productivity and Model Quality for Large Water System", Journal of America Water Works Association, Vol. 96, No.10, pp27-34.
[6] Wu, Z. Y, Walski, T., Mankowski, R., Herrin G., Gurrieri R. and Tryby, M.(2002) “Calibrating Water Distribution Model Via Genetic Algorithms”, in Proceedings of the AWWA IMTech Conference, April 16-19, Kansas City, MI.
Thank You!
Zheng Y. Wu, Ph.DBentley Systems, Incorporated
Haestad Methods Solution Center27 Siemon Co Dr. Suite200WWatertown, CT06759, USA
Email: [email protected]: www.bentley.com