1. 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

2. Outline Needs for innovative technology
Water system analysis in a nutshell
Long-standing and emerging challenges
Competitive solution methods
Practical applications

3. 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

4. WDS Analysis Overview Hydraulic model since 1960’s Water quality model
Flow conservation law
Energy conservation law
Water quality model
Reynold’s Transport Theorem (RTT)
Mass balance
Chemical reactions
Hundreds of millions invested in modeling

5. WDS Model Examples
Model tens or hundreds of thousands of elements (pipes, pumps, valves, tanks and reservoirs)
Model a few pipes

6. 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

7. Call for the best innovations!!
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!!

8. 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

9. Generalized Formulation
Search for:
Minimize:
Subject to:
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

10. fmGA Optimizer Multi-era and two-loop evolution
Initialization
Building Block Filtering
Generation = 1
Selection
Cut
Splice
Mutation
Generation++
Era++
Generation Loop
Era Loop
Multi-era and two-loop evolution
Start with short strings
Enable partial solutions for a large system optimization

11. Core Method  Darwin Calibrator
Integrated into WaterCAD and WaterGEMS standalone version and multiple platforms of MicroStation, AutoCAD and ArcGIS

12. Handle Parameter Dynamics
Snapshot dataset  system wide data at one time step
Allow multiple snapshots
Optimization for all snapshots

13. Application Guidance Make parameter sensitive grouping
Decompose system into subsystems
Progressive calibration/optimization in multiple inherited runs

14. 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

15. 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

16. Competitive Case II Water system model for city of Sidney, Ohio
More than 150 miles water mains
Identify system demand and pipe roughness coefficient
Enable informed system analysis

17. 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

18. Vasconcelos et al (1997)-Benchmark
Competitive Case III
Oberlin zone of Harrisburg, PA
Water quality benchmark funded by AWWARF
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
0.0860
Excel benchmark results
More robust and effective at handling all types of chemical reactions

19. 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

20. 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)

21. 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

22. 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, pp
[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 , 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.

23. Thank You! Zheng Y. Wu, Ph.D Bentley Systems, Incorporated
Haestad Methods Solution Center
27 Siemon Co Dr. Suite200W
Watertown, CT06759, USA
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