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: zheng.wu@bentley.comWebsite: www.bentley.com