S. Mohsen Sadatiyan A.,

Samuel Dustin Stanley,

Donald V. Chase,

Carol J. Miller,

Shawn P. McElmurry

Optimizing Pumping System

for Sustainable Water Distribution Network

by Using Genetic Algorithm

Energy & Water

Energy and water issues are linked together

About 5% of energy demand of US is related to water supply and treatment

About 75% of operation costs of municipal water facilities are attributed to energy demand

Energy Extraction & generation requires

water

Water Extraction, treatment & distribution

requires energy

Optimal Pumping Schedule

reduce total pumping cost

shift pump operation time & space

change in energy cost by time

optimal pump schedule

minimum energy demand, cost &

associated pollutant emissions

reduce pollutant emission

shift energy demand time & space

change in pollution emission by time

meet system requirements with different

set of operation schedules

Multi-Objective & Multi-Criteria Optimization

Optimization Methods

Traditional Analytical Methods

Evolutionary Algorithms

Genetic Algorithm

•pumping schedule

•genetic analogy

• the best solution of the last generation=optimum solution

Fitness evaluation & Elitist

Reproduction

(Crossover)

Mutation

Optimizing Software and Case Studies

PEPSO: Pollutant Emission & Pump Station Optimization

2 drinking water systems within the Great Lakes watershed

PEPSO V4.0~4.5 PEPSO

V8.0~8.0.3

Visual interface

Modified Crossover

& Mutation

Quasi-Newton Method Multi-

Objective

Variable speed pump

Genetic Algorithm

Discrete

Vs.

Continuous

PEPSO V1.0~3.0

Continuous Method

Discrete Method

Discrete & Continuous Methods

Memory Usage of Continuous Method

𝑴𝒄 = 𝒏 × 𝒄 × 𝟐 × 𝟐 𝒃𝒚𝒕𝒆𝒔

Mc= memory usage (byte)

n= number of pumps

c= number of cycle per modeling duration

2 bytes= required memory for storing a number between 0 to 86400 second (for greater time intervals or shorter modeling period, 1 byte can be used)

Memory Usage of Discrete Method

𝑴𝒅 = 𝒏 ×𝑻

𝑰×

𝟏 𝒃𝒚𝒕𝒆

𝟖

Md= memory usage (byte)

n= number of pumps

T= duration of modeling

I= time intervals

1 byte/8= 1 bit (“0” or “1” – ON or OFF)

Crossover of Continuous Method

Mutation of Continuous Method

• Mutation

•infeasible children

•pairs of controls instead of one control

•sorting solution arrays by time

•remaining problem for near optimum solutions

Crossover of Discrete Method

• Crossover

• multipoint crossover

• Identical breaking points for both parents

• Does not have time infeasibility

Mutation of Discrete Method

• Mutation

• invert randomly selected gene

• replace randomly selected gene by random number

Variable Speed Pumps

• A random number between min & max speed ratio for mutation

Continuous Method

a column for speed ratio of pump for each

cycle

Discrete Method

replace OFF=0 and ON=1, by fractional

numbers (speed ratio of pumps)

Existing PEPSO & New Research Areas

PEPSO V8.0.3.0

•Multi-objective

•Discrete method

•Multipoint crossover

•Variable speed pumps

•GA options

Key Points

Discrete method needs substantial storage space, especially for longer modeling periods and smaller time

intervals. Provides feasible solutions.

Adjusting parameters, such as modeling period, time intervals and hydraulic model details, are important to obtain accurate results during reasonable running time.

Evolutionary algorithms are useful to optimize pumping.

Questions? Comments? mohsen@wayne.edu