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S. Mohsen Sadatiyan A ., Samuel Dustin Stanley, Donald V. Chase, Carol J. Miller,

Optimizing Pumping S ystem for S ustainable W ater D istribution N etwork by U sing Genetic Algorithm. S. Mohsen Sadatiyan A ., Samuel Dustin Stanley, Donald V. Chase, Carol J. Miller, Shawn P. McElmurry. Energy & Water. Energy and water issues are linked together

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S. Mohsen Sadatiyan A ., Samuel Dustin Stanley, Donald V. Chase, Carol J. Miller,

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  1. Optimizing Pumping System for Sustainable Water Distribution Networkby Using Genetic Algorithm S. Mohsen Sadatiyan A., Samuel Dustin Stanley, Donald V. Chase, Carol J. Miller, Shawn P. McElmurry

  2. 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

  3. Optimal Pumping Schedule • optimal pump schedule • minimum energy demand, cost & associated pollutant emissions • meet system requirements with different set of operation schedules

  4. Optimization Methods • Multi-Objective & Multi-Criteria Optimization

  5. Genetic Algorithm • pumping schedule • genetic analogy • the best solution of the lastgeneration=optimum solution

  6. Optimizing Software and Case Studies • PEPSO: Pollutant Emission & Pump Station Optimization • 2drinking water systems within the Great Lakes watershed

  7. Discrete & Continuous Methods • Continuous Method • Discrete Method

  8. Memory Usage of Continuous Method • 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)

  9. Memory Usage of Discrete Method • 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)

  10. Crossover of Continuous Method

  11. 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

  12. Crossover of Discrete Method • Crossover • multipoint crossover • Identical breaking points for both parents • Does not have time infeasibility

  13. Mutation of Discrete Method • Mutation • invert randomly selected gene • replace randomly selected gene by random number

  14. Variable Speed Pumps • A random number between min & max speed ratio for mutation

  15. Existing PEPSO & New Research Areas • PEPSO V8.0.3.0 • Multi-objective • Discrete method • Multipoint crossover • Variable speed pumps • GA options

  16. Key Points

  17. Questions? Comments? mohsen@wayne.edu

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