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

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Presentation transcript:

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 fitness of solution Global Optimum derivatives or other auxiliary characteristics may results Local Optimum

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

Memory Usage of Discrete Method

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 V 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?