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Thursday 24 Jul 2014 Multi-objective optimisation using genetic algorithms to integrate pumping operations of alternative water supplies

Lisa Blinco -

Harrison 170 12:00-13:00

Traditionally, water supply has relied on surface waters from rivers, lakes and reservoirs for the majority of potable supply. As climate change and increasing consumer demands put a strain on current water supplies, alternative water sources are becoming more commonly used. This brings about a need for methodologies to optimise systems that use alternative water sources, as they can be significantly more complex than traditional systems. A genetic algorithm (GA) optimization model for water distribution system (WDS) operation was developed, linking to a user-friendly Excel interface and EPANET. Pumping operations were optimized from the perspectives of time-based scheduling, tank trigger levels and variable speed pumping (VSPs). An important focus was the minimization of operational greenhouse gas (GHG) emissions, in conjunction with operational economic cost, to provide a comprehensive solution to the pumping problem. Various possible future energy scenarios (including a number of renewable energy sources) have been investigated to determine the effect of varying GHG emissions factors on the optimal operational decisions for WDSs. This work will be extended to develop methodology for multi-objective optimisation of integrated pumping operations of alternative water sources. The trade-offs between minimisation of cost and minimisation of greenhouse gas emissions will be analysed. Various approaches for dealing with optimisation of operation on two different time scales long-term water allocations and daily pumping operations will be investigates. Case study networks with integrated pumping of multiple water sources - potable, recycled wastewater, harvested stormwater and groundwater - will be used to demonstrate the effectiveness of the methodology.

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