What is it about?
Identifying potentially efficient ways of operating water treatment works using computer models. Random setups of a works are first simulated and the best performing ones are selected. These higher performing setups are then used to create a second generation of setups which have characteristics similar to them. The performance of the 'parent' and 'offspring' setups are them assessed and the best setups selected again. This process is then repeated over multiple generations until good setups for the WTW are identified. The simulated conditions that the water treatment works operated under is created by repeatedly randomly selecting values for variables including the temperature, pH, organics content and abstraction rate. These values are selected in a way which makes them representative of what would be found at a real site.
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Perspectives
This is my second (and final paper) based on my PhD "Optimisation of Water Treatment Works using Monte-Carlo Methods and Genetic Algorithms" which I completed at the University of Birmingham. It follows on from a previous paper "An assessment of static and dynamic models to predict water treatment works performance" published in "Journal of Water Supply: Research and Technology—AQUA"
Roger Swan
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This page is a summary of: Optimisation of water treatment works performance using genetic algorithms, Journal of Hydroinformatics, June 2017, IWA Publishing,
DOI: 10.2166/hydro.2017.083.
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