Abstracts Engineering

Optimisation of water treatment works using Monte-Carlo methods and genetic algorithms

by Roger William Swan

Hand movements reveal the temporal characteristics of visual attention Optimisation of potable water treatment could result in substantial cost savings for water companies and their customers. To address this issue, computational modelling of water treatment works using static and dynamic models was examined alongside the application of optimisation techniques including genetic algorithms and operational zone identification. These methods were explored with the assistance of case study data from an operational works. It was found that dynamic models were more accurate than static models at predicting the water quality of an operational site but that the root mean square error of the models was within 5% of each other for key performance criteria. Using these models, a range of abstraction rates, for which a water treatment works was predicted to operate sufficiently, were identified, dependent on raw water temperature and total organic carbon concentration. Genetic algorithms were also applied to the water treatment works models to identify near optimal design and operating regimes. Static models were identified as being more suitable for whole works optimisation than dynamic models based on their relative accuracy, simplicity and computational demands.