Hybrid water treatment cost prediction model for raw water intake optimization. (January 2016) - Bertone, Edoardo; Stewart, Rodney A.; Zhang, Hong; O'Halloran, Kelvin
Abstract: In order to reduce the total cost of a dual source drinking water treatment plant operation, a comprehensive hybrid prediction model was built to estimate the necessary chemicals dosage and pumping energy costs for alternative source selection scenarios. Correlations between the water quality parameters and the required treatment chemicals were estimated using historical data and the expected pH variations associated with each chemical addition, which was based on the Caldwell–Lawrence diagram. The pumping energy costs were also estimated using a data-driven approach that was based on historical plant data. The research has practical implications for water treatment operators seeking to minimize plant operational costs through selecting raw water intake volumes for their treatment plant based on multiple source options and offtake tower gate levels. Future research seeks to better link current and future water treatment dosage cost predictions directly to water quality measurements taken from vertical profiling systems. Highlights: Prediction model built to estimate treatment costs for a dual source drinking WTP. Model optimises source selection proportions based on treatment and pumping costs. Model aids WTP plant operators to alter the source selection strategy in extreme events. Model utilisation for source selection optimization delivers life cycle monetary savings.