Search

The Mudgeeraba drinking water treatment plant, in Southeast Queensland, Australia, can withdraw raw water from two different reservoirs: the smaller Little Nerang dam (LND) by gravity, and the larger Advancetown Lake, through the use of pumps. Selecting the optimal intake is based on water quality and operators' experience; however, there is potential to optimise this process. In this study, a comprehensive hybrid (data-driven, chemical, and mathematical) intake optimisation model was developed, which firstly predicts the chemicals dosages, and then the total (chemicals and pumping) costs based on the water quality at different depths of the two reservoirs, thus identifying the cheapest option. A second data-driven, probabilistic model then forecasts the volume of the smaller LND 6 weeks ahead in order to minimise the depletion and spill risks. This is important in case the first model identifies this reservoir as the optimal intake solution, but this could lead in the long term to depletion and full reliance on the electricity-dependent Advancetown Lake. Both models were validated and proved to be accurate, and with the potential for substantial monetary savings for the water utility.

A reliable simulation of the manganese cycle in water supply system is essential for timely and effective manganese treatment strategies. A data-driven model estimating manganese levels in the treated water was developed through a threshold-based approach using raw water and supernatant return manganese levels. This data-driven model and a three-dimensional manganese cycle model for the source reservoir were integrated to simulate the overall manganese variations from the lake to the treated water. A decision support system based on the developed data-driven model was established to allow operators to react to situations that could result in elevated levels of manganese in the treated water. Scenario analyses were completed to examine the manganese variations in the reservoir, raw water and treated water due to different precipitations and wind conditions and to allow the identification of optimal strategies for manganese management from the reservoir to the water supply system.

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. 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.

Periods of urban water scarcity in cities places pressure on government agencies and water utilities to improve water distribution system efficiency through reducing the amount of water lost in the network as well as deploying a range of demand management techniques to conserve existing supply. Current estimations assume customer post meter leakage accounts for up to 10% of total water consumption, particularly in the residential sector. Households identified as having post meter leakage using a citywide installation of smart metering technology were subjected to a mix of basic and tailored information regarding water loss. The primary aim of the study was to test the effectiveness of communication interventions and the attributed water savings resulting from the repair of household leaks. The residential leakage communication strategy resulted in a reduction in minimum night flows by a very significant 89% over the duration of the study, while the control group receiving no communication increased consumption by 52%. Moreover, questionnaire surveys were conducted to establish the significant factors, including leak type, demographics and household awareness, to name a few, that will influence the development of a fit-for-purpose post-meter leak rectification policy and program. The paper concludes with some key recommendations for future work to develop a predictive model for identifying, classifying and quantifying post meter loss as well as presents least cost planning implications of a leak rectification policy. The study confirmed that smart metering provides water utilities with a powerful tool to rapidly identify and action the significant volumes of post meter leakage occurring in our cities.

Water utilities undertake long term planning for water source security, often with forecast cycles of 20–30 years. Whilst this planning is generally involved with investigations of source water abstraction security and the need to build dams or to increase the recharge rate of groundwater aquifers, planning for water efficiency gains occurs at annual intervals. Most water utilities in Australia are heavily engaged in water efficiency initiatives with rebate schemes for domestic water-efficient devices being ubiquitous across the industry. Wide Bay Water Corporation (WBWC) also engages in these activities but is increasingly interested in the concept of Time of Use Tariffs (TOUTs) to target high water users in order to reduce their demand on the system. In 2006, WBWC introduced smart metering technology across the city which captures hourly use data. Interrogation of this data has led to the ability to identify water use patterns for every domestic and commercial water customer and to design specific interventions to encourage water efficiency, such as a TOUT for domestic customers. A TOUT has been developed that imposes a penalty on all individual consumption greater than 600 L in any hourly interval of any day in the year. The tariff was designed to reduce both the annual peak hour and peak day demand. The ability to reduce both of these infrastructure design parameters may have the potential to deliver substantial savings in infrastructure planning and deployment. This paper details the design process of this tariff, examines the infrastructure savings potential derived by network modelling and explores the regulatory framework hurdles to be overcome in order to implement such tariffs in the water industry.