Hybrid water treatment cost prediction model for raw water intake optimization

Bertone, Edoardo; Stewart, Rodney Anthony; Zhang, Hong; O’Halloran, Kelvin

doi:10.1016/j.envsoft.2015.10.020

Cited by 20 publications

(16 citation statements)

References 18 publications

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“…These ordered, colour contour maps enable the researcher to visually identify similar patterns between some of them; this would mean that similar levels of different variables typically matched over time. That is, the visual inspection of the component planes of the SOM provides a rapid and intuitive means of examining the covariance between the selected variables in order to obtain an increased understanding of the system [26], especially in complex problems with several potential variables involved (e.g., [27]). Based on the conclusions of these first steps, a more in-depth analysis could be performed, and a simple prediction model was developed.…”

Section: Discussionmentioning

confidence: 99%

Analysis and Modelling of Taste and Odour Events in a Shallow Subtropical Reservoir

Bertone

O’Halloran

2016

Environments

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Understanding and predicting Taste and Odour events is as difficult as critical for drinking water treatment plants. Following a number of events in recent years, a comprehensive statistical analysis of data from Lake Tingalpa (Queensland, Australia) was conducted. Historical manual sampling data, as well as data remotely collected by a vertical profiler, were collected; regression analysis and self-organising maps were the used to determine correlations between Taste and Odour compounds and potential input variables. Results showed that the predominant Taste and Odour compound was geosmin. Although one of the main predictors was the occurrence of cyanobacteria blooms, it was noticed that the cyanobacteria species was also critical. Additionally, water temperature, reservoir volume and oxidised nitrogen availability, were key inputs determining the occurrence and magnitude of the geosmin peak events. Based on the results of the statistical analysis, a predictive regression model was developed to provide indications on the potential occurrence, and magnitude, of peaks in geosmin concentration. Additionally, it was found that the blue green algae probe of the lake's vertical profiler has the potential to be used as one of the inputs for an automated geosmin early warning system.

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Section: Discussionmentioning

confidence: 99%

Analysis and Modelling of Taste and Odour Events in a Shallow Subtropical Reservoir

Bertone

O’Halloran

2016

Environments

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“…This was the case with chemicals' dosages, since they are affected by other water quality parameters. Bertone et al (2016c) developed a model to predict chemical dosages based on several water quality input variables, but a whole new model would be required for this specific location before a correlation could be integrated into the SD model. Nevertheless, the authors believe the qualitative incorporation of such parameters provides a better qualitative understanding of feedbacks and time delays present in such a system, and future work could incorporate more quantitative assessments of the impact of such loops.…”

Section: System Dynamics Model Structure and Datamentioning

confidence: 99%

Assessing the impacts of extreme weather events on potable water quality: the value to managers of a highly participatory, integrated modelling approach

et al. 2018

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A decision support tool was created to estimate the treatment efficiency of an Australian drinking water treatment system based on different combinations of extreme weather events and long-term changes. To deal with uncertainties, missing data, and nonlinear behaviours, a Bayesian network (BN) was coupled with a system dynamics (SD) model. The preliminary conceptual structures of these models were developed through stakeholders' consultation. The BN model could rank extreme events, and combinations of them, based on the severity of their impact on health-related water quality. The SD model, in turn, was used to run a long-term estimation of extreme events' impacts by including temporal factors such as increased water demand and customer feedback. The integration of the two models was performed through a combined Monte Carlo–fuzzy logic approach which allowed to take the BN's outputs as inputs for the SD model. The final product is a participatory, multidisciplinary decision support system allowing for robust, sustainable long-term water resources management under uncertain conditions for a specific location.

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“…Pumping costs were also modelled based on the inflow from HUI. The full methodology is presented in Bertone et al (2016b). A Monte Carlo approach was then applied, where 100,000 simulations were run to identify the best blend of raw water sources.…”

Section: Intake Optimisation Model Developmentmentioning

confidence: 99%

Optimising water treatment operations with prediction modelling and smart technologies

2017

Syme, G., Hatton MacDonald, D., Fulton, B. And Piantadosi, J. (Eds) MODSIM2017, 22nd International Congress on Modelling and Si

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Optimising the raw water intake selection is a critical task for multiple source water treatment plants, as this depends on, among others, water quality, pumping costs, and safety considerations. In case of the Mudgeeraba treatment plant in SouthEast Queensland (Australia), the raw water can be withdrawn by gravity from the small Little Nerang dam, or pumped from the larger Hinze dam. Intake towers with different gates also allow for the plant operators to withdraw the raw water from different depths. Often though, decision on the optimal intake location is taken based on operators' experience, without a real scientific method relying on in-depth considerations on e.g. current water quality at multiple locations and electricity costs. Nevertheless, the large amount of data collected daily at the treatment plant, and at higher frequency in the two reservoirs by remote vertical profilers, provides an opportunity to link water quality with treatment costs and provide numerical evidence for the operators to take a more informed decision. As a consequence, a number of data-driven, chemical and mathematical models linking raw water quality with required chemicals dosages, as well as a model estimating the pumping costs, were developed. As a consequence, it was possible to predict the overall daily variable treatment costs based on raw water quality. Given that the water quality is monitored in real time in the two reservoirs for the whole water column, it was also possible to provide advice on a daily basis on which withdrawal depth and reservoir would lead to the lowest treatment cost. By running the model with historical data, it was found that for almost any given day, withdrawing water from Little Nerang dam would have been a cheaper option due to the associated energy costs (i.e. no pumping required) being much lower than potential extra treatment costs due to poorer raw water quality; however, traditionally operators prefer to keep it as a backup reservoir in cases of, for instance, power outages and inability to use the Hinze dam pumps. Given that Little Nerang dam, despite smaller, would be able to provide enough water for few months, an increased use of this water source was still recommended, especially around wet seasons. Based on this, a medium-term storage volume prediction model was developed, able to forecast the risk of depletion and spill for Little Nerang dam for the upcoming six weeks based on, among others, proposed daily withdrawal rates and weather forecasts. In this way, plant operators would be able to plan the future withdrawals in a way to reduce the costs, but also to avoid excessive spill or depletion risks; thus effectively achieving a better planning and management of water treatment operations. Despite the predicted benefits, it is critical to engage with the relevant stakeholders and potential end-users in order to incentivise the deployment of the end products of research projects; often, lack of proper communication and engagement can lead to the majority of the research outcomes to be...

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Hybrid water treatment cost prediction model for raw water intake optimization

Cited by 20 publications

References 18 publications

Analysis and Modelling of Taste and Odour Events in a Shallow Subtropical Reservoir

Analysis and Modelling of Taste and Odour Events in a Shallow Subtropical Reservoir

Assessing the impacts of extreme weather events on potable water quality: the value to managers of a highly participatory, integrated modelling approach

Optimising water treatment operations with prediction modelling and smart technologies

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