Data-driven water quality treatment management decision support system. Data-driven water quality treatment management decision support system. This project aims to develop a robust decision support system to predict manganese and the character and concentration of dissolved organic matter in drinking water reservoirs, using intelligent algorithms and data collected through remote autonomous instrumentation. These predicted water quality parameters could be used as model input variables to provi ....Data-driven water quality treatment management decision support system. Data-driven water quality treatment management decision support system. This project aims to develop a robust decision support system to predict manganese and the character and concentration of dissolved organic matter in drinking water reservoirs, using intelligent algorithms and data collected through remote autonomous instrumentation. These predicted water quality parameters could be used as model input variables to provide real-time decisions for plant operators on the required treatment regime for incoming raw water, and advise them on the optimal reservoir offtake depth. This will potentially minimise treatment costs and health risks for consumers. The ultimate goal is to significantly enhance current water supply management practices.Read moreRead less
A novel physical-digital approach for the assessing a large critical asset. This project aims to deliver an artificial intelligence-enabled decision-making tool to maintain and manage the floating covers of vast lagoons that treat raw sewage. The cover harvests the biogas released from the anaerobic digestion of sewage for electric power generation that exceeds the plant’s requirement. The approach involves an innovative thermographic technique and exploits transfer learning to adapt neural netw ....A novel physical-digital approach for the assessing a large critical asset. This project aims to deliver an artificial intelligence-enabled decision-making tool to maintain and manage the floating covers of vast lagoons that treat raw sewage. The cover harvests the biogas released from the anaerobic digestion of sewage for electric power generation that exceeds the plant’s requirement. The approach involves an innovative thermographic technique and exploits transfer learning to adapt neural networks trained on lab-scale and synthetic data to field implementation. The outcome is a machine learning framework to optimise biogas harvesting and renewable energy generation, and to avoid structural failure, that is capable of continuous improvement to take into account improved data and/or modelling capabilities.Read moreRead less
Smart management of disinfectant in chloraminated water-supply systems. Smart management of disinfectant in chloraminated water-supply systems. This project aims to develop an adaptive, real-time control system for managing disinfectant residuals in chloraminated water supply systems. While chloramine delivers microbiologically safe drinking water in warmer climates and in long distribution systems, it is largely unpredictable, costs water utilities millions of dollars annually, and has uncertai ....Smart management of disinfectant in chloraminated water-supply systems. Smart management of disinfectant in chloraminated water-supply systems. This project aims to develop an adaptive, real-time control system for managing disinfectant residuals in chloraminated water supply systems. While chloramine delivers microbiologically safe drinking water in warmer climates and in long distribution systems, it is largely unpredictable, costs water utilities millions of dollars annually, and has uncertain benefits. This project’s control system will be guided by quantitative models formulated from multi-pronged, fundamental experiments. The project will quantify microbial chloramine decay and determine mechanisms to increase predictability. The project will develop and demonstrate a real-time control technology which delivered microbiologically safe, cost-efficient drinking water to people in warmer climates, despite warming climate and increasing population.Read moreRead less
Assessing risk of oligomictic conditions in sub-tropical water supply lakes. Assessing risk of oligomictic conditions in sub-tropical water supply lakes. This project aims to assess the risk of low rates of mixing in sub-tropical drinking water supply reservoirs, using environmental monitoring and numerical modelling. Emerging evidence suggests sub-tropical drinking water supply reservoirs could transition to low mixing states with increasing age and projected changes in global climate. While th ....Assessing risk of oligomictic conditions in sub-tropical water supply lakes. Assessing risk of oligomictic conditions in sub-tropical water supply lakes. This project aims to assess the risk of low rates of mixing in sub-tropical drinking water supply reservoirs, using environmental monitoring and numerical modelling. Emerging evidence suggests sub-tropical drinking water supply reservoirs could transition to low mixing states with increasing age and projected changes in global climate. While this risk is poorly understood, it could significantly affect the long-term reliability of water supply and potable water treatment costs. Addressing this knowledge gap is expected to develop effective management responses to ensure the long term sustainable use of these water resources.Read moreRead less