Optimising dissolved air flotation (DAF) for algae removal by bubble modification in drinking water and advanced wastewater systems. Algal blooms in potable water reservoirs and advanced wastewater treatment lagoons can impact the performance and economic viability of water treatment processes resulting in taste and odour episodes and the risk of algal toxins as well as causing further limitation to already stressed water resources in Australia. This project aims to develop an adaptation of the ....Optimising dissolved air flotation (DAF) for algae removal by bubble modification in drinking water and advanced wastewater systems. Algal blooms in potable water reservoirs and advanced wastewater treatment lagoons can impact the performance and economic viability of water treatment processes resulting in taste and odour episodes and the risk of algal toxins as well as causing further limitation to already stressed water resources in Australia. This project aims to develop an adaptation of the dissolved air flotation process that is already used for algae treatment that will provide a more robust, economic and sustainable barrier to algal cells in accordance with the Australian Drinking and Recycled Water Guidelines.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