Household innovation and the transition to the low waste city. Australia is experiencing an urban waste crisis. Long-term solutions require new strategies to reduce waste generation. To be effective, these will need to engage and actively involve households. This project examines the capacity for experimentation and innovation in households necessary to transition to low waste cities. It integrates studies of demographic profiles of household waste generation, household low waste experiments and ....Household innovation and the transition to the low waste city. Australia is experiencing an urban waste crisis. Long-term solutions require new strategies to reduce waste generation. To be effective, these will need to engage and actively involve households. This project examines the capacity for experimentation and innovation in households necessary to transition to low waste cities. It integrates studies of demographic profiles of household waste generation, household low waste experiments and policy rationales and co-design to propose realistic pathways for decreasing waste generation. The research outcomes are critical for understanding and supporting pathways to low waste cities. The knowledge developed will support urban sustainability transitions in Australia and internationally. 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