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
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