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
Optimal trade-offs for managing environmental water in inland wetlands. This project aims to optimise long-term water trade-offs in inland wetlands on managed catchments, without compromising their environmental value. These managed wetlands compete for water allocations with irrigation and other uses. Realistic predictions of wetland status will be achieved through the development and integration of an ecohydrological model and a water management decisions model. Application of the tools will i ....Optimal trade-offs for managing environmental water in inland wetlands. This project aims to optimise long-term water trade-offs in inland wetlands on managed catchments, without compromising their environmental value. These managed wetlands compete for water allocations with irrigation and other uses. Realistic predictions of wetland status will be achieved through the development and integration of an ecohydrological model and a water management decisions model. Application of the tools will improve existing decision support models to help analyse the effects of individual local management decisions on the long-term evolution of the system and the effects of changes in operation policies and climate over the long term. The project will provide critical new information for the improved prediction of wetlands evolution and as a consequence better management.Read moreRead less