Industrial Transformation Research Hubs - Grant ID: IH200100010
Funder
Australian Research Council
Funding Amount
$5,000,000.00
Summary
ARC Research Hub for Transformation of Reclaimed Waste Resources to Engineered Materials and Solutions for a Circular Economy. This project aims to create new knowledge to reduce waste going to landfills and transform reclaimed waste into new materials for use in construction and other manufacturing sectors. It integrates multisector input and multidisciplinary academic research to address ten challenging waste streams. Expected outcomes are smart materials, socio-technical change, accelerated t ....ARC Research Hub for Transformation of Reclaimed Waste Resources to Engineered Materials and Solutions for a Circular Economy. This project aims to create new knowledge to reduce waste going to landfills and transform reclaimed waste into new materials for use in construction and other manufacturing sectors. It integrates multisector input and multidisciplinary academic research to address ten challenging waste streams. Expected outcomes are smart materials, socio-technical change, accelerated testing methods, predictive modeling, circular life cycle costing and a trusted evidence base. Outcomes will lead to commercial benefits as well as jobs and a significant contribution to addressing the pressing environmental impacts of waste production, management, and re-use.Read moreRead less
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