Data-driven Traffic Analytics for Incident Analysis and Management. Traffic incidents are among the primary concerns of all transport authorities around the world due to their significant impact in terms of traffic congestion and delay, air and noise pollution, and management cost. This project aims to address incident analysis and management in complex and multi-modal traffic networks by combining multidisciplinary research efforts from transportation engineering and data science. The intended ....Data-driven Traffic Analytics for Incident Analysis and Management. Traffic incidents are among the primary concerns of all transport authorities around the world due to their significant impact in terms of traffic congestion and delay, air and noise pollution, and management cost. This project aims to address incident analysis and management in complex and multi-modal traffic networks by combining multidisciplinary research efforts from transportation engineering and data science. The intended outcomes will be an innovative incident analysis and management framework synergising traffic data analytics and traffic simulation modelling as well as its key enabling techniques and prototype systems. This will significantly help mitigate incident impacts on daily commuters.Read moreRead less
Robust, valid and interpretable deep learning for quantitative imaging. One of the biggest challenges in employing artificial intelligence is the “black-box” nature of the models used. This project aims to improve the effectiveness and trustworthiness of deep learning within quantitative magnetic resonance imaging. Deep learning has great promise in speeding-up complex image processing tasks, but currently suffers from variable data inputs, predictions are not guaranteed to be plausible and it i ....Robust, valid and interpretable deep learning for quantitative imaging. One of the biggest challenges in employing artificial intelligence is the “black-box” nature of the models used. This project aims to improve the effectiveness and trustworthiness of deep learning within quantitative magnetic resonance imaging. Deep learning has great promise in speeding-up complex image processing tasks, but currently suffers from variable data inputs, predictions are not guaranteed to be plausible and it is not clear to the end user how reliable the results are. The outcomes intend to deliver advanced knowledge and capability in artificial intelligence and machine learning that Australia urgently needs to capitalise on bringing deep learning into practical applications delivering economic, commercial and social impact.Read moreRead less
Model studies of Australian lump ore applied to blast furnace ironmaking. Ore lump use in ironmaking blast furnaces (BFs) requires no preprocessing and has a lower carbon footprint. However, it suffers various technical problems. This project aims to understand and optimize the conditions for such operations. This will be achieved by means of a combined theoretical and experimental program, involving the use of state-of-the-art multiscale computer modelling and simulation techniques. The researc ....Model studies of Australian lump ore applied to blast furnace ironmaking. Ore lump use in ironmaking blast furnaces (BFs) requires no preprocessing and has a lower carbon footprint. However, it suffers various technical problems. This project aims to understand and optimize the conditions for such operations. This will be achieved by means of a combined theoretical and experimental program, involving the use of state-of-the-art multiscale computer modelling and simulation techniques. The research outcomes will be tested in the design and control of lump charging operations in practice through collaboration with the industrial partner. This will ultimately increase Australian ore lump usage in BFs, leading to significant financial and environmental benefits to Australia and the entire steel industry worldwide.Read moreRead less
Reducing direct greenhouse gas emissions from urban wastewater systems. This project aims to develop a systematic framework for water utilities to monitor and reduce direct greenhouse gas (GHG) emissions from wastewater systems. A standardised monitoring protocol will be developed to conduct an unprecedented nationwide sampling campaign. The obtained data, with microbial characterisation and mechanism analysis, will be used to develop novel models for accurate prediction of GHG emissions. Expect ....Reducing direct greenhouse gas emissions from urban wastewater systems. This project aims to develop a systematic framework for water utilities to monitor and reduce direct greenhouse gas (GHG) emissions from wastewater systems. A standardised monitoring protocol will be developed to conduct an unprecedented nationwide sampling campaign. The obtained data, with microbial characterisation and mechanism analysis, will be used to develop novel models for accurate prediction of GHG emissions. Expected outcomes include protocol to accurately monitor emissions, models to predict emission under various conditions, and mitigation guideline for typical plant configurations. The anticipated benefit is a significant reduction in GHG emissions from urban water industry and support it to meet net-zero-emission goal by 2050.Read moreRead less
CropVision: A next-generation system for predicting crop production. Accurate and timely production estimates are essential to Australia’s grain producers and industry to better deal with down side risk caused by climate extremes and market volatilities. However, current systems for predicting crop production are inaccurate and unreliable. This project aims to develop a next generation system for advance and high accuracy predictions for yield, crop type and area at field scale. This will be don ....CropVision: A next-generation system for predicting crop production. Accurate and timely production estimates are essential to Australia’s grain producers and industry to better deal with down side risk caused by climate extremes and market volatilities. However, current systems for predicting crop production are inaccurate and unreliable. This project aims to develop a next generation system for advance and high accuracy predictions for yield, crop type and area at field scale. This will be done by integrating the state of the art global climate models (GCM), biophysical crop modelling, and high-resolution earth observation technologies. This project will deliver a next generation crop prediction system to predict crop production at field scale for improved decision-making and enhancing resilience.Read moreRead less
Dynamic Earth Models for Frontier Mineral Exploration. This Project aims to investigate the link between supercontinents, mantle upwelling, and associated mineral resources by combining reconstructions of mantle flow with the global rock record. Mantle upwelling causes eruptions of volcanic provinces and associated rock formations that are rich in minerals. The expected outcomes of the Project include mapping the global potential for magmatic nickel, rare-earth elements, and diamond deposits fro ....Dynamic Earth Models for Frontier Mineral Exploration. This Project aims to investigate the link between supercontinents, mantle upwelling, and associated mineral resources by combining reconstructions of mantle flow with the global rock record. Mantle upwelling causes eruptions of volcanic provinces and associated rock formations that are rich in minerals. The expected outcomes of the Project include mapping the global potential for magmatic nickel, rare-earth elements, and diamond deposits from 1.8 billion years ago and building a research alliance between the University of Wollongong, Anglo American, and De Beers. Significant benefits will be the development of a digital framework to reduce risks in exploration for minerals that are essential for the transition to a low-carbon economy.Read moreRead less
The sensory prerequisites of effective simulator-based pilot training. This Project aims to investigate the use of head-mounted virtual reality systems for training, with specific focus on the aviation industry. The Project expects to improve our understanding of how pilots combine information from their sensory systems in order to successfully operate an aircraft. Expected outcomes include methods for specifying the optimal design of simulators intended to prepare pilots for a specific task, wi ....The sensory prerequisites of effective simulator-based pilot training. This Project aims to investigate the use of head-mounted virtual reality systems for training, with specific focus on the aviation industry. The Project expects to improve our understanding of how pilots combine information from their sensory systems in order to successfully operate an aircraft. Expected outcomes include methods for specifying the optimal design of simulators intended to prepare pilots for a specific task, with the ultimate goal of developing and validating a prototype training device. The outcomes are expected to benefit many areas of pilot training by improving the design and optimising the cost of simulator technologies at a time when the aviation industry is struggling to meet the global demand for new pilots.Read moreRead less
Commercial scale production of biocrude by hydrothermal liquefaction. The project aims to develop new understanding and tools to support commercial-scale production of biocrude from microalgae or biosolids and enable a breakthrough in cost-effective production of sustainable fuels. A novel hydrothermal liquefaction reactor has been developed that has strong potential to overcome the limitations of Muradel's existing demonstration reactor which, while world-leading, is uneconomical at commercial ....Commercial scale production of biocrude by hydrothermal liquefaction. The project aims to develop new understanding and tools to support commercial-scale production of biocrude from microalgae or biosolids and enable a breakthrough in cost-effective production of sustainable fuels. A novel hydrothermal liquefaction reactor has been developed that has strong potential to overcome the limitations of Muradel's existing demonstration reactor which, while world-leading, is uneconomical at commercial scale. The project aims to develop design tools to optimise the new reactor, comprising a chemical model of the complex, multi-component hydrothermal liquefaction reactions, a computational model of the mixing and heat transfer within it and a network model of the energy and exergy flows.Read moreRead less