Discovery Early Career Researcher Award - Grant ID: DE180100266
Funder
Australian Research Council
Funding Amount
$367,446.00
Summary
Granular interfaces for sustainable processing of raw materials. This project aims to develop an innovative interface model and a comprehensive understanding of the interfacial behaviours between granular materials using advanced numerical, experimental and theoretical approaches. This project expects to generate new knowledge of mixing and segregation in particle science and technology and a practical guide to applications. Expected outcomes of this project include the enhanced competitiveness ....Granular interfaces for sustainable processing of raw materials. This project aims to develop an innovative interface model and a comprehensive understanding of the interfacial behaviours between granular materials using advanced numerical, experimental and theoretical approaches. This project expects to generate new knowledge of mixing and segregation in particle science and technology and a practical guide to applications. Expected outcomes of this project include the enhanced competitiveness of Australia and energy efficiency in its important industries such as minerals, metallurgical, chemical, energy and pharmaceutical. These outcomes should provide significant benefits such as mitigated emissions and global warming in a carbon and resource constrained world.Read moreRead less
Data-driven modelling of complex reactive flows. Complex reactive flow is dominant in many chemicals, physical and biological processes and should be optimised online for operational efficiency and stability, yet it is hindered by the lack of reliable model techniques. The project tackles this challenge by developing a next-generation data-driven modelling approach via integrating continuum/discrete-scale fluid-particle dynamics with system/control theories, supported by lab/plant experiments. D ....Data-driven modelling of complex reactive flows. Complex reactive flow is dominant in many chemicals, physical and biological processes and should be optimised online for operational efficiency and stability, yet it is hindered by the lack of reliable model techniques. The project tackles this challenge by developing a next-generation data-driven modelling approach via integrating continuum/discrete-scale fluid-particle dynamics with system/control theories, supported by lab/plant experiments. Driven by online data, the generic approach can open up a powerful way to reliably describe the inner state of reactors and online predict operation anomalies. The outcomes can help transform a range of industries to smart manufacturing and design, which is vital to Australia's technological future.Read moreRead less