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Status : Active
Field of Research : Process Control and Simulation
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Chemical Engineering (6)
Process Control and Simulation (6)
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  • Researchers (28)
  • Funded Activities (6)
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  • Active Funded Activity

    Discovery Projects - Grant ID: DP190100983

    Funder
    Australian Research Council
    Funding Amount
    $646,000.00
    Summary
    Next generation gas separations via innovative adsorption technologies. This project aims to develop new gas separation technologies that combine novel materials and pressure swing adsorption cycles to deliver inexpensive industrial processes capable of both high recovery and high purity products. The project will advance our ability to manipulate the phenomenon of regulated guest admission into microporous materials, and integrate such materials within new types of dual-reflux adsorption cycles .... Next generation gas separations via innovative adsorption technologies. This project aims to develop new gas separation technologies that combine novel materials and pressure swing adsorption cycles to deliver inexpensive industrial processes capable of both high recovery and high purity products. The project will advance our ability to manipulate the phenomenon of regulated guest admission into microporous materials, and integrate such materials within new types of dual-reflux adsorption cycles that deliver multiple refined gas products. Successful implementation of these industrial developments will increase Australia's access to cheap supplies of natural gas, encourage the broader use of biomass, lower the carbon emissions of industrial processes, and efficiently recover high-value compounds only present at trace concentrations.
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    Active Funded Activity

    ARC Future Fellowships - Grant ID: FT180100572

    Funder
    Australian Research Council
    Funding Amount
    $998,125.00
    Summary
    Unlocking Australia's offshore gas endowment. This project aims to develop practical new methods of predicting and detecting the formation of solids in gas and liquefied natural gas (LNG) production. Australia has large offshore reserves of natural gas and has made the investments necessary to help fuel the global transition to cleaner, reliable energy sources. However, conventional engineering approaches of producing gas from deep-water reserves have reached the limits of viability because of t .... Unlocking Australia's offshore gas endowment. This project aims to develop practical new methods of predicting and detecting the formation of solids in gas and liquefied natural gas (LNG) production. Australia has large offshore reserves of natural gas and has made the investments necessary to help fuel the global transition to cleaner, reliable energy sources. However, conventional engineering approaches of producing gas from deep-water reserves have reached the limits of viability because of the costs required to prevent solids forming in subsea pipelines or cryogenic LNG plants. The project’s expected outcome include sophisticated tools in open-access software based on these new predictive methods, and a step-change in Australia’s ability to access its offshore gas.
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    Active Funded Activity

    Discovery Projects - Grant ID: DP210101978

    Funder
    Australian Research Council
    Funding Amount
    $449,440.00
    Summary
    A System Behavioral Approach to Big Data-driven Nonlinear Process Control. This project aims to develop a novel process control approach that utilises big process data to improve the cost-effectiveness of industrial processes. Existing monitoring systems in the industry have been collecting a tremendous amount of process operation data but little effort has been made to use the big process data to enhance process operations. Based on the system behavioural approach and dissipativity theory, inte .... A System Behavioral Approach to Big Data-driven Nonlinear Process Control. This project aims to develop a novel process control approach that utilises big process data to improve the cost-effectiveness of industrial processes. Existing monitoring systems in the industry have been collecting a tremendous amount of process operation data but little effort has been made to use the big process data to enhance process operations. Based on the system behavioural approach and dissipativity theory, integrated with machine learning techniques, this project expects to develop a novel framework for data-driven control using big process data. The outcomes are expected to benefit the Australian process industry, where many processes are controlled by inadequate logic controllers, by improving their operational efficiency.
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    Active Funded Activity

    Linkage Projects - Grant ID: LP210200903

    Funder
    Australian Research Council
    Funding Amount
    $785,000.00
    Summary
    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.
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    Active Funded Activity

    Linkage Projects - Grant ID: LP150101241

    Funder
    Australian Research Council
    Funding Amount
    $645,000.00
    Summary
    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.
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    Active Funded Activity

    Discovery Projects - Grant ID: DP220100355

    Funder
    Australian Research Council
    Funding Amount
    $405,000.00
    Summary
    Data-based Control of Process Feature Dynamics through Latent Behaviours. This project aims to develop a novel data-based approach to control the feature dynamics of complex industrial processes. The dynamic features of desired process operations (leading to high energy and material efficiencies and good product quality) are often not directly measured but can be distilled from high-dimensional big process data. However, little effort has been made to develop process control approaches to achiev .... Data-based Control of Process Feature Dynamics through Latent Behaviours. This project aims to develop a novel data-based approach to control the feature dynamics of complex industrial processes. The dynamic features of desired process operations (leading to high energy and material efficiencies and good product quality) are often not directly measured but can be distilled from high-dimensional big process data. However, little effort has been made to develop process control approaches to achieve desired dynamic features. This project aims to develop such a data-based approach by controlling latent variable dynamics, using the behavioural systems framework integrated with big data analytics and artificial neural networks. The outcomes are expected to help build a cornerstone for future smart manufacturing.
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    Showing 1-6 of 6 Funded Activites

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