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Australian State/Territory : QLD
Field of Research : Water resources engineering
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  • Active Funded Activity

    Linkage Projects - Grant ID: LP220200572

    Funder
    Australian Research Council
    Funding Amount
    $489,682.00
    Summary
    Mitigating the negative effects of process water on recovering gold. Low quality water has been used in the minerals industry to save fresh water but shows harmful effects on gold extraction. This project aims to understand the interactions of organic and inorganic components, existing in process water, with gold and determine problematic components that inhibit gold extraction. Expected outcomes will be developed bio-sorbents, based on agriculture waste, that can remove the problematic componen .... Mitigating the negative effects of process water on recovering gold. Low quality water has been used in the minerals industry to save fresh water but shows harmful effects on gold extraction. This project aims to understand the interactions of organic and inorganic components, existing in process water, with gold and determine problematic components that inhibit gold extraction. Expected outcomes will be developed bio-sorbents, based on agriculture waste, that can remove the problematic components in process water efficiently and economically. This will provide major benefits for the minerals industry by providing options to respond and adapt to the impacts of water quality change, leading to increases in yield, revenue and growth of the precious metal sector whilst cutting poisonous chemical consumptions.
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    Active Funded Activity

    Discovery Projects - Grant ID: DP230100153

    Funder
    Australian Research Council
    Funding Amount
    $418,740.00
    Summary
    Intelligent pattern recognition of water end uses enabling recommendations. This project aims to develop a hybrid machine learning method for autonomously disaggregating high- and low-resolution water flow data received from smart meters into discrete end-use events, and a customised recommender system for efficient resource demand management. Project novelty and significance relates to this coupling and autonomous disaggregation of datasets from advanced sensors, enabling more efficient utility .... Intelligent pattern recognition of water end uses enabling recommendations. This project aims to develop a hybrid machine learning method for autonomously disaggregating high- and low-resolution water flow data received from smart meters into discrete end-use events, and a customised recommender system for efficient resource demand management. Project novelty and significance relates to this coupling and autonomous disaggregation of datasets from advanced sensors, enabling more efficient utility services delivery and lower customer utility bills. Project benefits include enabling utilities to better manage and plan resources in the information age, while empowering customers with real-time water end-use data and behaviour changing consumption recommendations.
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