Comprehensive investigation of mine-impacted water treatment using cryo-purification: Bench-scale and pilot-scale stages with the aid of artificial intelligence application
Comprehensive investigation of mine-impacted water treatment using cryo-purification: Bench-scale and pilot-scale stages with the aid of artificial intelligence application
批准号:
567160-2021
负责人:
Ray, AjayKumarAK
金额:
$5.83万
依托单位国家:
加拿大
项目类别:
Alliance Grants
财政年份:
2022
资助国家:
加拿大
项目状态:
已结题
起止时间:
2022-01-01 至 2023-12-31
中文摘要
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英文摘要
The proposal deals with comprehensive investigation and testing of an environmental-friendly, energy-efficient, economically feasible freezing technology (cryo-purification) for mine-impacted water remediation in close collaboration with industry partner, Core Geoscience Services Inc. (Whitehorse, Yukon). The application of cryo-purification, especially in the locations where northern climates' cold temperature conditions can be utilized could be a viable water treatment solution. Northern Canada is of special interest considering that three of the five largest federal contaminated sites - Faro Mine, Giant Mine and Colomac Mine - are located within Yukon and Northwest Territories, where water treatment is required. The application of this technology (which can be effective for the removal of both inorganic and organic contaminants from aqueous solutions) could be particularly beneficial for Yukon First Nations (YFN) communities to address the lack of access to safe, clean household water in a cost-effective and easy-to-use manner. The Faro Mine is a legacy abandoned mine in Yukon, Canada, that is now the responsibility of the Government of Canada to manage and mitigate environmental impacts. Our team's initial study and recent results on laboratory testing and mathematical modeling for the removal of zinc from the mine-impacted water (Faro Mine, Yukon, Canada) using a range of cryo-purification techniques (within the previous 2020-2021 NSERC Alliance and Mitacs Accelerate-supported projects) provide a solid foundation for further successive stages, including bench and pilot-scale testing. The approach to be used combines laboratory testing, data analysis, mathematical modeling within bench- and pilot-scale stages with the aid of artificial intelligence application. This approach, together with close collaboration between the industry partner CoreGeo and Western University maximize the chances of project success and ultimately lead to the commercialization and application of cryo-purification at mine sites, or industrial sites, in the coming years.
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Deep Transfer Learning from Data for Operational Excellence in Refineries
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批准号:556066-2020
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项目类别:Alliance Grants
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资助金额:$1.82万
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财政年份:2022
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负责人:Ray, AjayKumarAK
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依托单位:
海外基金