首页|Study Data from Technical University Provide New Insights into Machine Learning (Advancing Efficiency in Mineral Construction Materials Recycling: A Comprehensive Approach Integrating Machine Learning and X-ray Diffraction Analysis)
Study Data from Technical University Provide New Insights into Machine Learning (Advancing Efficiency in Mineral Construction Materials Recycling: A Comprehensive Approach Integrating Machine Learning and X-ray Diffraction Analysis)
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Investigators publish new report on artificial intelligence. According to news reporting out of Schweinfurt, Germany, by NewsRx editors, research stated, “In the context of environmental protection, the construction industry plays a key role with significant CO2 emissions from mineral-based construction materials.” Funders for this research include Bavarian State Ministry of Environment And Consumer Protection; Center For Basic Materials Efficiency (Rez) At The Bavarian Environment Agency; Publication Fund of The Technical University of Applied Sciences Wuerzburg-schweinfurt. Our news correspondents obtained a quote from the research from Technical University: “Recycling these materials is crucial, but the presence of hazardous substances, i.e., in older building materials, complicates this effort. To be able to legally introduce substances into a circular economy, reliable predictions within minimal possible time are necessary. This work introduces a machine learning approach for detecting trace quantities ( 0.06 wt%) of minerals, exemplified by siderite in calcium carbonate mixtures.” According to the news reporters, the research concluded: “The model, trained on 1680 X-ray powder diffraction datasets, provides dependable and fast predictions, eliminating the need for specialized expertise. While limitations exist in transferability to other mineral traces, the approach offers automation without expertise and a potential for real-world applications with minimal prediction time.”
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