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Advancing Efficiency in Mineral Construction Materials Recycling: A Comprehensive Approach Integrating Machine Learning and X-ray Diffraction Analysis

  • In the context of environmental protection, the construction industry plays a key role with significant CO2 emissions from mineral-based construction materials. 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. 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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Metadaten
Author:Markus Wilhelm, Frank Lotter, Christian Scherdel, Jan Schmitt
URN:urn:nbn:de:bvb:863-opus-56030
DOI:https://doi.org/10.3390/buildings14020341
Parent Title (English):buildings
Publisher:MDPI
Document Type:Article
Language:English
Year of publication:2024
Release Date:2024/04/09
Volume:14
Issue:2
Article Number:340
Pages/Size:14
Institutes and faculty:Fakultäten / Fakultät Wirtschaftsingenieurwesen
Institute / Institut Digital Engineering (IDEE)
Open access colour:Gold
Licence (German):License LogoCreative Commons - CC BY - Namensnennung 4.0 International
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