Synthetic Multivariate µ-EDXRF Elemental Domain Mapping for Advanced Characterization of Secondary Raw Materials

Secondary raw materials (SRM) such as phosphogypsum, pyritic mining wastes, and metallurgical slags constitute increasingly important alternative sources of critical raw materials within circular economy strategies. However, these materials commonly exhibit strong compositional heterogeneity at micro- to millimeter scales, making their characterization challenging using conventional bulk analytical approaches. This study presents a multivariate n-dimensional synthetic µ-XRF fluorescence mapping workflow for the automated characterization and classification of heterogeneous secondary resources. High-resolution µ-EDXRF elemental maps were integrated into multidimensional feature spaces combining elemental intensities, spatial relationships, and statistical descriptors. Unsupervised machine learning approaches, including hierarchical clustering, K-means and Gaussian mixture models (GMM), were applied to identify compositional domains and reconstruct synthetic fluorescence maps representing statistically coherent elemental associations. Case studies involving phosphogypsum and slag resulting from pyrite roasting demonstrate the capability of the proposed workflow to distinguish complex mineralogical textures, identify elemental associations related to critical raw materials, and detect environmentally relevant compositional domains. The developed methodology provides a non-destructive and transferable computational framework for advanced secondary resource characterization and process-oriented evaluation of complex waste-derived materials.