Integrated Compositional Modeling and Machine Learning Analysis of REE-Bearing Coal Ash from a Weathered Dumpsite

Coal combustion residues are increasingly viewed as alternative sources of rare earth elements (REEs), but their heterogeneous composition and post-depositional alteration complicate resource evaluation. This study analyzes 50 coal ash (CA) samples collected from a weathered dumpsite near Almaty, Kazakhstan, originating from power generation using coal from the Ekibastuz Basin. A multi-method approach—comprising bulk chemical characterization, unsupervised clustering, X-ray diffraction (XRD), scanning electron microscopy (SEM), and supervised machine learning (ML)—was applied to identify consistent indicators of REE enrichment. While conventional regression models failed to predict individual REE concentrations accurately, ML algorithms consistently highlighted vanadium (V) as the most robust predictor of ΣREE across Random Forest, XGBoost, and LASSO. This suggests that V may act as a geochemical proxy for REE-bearing phases, potentially due to co-retention in amorphous or ferruginous matrices. Despite compositional similarity among many samples, XRD and SEM revealed marked variability in phase structure and crystallinity, underscoring the limitations of bulk oxide data alone. These findings demonstrate that REE behavior in ash cannot be predicted deterministically, but ML can be used to screen for informative compositional signals. The proposed workflow may support the preliminary classification and valorization of heterogeneous ash materials in secondary resource strategies.